Real estate development and construction risk management using artificial intelligence | |
| Author | Natthawat Chouydamrong |
| Call Number | AIT Thesis no.CM-25-07 |
| Subject(s) | Construction industry--Risk management--Technological innovations Artificial intelligence--Industrial applications |
| Note | A thesis submitted in partial fulfillment of the requirements for the Degree of Master of Engineering in Construction, Engineering and Infrastructure Management |
| Publisher | Asian Institute of Technology |
| Abstract | The assessment of construction and real estate project risks is critical for ensuring timely delivery, cost control, and quality outcomes in housing developments. This thesis investigates a comprehensive risk assessment framework by integrating expert‐validated input factors with an Artificial Neural Network (ANN) model. Through a literature review, expert surveys, and case‐study data collection, key variables from project size and complexity to financial and market factors were identified and quantified. The ANN model was trained and validated in MATLAB using a feedforward architecture with a single hidden layer of 22 neurons and Levenberg–Marquardt backpropagation. Predictor importance analysis revealed that equipment availability, cash flow management, and project complexity exert the greatest influence on project completion delays, while market trends, project complexity, and interest rate fluctuations most strongly drive cost overruns. Other outputs quality and safety issues, funding shortfalls, and ROI impact were likewise analyzed to highlight the dominant risk drivers for each category. The findings underscore the value of combining quantitative modeling with expert judgment to capture nonlinear interactions among risk factors. Practical recommendations include prioritizing resource‐planning controls, strengthening financial monitoring systems, and focusing training on the highest‐impact predictors. By applying this ANN‑based framework, developers and project managers can generate more accurate early warning signals, optimize risk mitigation strategies, and ultimately enhance the sustainability and profitability of housing projects. |
| Year | 2025 |
| Type | Thesis |
| School | School of Engineering and Technology |
| Department | Department of Civil and Infrastucture Engineering (DCIE) |
| Academic Program/FoS | Construction Engineering and Infrastructure Management (CM) |
| Chairperson(s) | Hadikusumo, Bonaventura H.W. |
| Examination Committee(s) | Avirut Puttiwongrak;Wasan Teerajetgul |
| Scholarship Donor(s) | Royal Thai Government |
| Degree | Thesis (M. Eng.) - Asian Institute of Technology, 2025 |