Dr Michael C.P. Sing FRICS MHKIS MAIBS MAIPMChartered building surveyor and academicFellow of the Royal Institution of Chartered Surveyors; Member of the Hong Kong Institute of Surveyors, the Australian Institute of Building Surveyors and the Australian Institute of Project Management

Making infrastructure predictable.

I turn records from hundreds of completed public projects into AI forecasts of what infrastructure will cost, the workforce it will need and how ageing buildings will hold up.

In use in Hong Kong

  • Cost contingency models in public-works estimating
  • AI cash-flow forecasts in the surveillance of public projects
  • Workforce models behind the construction industry’s manpower forecasts
Start a conversation Download CV (PDF)

From surveying to academia

Based in Hong Kong, with long-standing ties to Australia. Always open to a conversation.

3D model of a small city: an elevated highway for project delivery and performance, a construction site with a crane for infrastructure workforce planning and resilience, ageing housing blocks for ageing building stock management and prediction, and a park for urban resilience, under a floating network of AI and probabilistic models.

One method, four problems cities care about

AI and probabilistic models, built on government and industry data, applied where forecasting is hardest. Drag the model to rotate it, or pick an area below.

  • Funded by the Hong Kong Research Grants Council, the Hong Kong Construction Industry Council, the HKSAR Government and the Australian Research Council
  • The Hong Kong Polytechnic University2023–nowAssistant Professor and Programme Leader, BSc (Hons) Surveying
  • University of Canberra, Australia2026–2029Adjunct Professor
  • University of Newcastle, Australia2019–2023Associate Professor and Program Convenor (Program Director), Master of Project Management and Graduate Diploma in Project Management
  • Curtin University, Australia2009–2014PhD, Senior Lecturer and Acting Course Coordinator
  • Royal Institution of Chartered Surveyors2013–nowMember from 2013, Fellow (FRICS) from 2023; Chairperson, Assessment of Professional Competence (APC) Panel

The record at a glance

Research and teaching

Refereed journal articles
98
Citations, with an h‑index of 35a
4,899
Of my journal papers in Scopus Q1 journals, the top 25% of their fieldb
90%
Research and consultancy funding across my careerc
HK$22.6M
PhD candidates supervised to completion, three current
6
Student rating in classes of 100+, against a Faculty top‑10% mark of 4.6
4.7/5

Practice and professional standing

Years of surveying experience in the Hong Kong Government, before academia
8
Fellow of the Royal Institution of Chartered Surveyors, and a Chartered Building Surveyor
FRICS
Professional competence assessments chaired for the Royal Institution of Chartered Surveyors, in Hong Kong and Australia
20+
Commissioned research and consultancy for government, industry and professional bodiesc
HK$9.2M

a. Google Scholar, 10 September 2026 (SciVal: 2,967 citations, h-index 29). b. 88 of 98 journal papers, by the Scopus CiteScore percentile of the journal; 64 (65%) are in the top 10% and 48 in the top 5%. c. About US$2.9M at the HK$7.8 peg: HK$9.7M in external competitive grants (including an Australian Research Council Discovery Project), HK$9.2M in commissioned research and consultancy, and HK$3.8M in internal grants.

Highlights

Research, impact and teaching

A short summary of my work in each area, followed by the projects under way now. The details are further down the page and in my CV.

Research and funding

HK$22.6Macross 28 funded projects

  • 8external competitive grants, HK$9.7M
  • 10commissions from government, industry and professional bodies, HK$9.2M
  • 10internal grants, HK$3.8M

External competitive grants I have secured

  • HKGRFGeneral Research Fund, Hong Kong Research Grants CouncilPrincipal investigator
  • ARCAustralian Research Council Discovery ProjectChief Investigator
  • PPRPublic Policy Research Fund, HKSAR GovernmentPrincipal investigator
  • CICHong Kong Construction Industry CouncilPrincipal investigator

Impact on practice

10commissions for government, industry and professional bodies, 4 as lead consultant

  • Development Bureau: risk-based contingency models adopted into its estimating practice, and an AI cash-flow model in its Project Surveillance System
  • Construction Industry Council: manpower projections for workforce planning, and contractor-selection reference materials
  • Hong Kong Institute of Surveyors: the evidence behind its guidance on maintenance project management

HK$9.2M of commissioned research and consultancy since 2009, separate from research grants.

Teaching and leadership

300+students in the BSc (Hons) in Surveying I lead

  • Rated 4.7 out of 5 in classes of more than 100 students
  • Degrees taken through accreditation by the Royal Institution of Chartered Surveyors, the Australian Institute of Building and the Australian Institute of Project Management
  • An AI-tutored flipped classroom with 22,000+ student interactions

Letters of appreciation from a Pro Vice-Chancellor and a head of department.

Current projects

Six external competitive grants are current or about to start, worth HK$6.0M in total. I lead three of them, worth HK$4.0M.

AILead investigator

An AI forecasting platform for construction workforce planning

Extends the manpower forecasting framework I built with the Construction Industry Council into an AI platform, developed and validated on a newly curated dataset of more than 1,500 projects.

Hong Kong Construction Industry Council Research and Technology Development FundFrom December 2026, 18 monthsHK$2.37M
AILead investigator

A data-driven prediction model for ageing residential building stock

Combines a dynamic Bayesian network, building inspection reports and AI image recognition of defects to assess and predict the condition of ageing housing, so that inspection goes first where the risk is highest.

Hong Kong Research Grants Council General Research FundFrom January 2026, 36 monthsHK$1.11M
Lead investigatorPublic policy

Inclusive outdoor facilities for ethnic minorities

Field observation, practitioner interviews and spatial analysis in the district with Hong Kong’s highest share of ethnic-minority residents, leading to practical recommendations for public facilities.

Public Policy Research Fund, HKSAR GovernmentFrom November 2025, 12 monthsHK$0.51M

As co-investigator

  • Automated benchmarking of megaproject performanceHK$1.17MHong Kong Research Grants Council General Research Fund, from 2025, 36 months
  • A cloud-based fuzzy assessment framework for smart age-friendly housing for middle-class elderlyHK$0.57MHong Kong Research Grants Council, Competitive Research Funding Schemes for the Local Self-financing Degree Sector, from 2025, 24 months
  • Innovating property and facility management: harnessing technology to tackle manpower challengesHK$0.27MHong Kong Research Grants Council, Competitive Research Funding Schemes for the Local Self-financing Degree Sector, from 2026, 12 months

Also under way: an agentic AI pipeline for building-defect intelligence (lead investigator, internal grant), and a strategic framework for climate-resilient infrastructure governance in Hong Kong and the Greater Bay Area (co-investigator, Otto Poon Charitable Foundation).

Current project, project details

Empowering construction workforce resilience in Hong Kong: an AI-driven forecasting platform for workforce planning

Funder
Hong Kong Construction Industry Council, Research and Technology Development Fund (Explorative Project Scheme)
Role
Principal investigator, with co-investigators Albert P.C. Chan and M.W. Chan
Period
From December 2026, 18 months
Based at
Centre for Infrastructure Delivery (CeIDE)The Hong Kong Polytechnic University

Abstract

Hong Kong expects more than HK$120 billion a year in capital works over the next decade, yet the supply of skilled trades still does not match demand on site: some trades stand idle while others run short. Workforce planning still relies on static labour multipliers and broad trade groups, and site labour records are rarely used. This project combines artificial intelligence, which learns deployment patterns from site labour, training and project records, with system dynamics, which shows how training, wages and policy feed back on supply over time. The result is a web-based dashboard that gives monthly, trade-by-trade forecasts, warns of shortages or surpluses ahead, and lets government, contractors, training bodies and workers test what-if scenarios before they act.

The problems it tackles

  • Mismatch by tradeSome trades stand idle while others run short, which delays projects and raises costs.
  • Static forecastsLabour multipliers and broad trade groups miss how demand and supply respond to change.
  • Little visibilityTraining bodies and workers cannot see shortages or job openings coming.

How the platform works

  1. Step 1
    Learn from the records

    Site labour deployment and attendance records from public and private sites, training enrolments and completions, and a database of more than 1,500 completed and ongoing projects.

  2. Step 2
    Forecast with machine learning

    An ensemble of models forecasts monthly demand and supply for each trade, scored for accuracy against past data.

    XGBoostLightGBMSupport vector machinesNeural networksLSTMTemporal Fusion Transformer

  3. Step 3
    Simulate the feedback

    A system dynamics model adds what static forecasts miss: training delays, attrition, wages, project timing and the effect of modular integrated construction.

  4. Step 4
    Plan on the dashboard

    A web dashboard shows shortages and surpluses ahead, the factors behind each forecast, and the result of what-if scenarios, for government, contractors, training bodies, workers and unions.

New data retrains the models regularly, so the forecasts improve as the platform is used.

The feedback the model captures

+ + + − + − + + + − delay Construction cost Wages Recruitment Project pipeline Labour demand Labour gap Labour supply Training intake Retirement B1 B2
  • B1A shortage lifts wages, which draws in recruits and closes the gap.
  • B2Higher wages also raise costs, which can slow the pipeline and cut demand.
  • ||Training adds workers only after a delay; retirement drains supply.
Illustrative causal loop diagram, simplified from the project’s conceptual model.

What a planner sees

DemandSupply if nothing changesSupply with more training

More training starts Shortage ahead Now Year 1 Year 2 Year 3 Workers in one trade
Illustrative, not to scale. The dashboard flags the shortage early and tests whether a training policy closes it in time.

Eighteen months, three phases

Understand the systemInterviews with 25 to 30 contractors, unions and training bodies; causal-loop maps checked by an expert focus group
Months 1–5
Data and machine learningAssemble the project, labour and training data; train and test the forecasting models
Months 2–12
Integrate, validate and launchJoin AI forecasts to the system dynamics model; expert workshops and back-testing; build the dashboard
Months 11–18

What the project will deliver

  • An open-access web dashboard for government bodies and industry, with trade-by-trade forecasts and scenario tools.
  • Site trials on five active construction sites with main contractors, in the second half of the project.
  • Two journal papers and an international conference presentation.
  • Stakeholder workshops and a closing seminar with a live demonstration for policymakers, contractors, training providers and unions.

