| Author: | Liu, Kenny Ting Kan |
| Title: | Integrating Artificial Intelligence (AI) into pre-construction : an analysis of professional perceptions and literature to develop an initial framework |
| Advisors: | Seo, Joon Oh (BRE) |
| Degree: | DIREC |
| Year: | 2025 |
| Department: | Department of Building and Real Estate |
| Pages: | viii, 316 pages : color illustrations |
| Language: | English |
| Abstract: | The incorporation of Artificial Intelligence (AI) into pre-construction planning and design is increasingly regarded as vital to effectively navigate the escalating complexity of construction projects, alongside the rising exigency for sustainability and operational efficiency. This dissertation investigates the professional perspectives regarding AI integration within the pre-construction sector, undertaking a comprehensive analysis of both quantitative and qualitative findings in conjunction with existing literature to formulate an initial framework for fundamental AI implementation. The objectives of the study are: (1) to evaluate the advantages and drawbacks of AI-driven methodologies in contrast to conventional approaches in feasibility analysis, design optimization, cost estimation, and bidding strategies from the standpoint of industry experts; (2) to assess the perceptions, levels of acceptance, and factors contributing to resistance among professionals concerning AI integration; (3) to deepen the comprehension of AI's role in fostering sustainable practices and smart city initiatives through the optimization of resource utilization; and (4) to delineate an initial framework that articulates the essential components for effective AI integration, encompassing data management, design processes, cost estimation, risk management, and sustainability considerations. A mixed-methods research design was employed, integrating quantitative surveys administered to 270 industry professionals with qualitative interviews conducted with 30 experts, complemented by an extensive literature review. The quantitative data were subjected to analysis through descriptive and inferential statistics to discern trends and performance indicators such as time efficiency, cost-effectiveness, accuracy in feasibility assessments, and sustainability impacts. The qualitative data were thematically analyzed to extract insights concerning perceived benefits, challenges, and practical experiences associated with AI integration. The literature review encompassed theoretical frameworks including Lewin's Change Management Model, the Technology Acceptance Model (TAM), Diffusion of Innovations Theory, Socio-Technical Systems Theory, and Systems Thinking, thereby providing a robust theoretical underpinning. The findings reveal that professionals acknowledge AI's capacity to materially enhance precision in feasibility analyses via predictive analytics and machine learning, thereby facilitating market trend forecasting and project viability evaluations. AI-powered design optimization tools facilitate a more efficient examination of design alternatives, culminating in innovative and sustainable outcomes. In the realms of cost estimation and bidding, AI augments accuracy and competitiveness by scrutinizing historical data alongside current market dynamics. Nonetheless, impediments such as substantial implementation costs, concerns regarding data privacy, skill deficiencies necessitating specialized training, and stakeholder resistance to change were identified. To mitigate these challenges, an initial framework for foundational AI integration is proposed, informed by professional insights and firmly grounded in theoretical models. The framework advocates for a phased implementation strategy commencing with pilot projects in low-risk environments and underscores the significance of a robust data infrastructure, interdisciplinary collaboration, stakeholder engagement, and ongoing monitoring and refinement. This framework presents a theoretically informed and practical roadmap for organizations aspiring to effectively incorporate AI into pre-construction practices. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 8435.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 4.25 MB | Adobe PDF | View/Open |
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