Current project, project details

Development of a data-driven prediction model for the condition assessment of ageing residential building stock

Funder
Hong Kong Research Grants Council, General Research Fund
Role
Principal investigator, with co-investigators Joseph H.K. Lai, Albert P.C. Chan and M.W. Chan
Period
From January 2026, 36 months
Based at
Department of Building and Real Estate, The Hong Kong Polytechnic University

Abstract

By the end of 2024, more than 28,000 private residential buildings in Hong Kong had reached 30 years or older, about 64% of the stock. Many have had decades of too little maintenance, and owners often cannot afford inspection and repair. At the current pace of mandatory inspection, one full cycle of the ageing stock would take more than 20 years. This project develops a data-driven prediction model for the condition of ageing residential buildings. A dynamic Bayesian network learns how defects in walls, roofs, drainage and building services lead to one another over time, drawing on real building inspection reports and expert knowledge. A convolutional neural network reads panoramic photographs to detect defects automatically and keeps the model up to date. Owners, surveyors and the building authority can then predict deterioration, rank buildings by urgency and test maintenance strategies before they adopt them.

The problems it tackles

  • A fast-ageing stockAbout 64% of private residential buildings are 30 years or older, and falling concrete and rendering have raised public concern.
  • Inspection cannot keep upAt today’s pace, one inspection cycle of the ageing stock would take more than 20 years.
  • Piecemeal assessmentMost methods rate single components by eye and miss how one defect leads to another.

How the model is built: five stages over 36 months

  1. Stage 1
    Organise the building

    A taxonomy of components, from external walls and roofs to drainage and building services. Key defects and causes are text-mined from at least 500 inspection reports and checked by 10 to 15 experts.

  2. Stage 2
    Map cause and effect

    Structural equation modelling of at least 200 survey responses, then a Delphi panel of 15 or more experts, sets out how defects lead to one another.

  3. Stage 3
    Predict over time

    A dynamic Bayesian network splits deterioration into stages, such as cracks that lead to spalling and façade failure, and rates overall condition from very good to very bad.

    Dynamic Bayesian networkText mining

  4. Stage 4
    See defects automatically

    A convolutional neural network reads panoramic photographs of 200 buildings to detect defects such as debonded tiles and corroded pipes, and feeds them into the network.

    Convolutional neural networkImage recognition

  5. Stage 5
    Validate on real buildings

    Sensitivity tests, then ten recently inspected buildings compared with their official inspection reports, including the time and cost saved against manual inspection.

New inspection reports and photographs keep updating the network, so its predictions improve over time.

How one defect leads to another

Todayinspected In 5 yearspredicted In 10 yearspredicted Water seepage Concrete cracks Spalling Fair Water seepage Concrete cracks Spalling Poor Water seepage Concrete cracks Spalling Very poor
  • Today: observed from inspection reports and photographs, with defects detected by the image-recognition model.
  • Later periods: predicted by the dynamic Bayesian network, which also traces problems back to their causes.
  • Bottom row: overall condition on a five-step scale, from very good to very bad.
Illustrative, simplified from the project’s model. A repair at any stage changes the forecast.

What a building authority sees

Very goodGoodFairPoorVery bad

1 2 3

Across this district

Illustrative district of 24 buildings coloured by predicted condition. The numbered buildings are flagged for inspection first, so limited inspection capacity goes where the risk is highest.

Pathway to impact

  1. 1 to 3 yearsFind problems earlyFlag urgent defects, rank buildings for maintenance, and prepare for external-wall risks.
  2. 3 to 5 yearsPlan and budgetForecast how the stock will deteriorate under current policy, and allocate maintenance budgets precisely.
  3. 5 to 10 yearsTarget renewalFocus rehabilitation on the groups of buildings that need it most, and inform redevelopment and urban resilience planning.

What the project will deliver

  • A validated prediction model that assesses and forecasts building condition and updates itself with new inspection and image data.
  • An image-recognition tool that detects defects in panoramic photographs of building exteriors.
  • Open knowledge transfer: findings and algorithms on a public website, and technology transfer through PolyU’s Institute for Entrepreneurship.
  • Journal papers, international conferences and professional seminars with facility management and building surveying institutions.

Current project, project details

Enhancing inclusivity in Hong Kong’s outdoor facilities and venues: a study on ethnic minorities’ access and preferences

Funder
Public Policy Research Funding Scheme, HKSAR Government
Role
Principal investigator, with co-investigators Esther H.K. Yung, Nan Cao and Bryant P.H. Hui
Period
From November 2025, 12 months
Study area
Yau Tsim Mong, the district with Hong Kong’s highest share of ethnic-minority residents

Abstract

In a city of small homes, outdoor facilities and venues, from parks and sports grounds to plazas, are where community life happens. Ethnic minorities are a key part of Hong Kong’s society, yet design barriers and a lack of cultural sensitivity keep many from using these spaces. Recent Policy Addresses and the Hong Kong 2030+ strategy call for inclusive, universal design, but planning guidelines say little about ethnic minorities’ needs. This study combines GIS analysis of where facilities are and how easy they are to reach, questionnaires and field observation with ethnic-minority users, and immersive virtual-reality experiments that measure how people respond to culturally relevant designs. The results rank what matters most to ethnic-minority users and turn it into practical recommendations for inclusive planning and design.

The problems it tackles

  • Space is scarceWith small homes, public outdoor spaces carry much of daily life, from rest and play to meeting friends.
  • Guidelines are generalPlanning standards address older people and people with disabilities, but rarely ethnic minorities’ needs.
  • Barriers beyond provisionLanguage, booking systems and culturally unresponsive design keep people away even where facilities exist.

How the study works: five stages over 12 months

  1. Stage 1
    Frame the questions

    Focus groups with 10 to 15 experts from NGOs and ethnic-minority support services, then a questionnaire for about 270 ethnic-minority residents.

  2. Stage 2
    Map the facilities

    GIS analysis of where facilities are and how easy they are to reach, from land use and street connectivity to homes and transport, plus field observation.

  3. Stage 3
    Test designs in virtual reality

    In an immersive CAVE (Cave Automatic Virtual Environment), participants experience culturally relevant and neutral virtual venues; picture-based scales measure their response without language barriers.

    Immersive VRSelf-Assessment ManikinPerceived Restorativeness Scale

  4. Stage 4
    Model preferences

    An ordered logit model links background, facility attributes and psychological response to show what matters most.

  5. Stage 5
    Validate and recommend

    Focus groups with the groups concerned and a stakeholder workshop test the findings before they become policy recommendations.

Five things that shape use

Ethnic-minorityuse of outdoorfacilities Culturalintegration Accessibility Maintenanceand operation Socialenvironment Individualpreferences
The study’s framework of five dimensions, refined from expert focus groups, fieldwork and analysis.

Emerging findings

22
practitioners from nine organisations interviewed
30
outdoor facilities and venues observed in the field
4,061
interview and survey segments coded

Physical provision alone is not enough. Use depends on language, booking systems, culturally responsive design and awareness, and NGO-led programmes do most to bring people in.

Recommendations taking shape

  • Multilingual booking systems and signage
  • Walk-in access, to reduce reliance on booking
  • Structured partnerships between NGOs and government for community programmes
  • Facility zones that respond to the needs of women and girls

Twelve months, five stages

Frame the questionsExpert focus groups, questionnaire design and survey
Months 1–51–5
Map the facilitiesSpatial data and field observation
Months 3–43–4
Virtual-reality experimentsImmersive CAVE sessions with ethnic-minority participants
Months 5–75–7
AnalysisSpatial analysis and ordered logit modelling
Months 6–96–9
Validate and reportFocus groups, workshop, then the final report in month 12
Months 10–1210–12

What the project will deliver

  • Policy recommendations for culturally inclusive design guidelines and targeted programmes for outdoor facilities and venues.
  • A stakeholder workshop with government, NGOs, planners and community leaders to test the recommendations.
  • A public seminar and a policy booklet summarising the findings for policymakers, professionals and community organisations.
  • International conference papers sharing the methods and findings.

Models in action, details

AI and machine learning in my team’s research

Five recent studies show how my team turns records that public works and contractors already keep into forecasts, risk insight and early warnings. The cash-flow model is the newest; the other four show the wider toolkit behind it.

282
completed public infrastructure projects behind the cash-flow model
0.889
R² of the neural-network contingency model with a complexity index, up from 0.808
HK$36bn+
in 33 completed projects behind the reference class study
1,503
risk descriptions from 277 projects read by deep learning
0.99
R² of the best workforce model (XGBoost) on test data

1. AI cash-flow forecasting

Sing, Ma, Chan and Chan, Project Leadership and Society (2026)Read the paperOpen access

Infrastructure projects do not spend money the same way: some load spending early, some late, some evenly. Standard models force one pattern on all of them. Our model first describes each project’s own spending path with a path signature, a compact mathematical summary of the path, and feeds it with the raw history into an LSTM network. Tested on 282 completed roads, water treatment, school and hospital projects, it forecasts the next 12 months from the last 24.

  1. Step 1
    Monthly spending

    The last 24 months of a live project’s actual expenditure.

  2. Step 2
    Path signature

    Turns the spending path into features that keep its order, turns and higher-order interactions, so each project keeps its own pattern.

  3. Step 3
    LSTM network

    A recurrent neural network learns from the signature features and the raw history together.

  4. Step 4
    12-month forecast

    Next year’s spending, the horizon used for annual budgets, so leaders see drift early.

The forecasts feed budgeting and early warnings, so project leaders can act before spending drifts.

How it compares

R² on test projects, higher is better

Linear regression0.786
Transformer0.806
Encoder–decoder LSTM0.809
Stacked LSTM0.849
Signature-LSTM (ours)0.896
Results reported in the paper. The best signature model (multi-delay, depth 4) was about 5.6% more accurate than the strongest benchmark.

Why it matters

  • One model for different projects. It handles front-loaded, back-loaded and balanced spending without splitting projects into groups.
  • Deeper signatures, better forecasts. Accuracy rose as the signature captured more of each path’s structure (R² from 0.876 to 0.896).
  • Built for governance. A 12-month view matches annual budgeting, liquidity planning and portfolio oversight.

2. Four more ways the team uses AI

Neural networks set contingency from project complexity

Seven machine-learning models were trained on 122 completed public infrastructure projects in Hong Kong to predict how much cost contingency a project will really use. A neural network was the most accurate. Adding a complexity index, built from project size and duration, raised its R² from 0.808 to 0.889 and cut its average error by 27%.

Bootstrapping and Monte Carlo dropout turn the single figure into a range. With the index, 88% of actual outcomes fell inside the bootstrapped range, up from 80%.

R² of the neural network on test projects

Artificial neural networkSeven models comparedComplexity indexBootstrappingMonte Carlo dropout

Sing, Ma and Gu, Applied Sciences (2025)Read the paperOpen access

Outside view meets inside view for contingency

Across 33 completed projects worth over HK$36 billion, reference class forecasting at P70 and P80 was more accurate overall, while project-by-project risk analysis did better for drainage works. The paper sets out how to combine the two, and where machine learning can keep contingencies up to date.

Reference class forecastingProbabilistic risk analysisWilcoxon test

Sing, Kazeem, Chan and Liu, Production Planning & Control (2026)Read the paperOpen access

Deep learning reads risk registers

BERTopic, a transformer-based topic model, read 1,503 risk descriptions from 277 public works projects and found 11 cost risk factors, from design changes and market conditions to project delay and underground conditions. Its topics were more coherent than those of LDA, NMF, CTM, Top2Vec and GPT-4.1.

Sentence embeddingsUMAPHDBSCAN clusteringc-TF-IDF topics

Zhang, Sing, Chan and Liu, Journal of Construction Engineering and Management (2026), corresponding authorRead the paper

Machine learning forecasts the maintenance workforce

Four models were trained on 128 records of seven indicators from maintenance contractors. XGBoost was the most accurate, with R² of 0.99 on test data against 0.88 for a neural network, and an LSTM network forecast the value of maintenance work that drives labour demand.

XGBoostGradient-boosted treesSupport vector machineLSTM

Cao and Sing, Journal of Building Engineering (2024), corresponding authorRead the paper

Research statement

Predicting how infrastructure and buildings will perform

I develop data-driven predictive and decision-support methods for infrastructure and built assets, a programme built over more than a decade in Australia and Hong Kong.

refereed journal articles
98
in Scopus Q1 journals
90%
PhD completions, three current
6
  • Quality. First-authored work in Production Planning & Control links Reference Class Forecasting with project-specific risk; related studies extend prediction to cash flow and maintenance workforce demand.
  • Independent funding. Principal Investigator on Hong Kong’s General Research Fund, Public Policy Research Fund and Construction Industry Council Research and Technology Development Fund; previously Chief Investigator on an Australian Research Council Discovery Project.
  • Researcher development. Six doctoral completions across three universities, the latest rated Excellent by its external examiners, with papers in Applied Energy and Cities.
  • International standing. Adjunct Professor, University of Canberra; assessor for the Australian Research Council; Associate Editor, Built Environment Project and Asset Management; Editor, Edward Elgar’s Research Companion to Infrastructure Asset Management.

Next: testing the emerging predictive models in live operational decisions.

Knowledge transfer statement

Research at work in government and industry

I turn policy and industry problems into research questions, test the models on large operational datasets, and embed the results in government systems, industry planning and professional guidance.

commissions for government, industry and professional bodies
10
as lead consultant
4
commissioned research and consultancy
HK$9.2M
  • Development Bureau, estimating. As Lead Consultant for the reviews of Estimating Using Risk Analysis, I turned cost-overrun research into a probabilistic contingency method and a web-based Monte Carlo tool for project officers.
  • Development Bureau, project surveillance. Since 2023, evidence-based schedule benchmarks and cash-flow forecasting for the Bureau’s project surveillance.
  • Construction Industry Council, manpower planning. The labour-multiplier and stock-flow models I helped develop (2008–2012) still underpin the Council’s industry-wide workforce forecasts, as its 2026 methodology shows.
  • Hong Kong Institute of Surveyors, professional guidance. As Lead Academic Consultant, I led the defects study behind the Institute’s Professional Guide to Building Inspection, Volume 2.

Next: a HK$2.37M Hong Kong Construction Industry Council project turning these models into an AI forecasting platform.

Teaching statement

Active, authentic and AI-enabled learning

I teach from undergraduate to professional doctorate, drawing on my years in the surveying discipline at the Buildings Department and Architectural Services Department.

in classes of 100+ students
4.7/5
students in the programme I lead
300+
students in one AI-tutored class
177
  • Large classes. Rated 4.7 out of 5 in Construction Law (139 students) and Development Control Law (128), above the Faculty’s top-10% mark of 4.6.
  • AI-supported flipped learning. A custom AI tutor for Project Management and Procurement lifted ratings for timely feedback from 4.3 to 4.6; the framework is documented for department-wide use.
  • Programme leadership. Programme Leader, BSc (Hons) in Surveying; previously Deputy Programme Leader of the Doctor of International Real Estate and Construction, and Program Convenor of Newcastle’s Master of Project Management through its accreditation.
  • Standards beyond my own classes. External Examiner for HKU SPACE, PhD examiner for RMIT University and the University of Technology Sydney, and one of five academics leading the AI redesign of the department’s surveying degrees.

Next: embedding AI and digital skills across the surveying curriculum.
Research

AI for infrastructure, buildings and cities

My research has two areas: making infrastructure delivery predictable, and keeping ageing buildings and cities resilient. In both, I use AI and machine learning to predict cost, risk, workforce needs and building condition, and turn the results into tools that agencies use to plan, budget and maintain. Most of the questions come from government and industry.

Four topics: select a card to explore each one.

How the research works

AI and machine learning for infrastructure management

The same four steps run through both research areas. Models learn from records the industry already keeps, are tested under uncertainty, and end as tools that agencies and asset owners can use.

  1. Learn from the records

    Risk registers, cash-flow and payment records, inspection reports, workforce data and street-level images.

  2. Machine learning and AI

    Neural networks, deep learning and LSTM forecasting, large language models and agentic AI.

  3. Test under uncertainty

    Probabilistic risk analysis, Monte Carlo and system dynamics simulation, and reference class forecasting.

  4. Support the decision

    Contingency estimates, cash-flow early warnings, workforce plans and maintenance priorities.

Research area 1

Infrastructure performance, risk and delivery

Cost and schedule performance, contingency, workforce capacity, safety, rework and megaproject benchmarking. My recent work shows that reference class forecasting and probabilistic risk analysis are complementary rather than competing, and sets out how to use them together in public-works estimates.

Where AI and machine learning come in

  • Neural networks and deep learning that estimate cost contingency and pick out risk drivers from project risk registersJournal of Construction Engineering and Management (2026); Applied Sciences (2025)
  • AI cash-flow forecasting that flags projects drifting from their expected spendingProject Leadership and Society (2026)
  • An AI-driven forecasting platform for construction workforce planningHong Kong Construction Industry Council grant, 2026
  • Data-driven, complex-network analysis of the causes of metro construction incidentsTunnelling and Underground Space Technology (2026)

Current external competitive grants

  • AI-driven construction workforce forecasting platformHK$2.37MHong Kong Construction Industry Council Research and Technology Development Fund, lead investigator, 2026
  • Automated benchmarking of megaproject performanceHK$1.17MHong Kong Research Grants Council General Research Fund, co-investigator, 2025

Completed external competitive grants

  • Containment and reduction of rework in transport megaprojectsA$0.28MAustralian Research Council Discovery Project, Chief Investigator, University of Newcastle, 2021
Research area 2

Built-asset and urban resilience

Ageing building stock management and prediction, maintenance workforce forecasting, digital building surveying and evidence-based maintenance planning, so that inspection and repair go where the risk is highest. The same evidence base extends to heritage conservation and inclusive public space.

Where AI and machine learning come in

  • A data-driven prediction model, with AI image recognition of defects, for the condition assessment of ageing residential building stockHong Kong General Research Fund grant, 2026
  • Machine learning and LSTM models that forecast the maintenance and repair workforceJournal of Building Engineering (2024)
  • Large language models that assess how people perceive changing streetscapesBuilding and Environment (2026)
  • An agentic AI pipeline for building-defect intelligenceNew project, 2026

Current external competitive grants

  • Development of a data-driven prediction model for the condition assessment of ageing residential building stockHK$1.11MHong Kong Research Grants Council General Research Fund, lead investigator, 2026
  • Ethnic minorities’ access to outdoor public facilitiesHK$0.51MPublic Policy Research Fund, lead investigator, 2025
  • Innovating property and facility management: harnessing technology to tackle manpower challengesHK$0.27MHong Kong Research Grants Council, Competitive Research Funding Schemes for the Local Self-financing Degree Sector, co-investigator, 2026
  • A cloud-based fuzzy assessment framework for smart age-friendly housing for middle-class elderly in Hong KongHK$0.57MHong Kong Research Grants Council, Competitive Research Funding Schemes for the Local Self-financing Degree Sector, co-investigator, 2025

Completed external competitive grants

  • Integrated approaches for point-line-plane heritage conservation in Hong Kong: international experienceHK$2.00MBuilt Heritage Conservation Fund, HKSAR Government, co-investigator, 2018

Models in action

Four of the models behind the research, from system dynamics to large language models. The diagrams are illustrative.

ageingdecay SoundAgeingDilapidated rehabilitation P policy: inspection and subsidies B

System dynamicsPolicy simulation

How a city’s building stock ages, and what policy can change

Stock-and-flow models follow buildings as they age, decay and are rehabilitated, so inspection and subsidy policies can be tested over decades before they are adopted. The same approach models the workforce infrastructure programmes need.

Cities (2019), first author; Journal of Management in Engineering (2016), first author

today plan actual AI forecast StartCompletion 100%0%

AI forecastingPath signatures + LSTM282 projects

AI cash-flow forecasting for infrastructure projects

Reads each project’s own spending pattern with path signatures, then forecasts the next 12 months with an LSTM network. Tested on 282 completed public infrastructure projects, it beat four benchmark models, raising R² from 0.849 to 0.896.

Project Leadership and Society (2026), first author

cluster A cluster B energy saved light medium deep BIM + energysimulation

BIMEnergy simulationClustering

Retrofitting simulation for public housing estates

Groups ageing residential blocks into clusters of similar buildings, then uses BIM and energy simulation to compare retrofit packages across each cluster, so investment goes to the options that save the most.

Facilities (2020), first author; conference paper on BIM and energy simulation for aged building groups (2021)

LLM lowerhigher perceived quality

Spatial analysisStreet-view imageryLarge language models

Mapping how people perceive the city’s streets

Tests whether ChatGPT-5 can judge streetscapes the way people do, and maps perceived quality and mental well-being across Hong Kong’s dense streets, linking street density and greenness to how people feel.

With PhD candidate Mengyuan Yin: Building and Environment (2026); American Association of Geographers Annual Meeting (2026)

Also in the toolkit: probabilistic risk modelling and Monte Carlo simulation, Bayesian networks, complex-network analysis and multi-criteria decision models. Next, interpretable AI tools that agencies can run themselves.

Government & industry

Work with government and industry

I spent eight years in the surveying discipline of the Hong Kong Government, and much of my research is still commissioned by public bodies. Each project follows the same five steps, from a real problem to a tool the client uses.

  1. A real problem

    An agency or industry body brings a cost, workforce or asset question.

  2. A research question

    Framed so the answer can be tested against data.

  3. Analytical modelling

    Probabilistic, machine-learning or simulation models.

  4. A validated tool

    Checked against past projects before anyone relies on it.

  5. Adopted in practice

    Built into the client’s own procedures and systems.

Development Bureau, HKSAR Government

Risk-based contingency for public works

As lead consultant, I developed probabilistic cost-risk models to replace fixed-percentage contingencies, then extended them to more types of infrastructure. Both rounds were adopted into the Bureau’s estimating practice.

Lead consultant and project manager, 2018 and 2024

Development Bureau, HKSAR Government

Cash-flow forecasting for capital works

An AI cash-flow forecasting model now runs inside the Project Surveillance System, which the Bureau uses to monitor delivery across its capital works programme.

Consultant, two commissions from 2023

Construction Industry Council and Hong Kong Institute of Surveyors

Workforce planning and professional guidance

Manpower projections used in Hong Kong’s construction workforce planning, contractor-selection reference materials, and the evidence behind the Hong Kong Institute of Surveyors’ guidance on maintenance project management.

Six commissions, 2009 to 2018

Government commission, project details

Evidence-based contingency for Hong Kong’s public works

Client
Development Bureau, HKSAR Government
Study
Review and enhancement of Estimating Using Risk Analysis (ERA)
Role
Lead Consultant and Project Manager
Period
2018, with a further phase from 2024 to 2025

Hong Kong’s public works set contingency through Estimating Using Risk Analysis. I helped the Bureau add a probabilistic layer to it: probability curves fitted to past project records, a Monte Carlo engine, and calibration against how completed projects turned out, so contingency is estimated consistently and backed by evidence.

How the model works

  1. Collect the evidence

    Risk registers and cost records from past public works projects, across project types and contract forms.

    Past project records
  2. Fit probability curves

    Each risk factor, from client changes to uncharted utilities, gets its best-fit distribution for each project type.

    Best-fit distributions
  3. Simulate

    Monte Carlo runs combine the risks into a full range of outcomes, giving contingency at P50, P80, P90 and P95.

    Thousands of runs per project
  4. Calibrate and deploy

    Results checked against completed-project outcomes, then built into a web-based tool for project officers.

    Web-based tool

Calibrating the simulation to real outcomes

P50P80P90 Lower contingency Higher contingency →
Illustrative only, not to scale. The simulation turns the risk curves into a range of possible contingency levels. Calibration aligns that range with how completed projects actually turned out, and officers read off the level for the confidence they need.

What the Bureau received

  • A web-based Monte Carlo tool. Officers enter a project, adjust pre-loaded risks and read off contingency at each confidence level.
  • Guidance and training. A step-by-step procedure and user guide so project teams apply the method consistently.
  • Evidence for budget confidence levels. Advice on balancing the risk of overrun against tying up capital in over-cautious reserves.

Government commission, project details

Cash-flow early warning for Hong Kong’s capital works

Client
Development Bureau, HKSAR Government
Studies
Upgrading, review and maintenance of the Project Surveillance System, with reference class forecasting for cost and schedule
Role
Consultant
Period
2023 to 2025

The Project Surveillance System compares each capital project’s spending with the patterns of completed projects and flags those drifting off course, so senior management can step in early. I helped the Bureau strengthen it: benchmark curves from projects that finished on time and on budget, monitoring through to the final account, support for an AI forecasting application, and refreshed reference classes for cost and schedule.

How the early warning works

  1. Learn from past projects

    Cash-flow records from completed capital projects, checked, cleaned and grouped by project type.

    Completed-project records
  2. Put projects on one timeline

    Each project’s spending is rescaled to ten equal stages, so projects of any length can be compared.

    Standard S-curves
  3. Set the performance zones

    Projects that finished on time and on budget define green, yellow and red zones around the expected curve.

    Green, yellow, red zones
  4. Warn early

    Live projects are tracked against the zones, and alerts show which ones need attention, and when.

    Earlier alerts

Spotting a project that is drifting

Yellow alert Red alert 100% 0% Start Ten equal stages Completion Cumulative spend
Illustrative only, not to scale. The zones come from completed projects that finished on time and on budget. A live project that slips out of the green zone raises a yellow alert, then a red one, while there is still time to act.

Setting allowances with reference class forecasting

50%60%70%80%90% 1 2 3 Certainty needed Uplift to add Uplift on the base estimate Level of certainty the allowance is enough
Illustrative only, not to scale. Each class of similar projects has its own curve, for cost and for schedule, drawn from how those projects actually turned out. Officers choose how sure they need to be, go up to the curve and read across to the uplift to add.

What the Bureau received

  • Stricter benchmark curves. Built from well-performing projects, they flagged drifting projects earlier when tested on recently completed work.
  • Monitoring to the final account. The curves now run past completion, so spending is followed until the accounts close.
  • An AI forecasting application. Support for developing and testing a web application that forecasts cost and schedule performance.
  • Refreshed reference classes. Updated cost uplift curves and new schedule curves, read as in the chart above, so allowances match the certainty a project needs.

Professional body commission, project details

Evidence for a professional guide to inspecting post-1980 buildings

Client
Hong Kong Institute of Surveyors, Building Surveying Division
Study
Research for the Professional Guide to Building Inspection, Volume 2: Post-1980 Residential and Composite Buildings in Hong Kong
Role
Lead Academic Consultant
Period
Survey 2022; research report 2023; findings published in Facilities (2026)

Hong Kong’s post-1980 residential and composite buildings are entering the age when inspection and repair become routine, yet practice varies. The study gave the Institute research-based evidence on how building surveyors inspect these buildings, which defects matter most and where practitioners need support, to shape its professional guide.

How the study worked

  1. Review and pilot

    A literature review set the topics, and experienced building surveyors tested the questionnaire before launch.

    Piloted questionnaire
  2. Survey the profession

    An online survey of the Institute’s building surveyors on inspection practice, defects and new techniques, analysed statistically.

    Practitioner survey
  3. Test with practitioners

    A focus group of experienced surveyors checked the results, settled contradictions and shared best practice.

    Focus group
  4. Recommend

    Recommendations on what the guide should cover, from defect causes to report templates and inspection techniques.

    Guide recommendations

What the study covered

123456
  1. 1External walls: water seepage, cracks and curtain walls, and hard-to-reach areas such as lightwells
  2. 2Concrete defects and their causes
  3. 3Drainage above and below ground
  4. 4Fire safety: means of escape, means of access and fire-resisting construction
  5. 5Maintainability designed in from the start
  6. 6New techniques: drones, 3D laser scanning and computer-aided surveys
Illustrative building, not to scale.

What the study found

  • Monitoring and pathology are the hardest part of an inspection, and report writing takes the most time.
  • Water seepage, concrete cracks and curtain walls need the closest attention. Visual inspection is the usual method, but it is less reliable at height and in lightwells.
  • Blockages and misconnections are the most common drainage defects, above and below ground.
  • Early involvement of maintenance professionals in design is the surest way to make buildings easier to maintain.
  • Drones, laser scanning and computer-aided surveys are known to surveyors, but their use in Hong Kong is still at an early stage.

What the Institute received

  • A research report ranking the symptoms, causes and inspection methods for each defect type, checked by practitioners.
  • Recommendations for the guide: defect causes, a report template, and techniques that make inspection more reliable and efficient.

Also on this card: workforce planning for the Construction Industry Council

From 2009 to 2012 I worked on the Council’s manpower research for the construction industry, workforce projection updates, technician projections and a study of workforce mobility. The labour-multiplier and stock-flow models from that work still underpin its industry-wide workforce forecasts. In 2017, as lead consultant, I drafted the Council’s reference materials on the selection of contractors.

Roles in and with government

  • Surveying discipline, HKSAR GovernmentBuildings Department and Architectural Services Department, 8 years

    Condition surveys, defect diagnosis, plan scrutiny, statutory building control and enforcement.

  • Member, Chief Executive’s Policy Unit think tankHKSAR Government

    Contributed research to the Five-Year Plan workshop and took part in Policy Address consultations on infrastructure and built assets.

  • Lead consultant and project managerDevelopment Bureau, Construction Industry Council, Hong Kong Institute of Surveyors

    Led commissioned studies on risk-based estimating, contractor selection and maintenance project management.

  • Founding Secretary, Centre for Infrastructure DeliveryThe Hong Kong Polytechnic University, 2025–now

    Governance of a centre run in partnership with the Development Bureau; helped coordinate its first international conference, with more than 300 participants.

  • Technical committee memberBuildings Department, 2017–2019

    Committee on barrier-free access in buildings.

  • Invited expertHong Kong Institute of Surveyors, 2026

    Task Force on Repair, Maintenance, Alteration and Addition works.

Teaching

Teaching

I teach through real professional problems and use technology to make students’ learning visible. Students at four universities, in Australia and Hong Kong, have rated my teaching highly.

Four topics: select a card to explore each one.

Student ratings

93%
Curtin UniversityPerth, 2012–201493 / 100Certificate of appreciation
93%
City University of Hong Kong2015–20166.48 / 7Top 5% in the Faculty
96%
The Hong Kong Polytechnic University2016–20194.8 / 5Top 10% across the University
93%
University of NewcastleNew South Wales, 2019–20234.64 / 5Letter of appreciation from the Pro Vice-Chancellor
94%
The Hong Kong Polytechnic University2023–now4.7 / 5Classes of 100+ students; top 10% in the Faculty

AI tutor project

Teaching innovation

An AI tutor for a class of 177

For Project Management and Procurement, I built an AI tutor that answers only from the subject’s own materials and two research papers. It supports students before, during and after class, and their tutor transcripts became part of the assessment, so their reasoning could be marked as well as their answers.

Funding, how it works, and three charts of the results
  • 22,000+tutor interactions in one semester
  • 85.9average mark on research-level reading
  • 52.7%of students asked follow-up questions
  • 83%of student comments positive

Subjects

Subjects I teach, from undergraduate to professional doctorate

Undergraduate

  • Construction law, contract law and administration
  • Development control law
  • Project management and procurement
  • Construction project planning with BIM
  • Building surveying, structures and quality in building
  • Capstone projects and professional workshops

Taught postgraduate

  • Digital technologies for construction projects
  • Project scheduling, resources and leadership
  • Project appraisal and managing people in projects
  • Maintenance management and operation
  • Construction law and engineering contract procedure
  • Professional research studio

Professional doctorate and PhD

  • International Study Visit (Asia Pacific), Doctor of International Real Estate and Construction
  • Doctoral thesis supervision
  • Research Frontiers in Construction and Environment
  • PhD supervision: six completions, three current

Leadership

Programme leadership

  1. 2026–nowProgramme Leader, BSc (Hons) in Surveying, PolyU: more than 300 students
  2. 2024–2026Deputy Programme Leader, Doctor of International Real Estate and Construction (DIREC), PolyU
  3. 2023–2024Deputy Programme Leader, MSc in Construction and Real Estate, PolyU
  4. 2019–2023Program Convenor (Program Director), Master of Project Management and Graduate Diploma in Project Management, University of Newcastle
  • Professional accreditationTook degrees through accreditation by the Royal Institution of Chartered Surveyors, the Australian Institute of Building and the Australian Institute of Project Management.
  • Doctoral studentsSix PhD completions and two professional doctorates; the most recent thesis was rated Excellent by both external examiners.
  • Learning beyond the classroomLed a doctoral study visit to Singapore, with seminars at the National University of Singapore and policy sessions at the Urban Redevelopment Authority.

Teaching innovation, project details

An AI tutor for a class of 177

Subject code
BRE350 Project Management and Procurement
Project code
48BV (work programme)
Funding
Seed Fund for Developing Flipped Learning Signature Showcases 2025, Educational Development Centre (EDC), The Hong Kong Polytechnic University
Role
Project Leader, HK$70,000, August 2025 to January 2026

How the AI tutor works

1Designed by the teacher

  • Curated knowledge baseCourse materials and the two assigned journal papers, so answers stay within the subject.
  • Task promptsExplain in simple language, give examples and encourage follow-up questions.
  • A learning pathA node-based workflow, built like an activity-on-node network.

2The tutor, open 24/7

Illustrative exchange

When should a client choose design-and-build rather than traditional procurement?

When the client wants one point of responsibility and an earlier start, and can accept less control over the design. For your case, which matters most: time, cost certainty or design quality?

The learning path, drawn as an activity-on-node network

  1. Choose a topic or paper
  2. Read a section
  3. Ask and follow up
  4. Back to the main path

Built on Botpress. Students treat answers as a guide to check, not an authority.

3Evidence back to the teacher

  • Interaction recordsStudents hand in their tutor transcripts with the assignment and a quiz.
  • Thinking-level analysisQuestions coded by Bloom’s level with AI help, then checked by hand.
  • 53% asked follow-up questionsStudents probed further on the research papers, rather than stopping at the first answer.

The records show where students struggle, so the next class and the tutor’s support can be aimed there. The teacher keeps the design, guidance and assessment.

How a week runs

  1. Before class

    AI-guided reading, concept checks and quizzes, so students arrive knowing what they find difficult.

    1,628 messages from 58 students
  2. In class

    Contact time goes to network analysis, critical path calculations, planning cases and procurement exercises.

    Class time for problem-solving
  3. After class

    Review, reflection and assignment preparation at each student’s own pace.

    6,094 messages from 52 students

The research-paper assignment. Students worked through two journal papers on path float and precedence networks with the tutor, then handed in their transcripts and sat a quiz. 11,109 messages from 164 students; average mark 85.9 out of 100.

Higher marks went with more tutor use

Average tutor messages per student, by mark on the research-paper assignment. Correlation 0.51: a clear link, though not proof that one causes the other.

What students asked the tutor

2,638 learning questions, sorted by Bloom’s taxonomy of thinking skills. 91% went beyond recalling facts. Point at or tap a bar to see a real example.

Student feedback rose on every core item

Timely and useful feedback
4.34.6
Help available after class
4.34.5
Stimulated to learn more
4.34.5
Valuable learning experience
4.34.5
Student Feedback Questionnaire, five-point scale. The overall mean rose from 4.3 to 4.5.

In other people’s words

A journal editor, a former head of discipline, and the students I’ve taught.

  • “I have always valued Michael’s ability to connect his research to the problems faced by practitioners and help translate research outcomes into useful practice.”

    Professor Mohan M. KumaraswamyEditor-in-Chief, Built Environment Project and Asset Management; Honorary Professor, The University of Hong Kong

  • “He demonstrated to me that he could combine effective teaching, leadership and service with an active and high-ranking research profile.”

    Associate Professor Marcus JefferiesFormer Head of Discipline, Construction Management, University of Newcastle, Australia

  • “Dr. Sing not only taught what he prepared on the lecture notes, but he also taught and reminded us some other issues that might be important for our future career.”

    Student, The Hong Kong Polytechnic UniversityAnonymous end-of-subject feedback

  • “This study trip experience [has] equipped me with a more nuanced understanding of global best practices and the importance of contextual adaptation.”

    Doctoral candidateInternational Study Visit (Asia Pacific) to Singapore, 2023/24
Publications

Publications

98 refereed journal articles across two research areas, nine in ten of them in Scopus Q1 journals.

refereed journal articles, 2012 to 2026
98
citations and an h‑index of 35 on Google Scholar
4,899
in Scopus Q1 journals (top 25% of their field); 65% in the top 10%
90%
of the 33 papers since 2023 in Q1 journals; 79% in the top 10%
100%

Papers per year

Since 2023 25 papers in 2025 and 2026 so far.

Four views: select a card to explore each one.

Research focus

Two connected research areas: making infrastructure delivery predictable, and keeping existing buildings and cities resilient. Select a theme to see its papers.

How to read the percentages. Scopus ranks every journal within its subject field. A Q1 journal is in the top 25% of its field, and a top‑10% journal is in the top tenth. The higher the share, the more of the work appears in the field’s leading, most selective journals.

Infrastructure performance, risk and delivery

59 papers86% in Q1 journals64% in top‑10% journals

Built-asset and urban resilience

39 papers95% in Q1 journals67% in top‑10% journals

Five standout papers

  1. 98thpercentile journalFirst author, 2026
    Synergies strategies for cost contingency: uniting reference class forecasting and probabilistic risk analysis in infrastructure project delivery Sing, M.C.P., Kazeem, K.O., Chan, A.P., & Liu, H.J. (2026). Production Planning & Control, 1-16.

    Shows that reference class forecasting and probabilistic risk analysis work best together when setting contingency for public works.

    98th percentile
  2. 98thpercentile journalCorresponding author, 2026
    Can ChatGPT-5 assess human perceptions of dynamic urban streetscape transitions? Insights from Hong Kong Yin, M., Sing, M.C.P., & Yung, E.H.K. (2026). Building and Environment, 114155.

    Tests whether a large language model can judge how people perceive changing streetscapes in Hong Kong, a faster route to evidence for urban design.

    98th percentile
  3. 99thpercentile journalCorresponding author, 2024
    Workforce forecasting in the building maintenance and repair work: evaluating machine learning and LSTM models Cao, N., & Sing, M.C.P. (2024). Journal of Building Engineering, 95, 110125.

    Compares machine-learning and LSTM models for forecasting the maintenance workforce an ageing building stock will need.

    99th percentile12 citations
  4. 83citationsSecond author, 2019
    The cost performance of transportation projects: The fallacy of the Planning Fallacy account Love, P.E., Sing, M.C.P., Ika, L.A., & Newton, S. (2019). Transportation Research Part A: Policy and Practice, 122, 1-20.

    A widely cited contribution to the international debate on why transport projects overrun their budgets.

    96th percentile83 citations
  5. 98thpercentile journalFirst author, 2019
    Rehabilitation of existing building stock: A system dynamics model to support policy development Sing, M.C.P., Love, P.E., & Liu, H.J. (2019). Cities, 87, 142-152.

    A system-dynamics model of the existing building stock that shows how rehabilitation policies play out over time, to support policy development.

    98th percentile39 citations

Books

  1. Monograph, 2015
    Mandatory Building Inspection: An Independent Study on Aged Private Buildings and Professional Workforce in Hong Kong Leung, A.Y.T., Sing, M.C.P., & Chan, H.C. City University of Hong Kong Press. 240 pages. ISBN 978-962-937-239-2.

    Reviews the number and spread of Hong Kong’s aged private buildings under the Mandatory Building Inspection Scheme, and projects the registered-inspector workforce the scheme needs. Written for practising professionals and for students and researchers in surveying and construction.

  2. Edited volume, in preparation
    Research Companion to Infrastructure Asset Management Sing, M.C.P., Chan, A.P.C., & Chan, M.W. (Eds.). Edward Elgar Publishing, Elgar Companions to the Built Environment series. Supported by the Centre for Infrastructure Delivery, The Hong Kong Polytechnic University.

    Chapter proposals are welcome. See the call for chapters.

All 98 papers by research theme

Newest first. Journal percentiles are Scopus CiteScore percentiles; citation counts are from SciVal, September 2026. Role tags show first authorship throughout and corresponding authorship from 2023.

Infrastructure performance, risk and delivery

Cost, contingency and project performanceOverruns, contingency, reference class forecasting, cash flow and life-cycle performance of infrastructure.15 papers 12 in Q1 journals
  1. 2026
    Synergies strategies for cost contingency: uniting reference class forecasting and probabilistic risk analysis in infrastructure project deliverySing, M.C.P., Kazeem, K.O., Chan, A.P., & Liu, H.J. Production Planning & Control, 1-16.98th percentileFirst author
  2. 2026
    Strengthening project leadership and financial governance in Infrastructure: An AI-driven cash flow forecasting modelSing, M.C.P., Ma, L.Q., Chan, A.P. & M.W. Chan. Project Leadership and Society, 7, 100242.97th percentileFirst author
  3. 2026
    Utilizing deep learning for the extraction of cost risk factors from project risk registers: Enhancing contingency estimationZhang, P., Sing, M.C.P., Chan, A.P., & Liu, H.J. Journal of Construction Engineering and Management, 152(5), 04026049.87th percentileCorresponding author
  4. 2025
    Improving cost contingency estimation in infrastructure projects with artificial neural networks and a complexity indexSing, M.C.P., Ma, Q., & Gu, Q. Applied Sciences, 15(7), 3519.82nd percentile3 citationsFirst author
  5. 2022
    A theoretical framework for classifying project complexity at the preconstruction stage using cluster analysis techniquesSing, M.C.P., Edwards, D.J., Leung, A.W., Liu, H., & Roberts, C.J. Engineering, Construction and Architectural Management, 29(9), 3754-3774.97th percentile11 citationsFirst author
  6. 2022
    Does the planning fallacy prevail in social infrastructure projects? Empirical evidence and competing explanationsLove, P.E., Ika, L.A., & Sing, M.C.P.. IEEE Transactions on Engineering Management, 69(6), 2588-2602.93rd percentile39 citations
  7. 2019
    The cost performance of transportation projects: The fallacy of the Planning Fallacy accountLove, P.E., Sing, M.C.P., Ika, L.A., & Newton, S. Transportation Research Part A: Policy and Practice, 122, 1-20.96th percentile83 citations
  8. 2019
    Conceptual framework of life-cycle performance measurement: Ensuring the resilience of transport infrastructure assetsLiu, H.J., Love, P.E., Sing, M.C.P., Niu, B., & Zhao, J. Transportation Research Part D: Transport and Environment, 77, 615-626.94th percentile40 citations
  9. 2019
    Ex post evaluation of economic infrastructure assets: Significance of regional heterogeneities in AustraliaLiu, H.J., Love, P.E., Sing, M.C.P., & Smith, J. Journal of Infrastructure Systems, 25(2), 05019005.69th percentile5 citations
  10. 2018
    Cost profiling of water infrastructure projectsLove, P.E., Ahiaga-Dagbui, D.D., Smith, S.D., Sing, M.C.P., & Tokede, O. Journal of Infrastructure Systems, 24(4), 04018023.69th percentile19 citations
  11. 2017
    Off the rails: The cost performance of infrastructure rail projectsLove, P.E., Zhou, J., Edwards, D.J., Irani, Z., & Sing, M.C.P.. Transportation Research Part A: Policy and Practice, 99, 14-29.96th percentile74 citations
  12. 2015
    Estimating construction contingency: Accommodating the potential for cost overruns in road construction projectsLove, P.E., Sing, M.C.P., Carey, B., & Kim, J.T. Journal of Infrastructure Systems, 21(2), 04014035.69th percentile80 citations
  13. 2014
    Overruns in transportation infrastructure projectsLove, P.E., Sing, M.C.P., Wang, X., Irani, Z., & Thwala, D.W. Structure and Infrastructure Engineering, 10(2), 141-159.92nd percentile75 citations
  14. 2013
    Determining the probability of project cost overrunsLove, P.E., Wang, X., Sing, M.C.P., & Tiong, R.L. Journal of Construction Engineering and Management, 139(3), 321-330.87th percentile125 citations
  15. 2013
    Probability distribution fitting of schedule overruns in construction projectsLove, P.E., Sing, M.C.P., Wang, X., Edwards, D.J., & Odeyinka, H. Journal of the Operational Research Society, 64(8), 1231-1247.87th percentile32 citations
Workforce and productivity forecastingLabour demand and supply models, from manpower multipliers to system dynamics.9 papers 8 in Q1 journals
  1. 2022
    Dynamic construction site layout planning: an application of branch and bond algorithmSing, M.C.P., Fung, I.W., Edwards, D.J., & Liu, H. International Journal of Building Pathology and Adaptation, 40(4), 523-538.81st percentile9 citationsFirst author
  2. 2021
    Predicting production-output performance within a complex business environment: from singular to multi-dimensional observations in evaluationLiu, H.J., Love, P.E., Ma, L., & Sing, M.C.P.. International Journal of Production Research, 59(24), 7390-7399.97th percentile11 citations
  3. 2017
    Critical analysis of construction workforce sustainability in a developed economy–case study in Hong KongSing, M.C.P., Tam, V.W., Fung, I.W., & Liu, H.J. Proceedings of the Institution of Civil Engineers-Engineering Sustainability, 171(7), 342-350.72nd percentile18 citationsFirst author
  4. 2016
    Dynamic modeling of workforce planning for infrastructure projectsSing, M.C.P., Love, P.E., Edwards, D.J., & Liu, J. Journal of Management in Engineering, 32(6), 04016019.99th percentile35 citationsFirst author
  5. 2015
    Forecasting private-sector construction works: VAR model using economic indicatorsSing, M.C.P., Edwards, D.J., Liu, H.J., & Love, P.E. Journal of Construction Engineering and Management, 141(11), 04015037.87th percentile29 citationsFirst author
  6. 2015
    Modeling Australia’s construction workforce demand: Empirical study with a global economic perspectiveLiu, J., Love, P.E., Sing, M.C.P., Carey, B., & Matthews, J. Journal of Construction Engineering and Management, 141(4), 05014019.87th percentile23 citations
  7. 2014
    Forecasting the demand and supply of technicians in the construction industrySing, M.C.P., Love, P.E., & Tam, C.M. Journal of Management in Engineering, 30(3), 04014006.99th percentile19 citationsFirst author
  8. 2012
    Multiplier model for forecasting manpower demandSing, M.C.P., Love, P.E., & Tam, C.M. Journal of Construction Engineering and Management, 138(10), 1161-1168.87th percentile27 citationsFirst author
  9. 2012
    Stock-flow model for forecasting labor supplySing, M.C.P., Love, P.E.D., & Tam, C.M. Journal of Construction Engineering and Management, 138(6), 707-715.87th percentile26 citationsFirst author
Safety, quality and reworkAccidents and near misses, safety climate, quality management and the cost of rework.15 papers 14 in Q1 journals
  1. 2026
    Data-driven causal factor analysis of metro construction incidents using complex network theoryZhang, P., Sing, M.C.P., Chan, A.P., & Guo, S. Tunnelling and Underground Space Technology, 171, 107464.96th percentile1 citationCorresponding author
  2. 2026
    What drives the adoption of quality management behavior? Evidence from owners in China’s construction industryZhang, P., Sing, M.C.P., & Guo, S. Journal of Engineering, Design and Technology, 24(3), 692-712.92nd percentileCorresponding author
  3. 2025
    Developing a probabilistic model for evaluating the organisational safety climate factors in offsite manufacturingVithanage, S.C., Sing, M.C.P., Davis, P.R., & Newaz, T. Engineering, Construction and Architectural Management, 33(4), 2771-2792.97th percentile
  4. 2025
    Causal factors of near misses and accidents in urban railway construction: A complex network approachZhang, P., Sing, M.C.P., Guo, S., Chan, I.Y., & Fung, I.W. Journal of Construction Engineering and Management, 151(7), 04025081.87th percentile3 citationsCorresponding author
  5. 2022
    State of science: Why does rework occur in construction? What are its consequences? And what can be done to mitigate its occurrence?Love, P.E., Matthews, J., Sing, M.C.P., Porter, S.R., & Fang, W. Engineering, 18, 246-258.99th percentile45 citations
  6. 2022
    Systematic review on the identification of safety risks in off-site manufacturing (OSM)Vithanage, S.C., Sing, M.C.P., Davis, P., & Pillay, M. Journal of Engineering, Design and Technology, 20(4), 935-964.92nd percentile11 citations
  7. 2022
    Assessing the off-site manufacturing workers’ influence on safety performance: A Bayesian network approachVithanage, S.C., Sing, M.C.P., Davis, P., & Newaz, M.T. Journal of Construction Engineering and Management, 148(1), 04021185.87th percentile21 citations
  8. 2019
    Make-or-break during production: shedding light on change-orders, rework and contractors margin in constructionLove, P.E., Ika, L.A., Ahiaga-Dagbui, D.D., Locatelli, G., & Sing, M.C.P.. Production Planning & Control, 30(4), 285-298.98th percentile25 citations
  9. 2019
    Risk of excavators overturning: determining horizontal centrifugal force when slewing freely suspended loadsEdwards, D., Parn, E.A., Sing, M.C.P., & Thwala, W.D. Engineering, Construction and Architectural Management, 26(3), 479-498.97th percentile56 citations
  10. 2019
    Evaluation of workplace safety performance in the Chinese petroleum industryMa, M., Tam, V.W., Huang, Z., Sing, M.C.P., & Shen, L. Organization, Technology & Management in Construction: An International Journal, 11(1), 1904-1910.54th percentile1 citation
  11. 2017
    Development of an accident modelling in the Hong Kong construction industryTsang, Y.T., Fung, I.W., Tam, V.W., Sing, M.C.P., & Lu, C.T. International Journal of Construction Management, 17(2), 124-131.98th percentile16 citations
  12. 2016
    Psychological climate in occupational safety and health: the safety awareness of construction workers in South ChinaFung, I.W., Tam, V.W., Sing, M.C.P., Tang, K.K.W., & Ogunlana, S.O. International Journal of Construction Management, 16(4), 315-325.98th percentile44 citations
  13. 2015
    The symbiotic nature of safety and quality in construction: Incidents and rework non-conformancesLove, P.E., Teo, P., Carey, B., Sing, M.C.P., & Ackermann, F. Safety Science, 79, 55- 62.98th percentile48 citations
  14. 2014
    Personality and occupational accidents: Bar benders in Guangdong province, Shenzhen, ChinaSing, M.C.P., Love, P.E.D., Fung, I.W.H., & Edwards, D.J. Journal of Construction Engineering and Management, 140(7), 05014005.87th percentile36 citationsFirst author
  15. 2013
    Determining the probability distribution of rework costs in construction and engineering projectsLove, P.E., & Sing, M.C.P.. Structure and Infrastructure Engineering, 9(11), 1136-1148.92nd percentile48 citations
PPPs, procurement and governanceValue for money, risk and accountability in public-private partnerships, and integrity in project organisations.20 papers 17 in Q1 journals
  1. 2026
    Drivers of integrity-based approach to ethics management (IBAEM) in the Nigerian construction organizationsOjo, L.D., Chan, A.P., & Sing, M.C.P.. International Journal of Construction Management, 26(3), 396-411.98th percentile4 citations
  2. 2026
    Impact of integrity-based approach to ethics management on construction organization reputationOjo, L.D., Chan, A.P., Sing, M.C.P., Kazeem, K.O., & Debrah, C. International Journal of Construction Management, 1-20.98th percentile
  3. 2026
    Impact of integrity-based approach to ethics management on project performanceOjo, L.D., Chan, A.P., Sing, M.C.P., & Kazeem, K.O. Journal of Engineering, Design and Technology, 24(5), 1114-1134.92nd percentile1 citation
  4. 2025
    Characterisation and the relationships between the critical barriers to integrity-based approach to ethics management in the Nigerian construction industryOjo, L.D., Chan, A.P.C., & Sing, M.C.P.. Engineering, Construction and Architectural Management, 1-24.97th percentile4 citations
  5. 2025
    Examining the mediating role of personal integrity in reducing corruption in construction projectsOjo, L.D., Chan, A.P., & Sing, M.C.P.. Engineering, Construction and Architectural Management, 1-26.97th percentile1 citation
  6. 2025
    Impact of integrity-based approach to ethics management on the integrity of construction professionals in a developing economyOjo, L.D., Chan, A.P., & Sing, M.C.P.. Journal of Engineering, Design and Technology, 24(1), 85-104.92nd percentile7 citations
  7. 2024
    Conceptual framework for enabling PPP project sustainability: a system thinking and modellingWang, X., Zhang, Q., Wang, D., Liu, H.J., & Sing, M.C.P.. Production Planning & Control, 35(15), 2034-2049.98th percentile3 citations
  8. 2024
    Pre-construction duration of public-private partnership projects: evidence from ChinaWang, C., Liu, H., Sing, M.C.P., & Wu, J. Engineering, Construction and Architectural Management, 31(4), 1715-1729.97th percentile1 citation
  9. 2023
    Value for money assessments for public-private partnerships: Characteristics, research directions, and policy implicationsZhao, J., Liu, H.J., Love, P.E., Greenwood, D., & Sing, M.C.P.. Developments in the Built Environment, 16, 100246.98th percentile12 citations
  10. 2023
    Optimisation of government subsidies in infrastructure public-private partnershipsWang, D., Wang, X., Wang, L., Liu, H., Sing, M.C.P., & Liu, B. Journal of Engineering, Design and Technology, 21(5), 1280-1302.92nd percentile6 citations
  11. 2022
    Public-private partnerships: A dynamic discrete choice model for road projectsZhao, J., Liu, H.J., Love, P.E., Greenwood, D.J., & Sing, M.C.P.. Socio-Economic Planning Sciences, 82, 101227.96th percentile25 citations
  12. 2022
    Managing participants’ behaviors: a framework to improve the process efficiency of transport public-private partnershipsDong, X., Du, Y., Liu, H.J., Sing, M.C.P., & Wu, J. Journal of Infrastructure Systems, 28(1), 04021047.69th percentile4 citations
  13. 2021
    Delivery of transport infrastructure assets: decision-making model to ensure value for moneyZhao, J., Liu, H.J., Sing, M.C.P., Jin, X., & Ginige, K. Journal of Infrastructure Systems, 27(1), 05020008.69th percentile6 citations
  14. 2020
    Public–private partnerships: implications from policy changes for practice in managing risksWu, J., Liu, H.J., Sing, M.C.P., Humphrey, R., & Zhao, J. Engineering, Construction and Architectural Management, 27(9), 2253-2269.97th percentile11 citations
  15. 2018
    From design to operations: a process management life-cycle performance measurement system for Public-Private PartnershipsLiu, H.J., Love, P.E., Smith, J., Irani, Z., Hajli, N., & Sing, M.C.P.. Production Planning & Control, 29(1), 68-83.98th percentile62 citations
  16. 2018
    Evaluation of public–private partnerships: a life-cycle performance prism for ensuring value for moneyLiu, H.J., Love, P.E., Smith, J., Sing, M.C.P., & Matthews, J. Environment and Planning C: Politics and Space, 36(6), 1133-1153.84th percentile41 citations
  17. 2017
    Learning to build relationships for a better Australia: Indigenous reconciliation in action in the construction and resource sectorsHeard, I., Love, P.E., Sing, M.C.P., & Goerke, V. Construction Innovation, 17(1), 4-24.96th percentile8 citations
  18. 2017
    PPP social infrastructure procurement: Examining the feasibility of a lifecycle performance measurement frameworkLiu, J., Love, P.E., Sing, M.C.P., Smith, J., & Matthews, J. Journal of Infrastructure Systems, 23(3), 04016041.69th percentile39 citations
  19. 2016
    Praxis of performance measurement in public-private partnerships: Moving beyond the iron triangleLiu, J., Love, P.E., Smith, J., Matthews, J., & Sing, M.C.P.. Journal of Management in Engineering, 32(4), 04016004.99th percentile66 citations
  20. 2016
    Government accountability within infrastructure public–private partnershipsWu, J., Liu, J., Jin, X., & Sing, M.C.P.. International Journal of Project Management, 34(8), 1471-1478.91st percentile49 citations

Built-asset and urban resilience

Ageing buildings, maintenance and facilitiesCondition, inspection, maintenance workforce, retrofitting and facilities performance of existing buildings.10 papers 9 in Q1 journals
  1. 2026
    Mapping urban renewal priorities for aging residential districts: A spatial evaluation framework based on EWM-TOPSISCao, N., Sing, M.C.P., & Hou, C.H. Journal of Urban Management.95th percentile4 citationsCorresponding author
  2. 2026
    A comprehensive analysis of building inspection practices for post-1980 residential and composite buildings in Hong KongSing, M.C.P., Cao, N., Dy, P.W., & Wong, S. Facilities, 44(5-6), 411-430.85th percentileFirst author
  3. 2025
    Transformative response in office workplace: A systematic review of post-pandemic changesHou, H., & Sing, M.C.P.. Buildings, 15(9), 1519.91st percentile13 citations
  4. 2024
    Workforce forecasting in the building maintenance and repair work: evaluating machine learning and LSTM modelsCao, N., & Sing, M.C.P.. Journal of Building Engineering, 95, 110125.99th percentile12 citationsCorresponding author
  5. 2023
    Post-occupancy evaluation: process delineation and implementation trends in the UK higher education sectorRoberts, C., Edwards, D.J., Sing, M.C.P., & Aigbavboa, C. Architectural Engineering and Design Management, 19(2), 125-147.94th percentile11 citations
  6. 2022
    Importance of hospital facilities management performance indicators: Building practitioners’ perspectivesLai, J.H., Hou, H.C., Chiu, B.W., Edwards, D., Yuen, P.L., Sing, M.C.P., & Wong, P. Journal of Building Engineering, 45, 103428.99th percentile32 citations
  7. 2020
    Energy-efficient retrofitting of multi-storey residential buildingsSing, M.C.P., Chan, V.W., Lai, J.H., & Matthews, J. Facilities, 39(11-12), 722-736.85th percentile16 citationsFirst author
  8. 2019
    Rehabilitation of existing building stock: A system dynamics model to support policy developmentSing, M.C.P., Love, P.E., & Liu, H.J. Cities, 87, 142-152.98th percentile39 citationsFirst author
  9. 2016
    Building maintenance and repair: determining the workforce demand and supply for a mandatory building-inspection schemeSing, M.C.P., Chan, H.C., Love, P.E.D., & Leung, A.Y.T. Journal of Performance of Constructed Facilities, 30(2), 04015014.68th percentile5 citationsFirst author
  10. 2014
    Experimental study on condition assessment of reinforced concrete structure using a dynamics response approachSing, M.C.P., Love, P.E.D., & Davis, P.R. Structural Survey (now International Journal of Building Pathology and Adaptation), 32(2), 89-101.81st percentileFirst author
Digital construction and asset informationBIM, scan-to-BIM, systems information models and digital plan scrutiny.15 papers 15 in Q1 journals
  1. 2025
    Building information modelling and project performance: A framework unpacking the trustWang, D., Wang, X., Liu, H., Liu, M., Sing, M.C.P., & Tang, H. Engineering, Construction and Architectural Management, 1-23.97th percentile1 citation
  2. 2025
    Digitalization in Hong Kong building surveying: advancements in building plan scrutiny processSing, M.C.P., Cao, N., Liu, H.J., & Choy, Y.N. Built Environment Project and Asset Management, 15(4), 827-843.89th percentileFirst author
  3. 2024
    Scan-to-BIM technique in building maintenance projects: practicing quantity take-offSing, M.C.P., Luk, S.Y., Chan, K.H., Liu, H.J., & Humphrey, R. International Journal of Building Pathology and Adaptation, 42(6), 1250-1262.81st percentile13 citationsFirst author
  4. 2018
    Origins and probabilities of MEP and structural design clashes within a federated BIM modelPärn, E.A., Edwards, D.J., & Sing, M.C.P.. Automation in Construction, 85, 209-219.99th percentile98 citations
  5. 2017
    The building information modelling trajectory in facilities management: A reviewPärn, E.A., Edwards, D.J., & Sing, M.C.P.. Automation in Construction, 75, 45-55.99th percentile365 citations
  6. 2017
    System information modelling in practice: Analysis of tender documentation quality in a mining mega-projectLove, P.E.D., Zhou, J., Matthews, J., Sing, M.C.P., & Edwards, D.J. Automation in Construction, 84, 176-183.99th percentile27 citations
  7. 2016
    Retrospective future proofing of a copper mine: Quantification of errors and omissions in ‘As-built’ documentationLove, P.E., Zhou, J., Matthews, J., & Sing, M.C.P.. Journal of Loss Prevention in the Process Industries, 43, 414-423.90th percentile7 citations
  8. 2015
    Future proofing PPPs: Life-cycle performance measurement and building information modellingLove, P.E., Liu, J., Matthews, J., Sing, M.C.P., & Smith, J. Automation in Construction, 56, 26-35.99th percentile127 citations
  9. 2015
    Object-oriented model for life cycle management of electrical instrumentation control projectsZhou, J., Love, P.E.D., Matthews, J., Carey, B., & Sing, M.C.P.. Automation in Construction, 49, 142-151.99th percentile20 citations
  10. 2015
    Toward productivity improvement in electrical engineering documentationZhou, J., Love, P.E.D., Matthews, J., Carey, B., Sing, M.C.P., & Edwards, D.J. International Journal of Productivity and Performance Management, 64(8), 1024-1040.93rd percentile7 citations
  11. 2015
    A systems information model for managing electrical, control, and instrumentation assetsLove, P.E., Zhou, J., Matthews, J., Sing, M.C.P., & Carey, B. Built Environment Project and Asset Management, 5(3), 278-289.89th percentile14 citations
  12. 2014
    Discussion of "State of Practice of Building Information Modeling in the Electrical Construction Industry" by Awad S. Hanna, Michael Yeutter, and Diane G. AounLove, P.E.D., Zhou, J., Matthews, J., Sing, M.C.P., Olatunji, O., & Carey, B. Journal of Construction Engineering and Management, 142(3), 07015001.87th percentile3 citations
  13. 2014
    Assessing the impact of RFIs in electrical and instrumentation engineering contractsLove, P.E., Zhou, J., Sing, M.C.P., & Kim, J.T. Journal of Engineering Design, 25(4-6), 177-193.78th percentile19 citations
  14. 2013
    A conceptual framework for integrating building information modeling with augmented realityWang, X., Love, P.E., Kim, M.J., Park, C.S., Sing, M.C.P., & Hou, L. Automation in Construction, 34, 37-44.99th percentile255 citations
  15. 2013
    Documentation errors in instrumentation and electrical systems: Toward productivity improvement using System Information ModelingLove, P.E., Zhou, J., Sing, M.C.P., & Kim, J.T. Automation in Construction, 35, 448-459.99th percentile33 citations
Sustainable construction, cities and housingNet-zero and energy transition, smart and inclusive cities, prefabrication and housing markets.14 papers 13 in Q1 journals
  1. 2026
    Can ChatGPT-5 assess human perceptions of dynamic urban streetscape transitions? Insights from Hong KongYin, M., Sing, M.C.P., & Yung, E.H.K. Building and Environment, 114155.98th percentileCorresponding author
  2. 2026
    Navigating the energy trilemma: integrating technological innovation and digitalization transition for sustainable solutionsLi, J., Sing, M.C.P., & Guan, M. Applied Energy, 412, 127717.98th percentileCorresponding author
  3. 2026
    Accelerating net-zero construction: A systematic review of multidimensional assessments and strategic interventions for energy sustainabilityLi, J., & Sing, M.C.P.. Energy Strategy Reviews, 64, 102123.88th percentileCorresponding author
  4. 2025
    Exploring the decoupling evolution of carbon emissions from economic efficiency in the construction sector: a comparative perspectiveLi, J., Sing, M.C.P., Huang, Z., & Shen, L. Sustainable Cities and Society, 128, 106464.99th percentile4 citationsCorresponding author
  5. 2025
    Bridging the gap between smart cities and sustainability: Current practices and future trendsGu, Q., Sing, M.C.P., Jefferies, M., & Kanjanabootra, S. Cities, 159, 105799.98th percentile22 citations
  6. 2025
    Mapping energy sustainability: An integrated analysis of research landscapes and policy alignmentLi, J., & Sing, M.C.P.. Environmental Science & Policy, 174, 104282.96th percentile2 citationsCorresponding author
  7. 2023
    Developing an analytic hierarchy process-based decision model for modular construction in urban areasSing, M.C.P., Chan, J., Liu, H., & Ngai, N.N. Journal of Engineering, Design and Technology, 21(4), 1212-1229.92nd percentile10 citationsFirst author
  8. 2022
    Preconditions, processes and structures: Interorganisational collaboration in the provision of post-disaster water, sanitation and hygiene (WASH) servicesHosseinpourtehrani, M., Gajendran, T., Maund, K., & Sing, M.C.P.. International Journal of Disaster Risk Reduction, 80, 103177.95th percentile6 citations
  9. 2022
    Mapping the knowledge domain of affected local community participation research in megaproject-induced displacementZhang, S., Mackee, J., Sing, M.C.P., & Tang, L.M. Sustainability, 14(22), 14745.87th percentile9 citations
  10. 2021
    Housing price dynamics on residential construction: A case study of the Australian property sectorMa, L., Liu, H.J., Edwards, D.J., & Sing, M.C.P.. Structural Change and Economic Dynamics, 59, 525-532.91st percentile6 citations
  11. 2020
    Responsiveness of residential construction-production progress to house price dynamicsMa, L., Liu, H., & Sing, M.C.P.. International Journal of Housing Markets and Analysis, 14(4), 796-814.80th percentile
  12. 2016
    Adaptive reuse in sustainable development: An empirical study of a Lui Seng Chun building in Hong KongTam, V.W., Fung, I.W., & Sing, M.C.P.. Renewable and Sustainable Energy Reviews, 65, 635-642.98th percentile29 citations
  13. 2015
    Best practice of prefabrication implementation in the Hong Kong public and private sectorsTam, V.W., Fung, I.W., Sing, M.C.P., & Ogunlana, S.O. Journal of Cleaner Production, 109, 216-231.98th percentile196 citations
  14. 2015
    Systematic analysis of affordable housing development and pricing structure in Shenzhen, ChinaSing, M.C.P., Love, P.E., Fung, W.H., & He, Y.Z. International Journal of Strategic Property Management, 19(1), 58-65.42nd percentile3 citationsFirst author
Google Scholar ORCID 0000-0003-3526-0702 Scopus author profile
Portrait of Michael C.P. Sing, wearing glasses and a navy blazer
Michael C.P. SingChartered Building Surveyor (FRICS)PhD (Curtin), MEng with Distinction and BSc First Class Honours (CityU).
About

A surveyor first, then an academic

I am a chartered building surveyor, born and educated in Hong Kong. I spent my first eight years in the Hong Kong Government, in the surveying discipline of the Buildings Department and the Architectural Services Department, and the work went well beyond inspection. It taught me that good decisions balance technical limits, regulation, budgets and people, and it still shapes what I research and how I teach.

Eight years in government, across the life of a building

  1. PlanNew development schemesPlan scrutiny and statutory building control
  2. DeliverProject managementMaintenance and alteration works
  3. RegulateBuilding control and enforcementStatutory control of existing buildings
  4. DiagnoseCondition surveysCondition assessment and defect diagnosis

The same span runs through my research, from project delivery to the ageing building stock.

Since 2012 I’ve worked full time in universities in Australia and Hong Kong. I took my PhD at Curtin University in Perth, stayed on as Senior Lecturer, and later spent four years as Associate Professor at the University of Newcastle, where I led the Master of Project Management through accreditation by the Australian Institute of Project Management. I’m now at The Hong Kong Polytechnic University, and I keep close ties with Australia as an Adjunct Professor at the University of Canberra and an assessor for the Australian Research Council.

Professional qualifications in the UK, Hong Kong and Australia

  • UK and worldwideFRICSFellow, Royal Institution of Chartered SurveyorsChartered Building Surveyor and Property and Facilities Management Surveyor. Member from 2013, Fellow from 2023.
  • Hong KongMHKISMember, Hong Kong Institute of Surveyors
  • AustraliaMAIBSMember, Australian Institute of Building Surveyors
  • AustraliaMAIPMMember, Australian Institute of Project Management

I also assess other surveyors. Chairperson of the Royal Institution of Chartered Surveyors’ Assessment of Professional Competence Panel since 2023, with more than 20 assessments chaired in Hong Kong and Australia. I have also led degrees through accreditation by the Royal Institution of Chartered Surveyors, the Australian Institute of Building and the Australian Institute of Project Management.

Career

  1. 2026–2029Adjunct ProfessorUniversity of Canberra
  2. 2023–nowAssistant Professor; Programme Leader, BSc (Hons) Surveying from 2026The Hong Kong Polytechnic University
  3. 2019–2023Associate Professor and Program Convenor (Program Director), Master of Project Management and Graduate Diploma in Project ManagementUniversity of Newcastle, NSW
  4. 2016–2019Research Assistant ProfessorThe Hong Kong Polytechnic University
  5. 2015–2016Visiting Assistant ProfessorCity University of Hong Kong
  6. 2012–2014Senior LecturerCurtin University, Perth
  7. 8 yearsSurveying discipline, Buildings Department and Architectural Services DepartmentHKSAR Government

Education

  1. DoctoratePhD, Construction Management and EngineeringCurtin University, Australia, 2012
  2. Master’sMaster of Engineering, Building Engineering, with DistinctionCity University of Hong Kong
  3. Bachelor’sBSc (Hons) Building Surveying, First Class HonoursCity University of Hong Kong

Service to the field

  • Associate Editor, Built Environment Project and Asset Management
  • Editor, Edward Elgar’s Research Companion to Infrastructure Asset Management (chapter proposals welcome)
  • Assessor, Australian Research Council
  • Chairperson, Assessment of Professional Competence (APC) Panel, Royal Institution of Chartered Surveyors
  • PhD examiner for RMIT, UTS and Western Sydney University
  • Founding Secretary, PolyU Centre for Infrastructure Delivery

Quick facts

Disciplines
Building surveying; construction and project management; infrastructure and built-asset management; property and facilities management; real estate and the built environment.
Research areas
Infrastructure cost risk and contingency, construction workforce forecasting, megaproject performance, the condition and maintenance of ageing buildings, and urban resilience. Methods include AI and machine learning, probabilistic modelling and simulation.
Leadership
Programme Leader of the BSc (Hons) in Surveying at PolyU, with more than 300 students; before that, Deputy Programme Leader of the Doctor of International Real Estate and Construction (DIREC). Previously Program Convenor (Program Director) of the Master of Project Management and the Graduate Diploma in Project Management, and a School Executive member at the University of Newcastle. Lead consultant and project manager on studies for the Development Bureau, the Construction Industry Council and the Hong Kong Institute of Surveyors. Founding Secretary of the Centre for Infrastructure Delivery, Associate Editor of Built Environment Project and Asset Management, editor of an Edward Elgar research companion, and Chairperson of the Royal Institution of Chartered Surveyors’ Assessment of Professional Competence (APC) Panel.
Teaching
Construction law and contract administration, development control, project management and procurement, project planning and cost planning with BIM, building surveying, building maintenance and asset management, and research methods. From first year to professional doctorate, face to face and online.
Professional standing
Fellow of the Royal Institution of Chartered Surveyors (FRICS) since 2023, and a member since 2013; Member of the Hong Kong Institute of Surveyors (MHKIS), Member of the Australian Institute of Building Surveyors (MAIBS) and Member of the Australian Institute of Project Management (MAIPM). Chairperson of the Royal Institution of Chartered Surveyors’ Assessment of Professional Competence (APC) Panel. Led degrees through accreditation by the Royal Institution of Chartered Surveyors and the Australian Institute of Building at Curtin University, and by the Australian Institute of Project Management at the University of Newcastle.
Professional ethics
Bound by the Rules of Conduct of the Royal Institution of Chartered Surveyors. Chairperson of its Assessment of Professional Competence Panel since 2023, with more than 20 assessments chaired in Hong Kong and Australia that test candidates’ competence, ethics and statutory knowledge. Formerly a member of Curtin University’s Human Research Ethics Committee, and co-author of five recent papers on integrity-based ethics management in construction.
Doctoral supervision
Six PhD candidates supervised to completion, plus two professional doctorates. Chief supervisor of three current PhD candidates at PolyU and co-supervisor of one at the University of Canberra. Thesis examiner for RMIT University, the University of Technology Sydney and Western Sydney University.
Collaboration
Open to joint research, industry partnerships, advisory and consultancy work, PhD supervision and examining, and visiting, adjunct and leadership appointments, with universities, government and research organisations in Hong Kong, the UK and Australia.
Contact

Start a conversation

Commissioning a study, planning a joint grant, considering an appointment, or looking for a supervisor, examiner or speaker? Send a note and I’ll reply personally.

Enter your name.

Enter an email address like name@organisation.org.

Write a short message so I know how to help.

Your details are used only to reply to you.