Factors for artificial intelligence and machine learning applications in the project lifecycle of the construction industry in Klang Valley Malaysia

Chong, Chee Hao (2026) Factors for artificial intelligence and machine learning applications in the project lifecycle of the construction industry in Klang Valley Malaysia. Master dissertation/thesis, UTAR.

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Abstract

Malaysia government’s Ministry of Work through, Construction Industry Development Board (CIDB) has implemented Construction 4.0 Strategic Plan (2021 – 2025) which is a blueprint for Malaysian construction industry to adapt Industrial Revolution 4.0 (IR 4.0) to overcome the schedule, cost overrun and productivity constraints. Although the plan was introduced since 2016, compared with other sector particularly manufacturing sector, construction industry lags very much behind in digitization in every phase of construction project lifecycle especially application of Artificial intelligence (AI) and machine learning (ML). Therefore, in this research paper aims to investigates the factors influencing AI and ML adoption in the construction project lifecycle in Klang Valley and evaluates their potential benefits in improving project lifecycle success. A quantitative approach was employed using questionnaire surveys based on Technology-Organisation-Environment (TOE) framework. The respondents are from all levels of construction practitioners which are registered with CIDB Malaysia. The data are analysed through mean ranking analysis (descriptive statistics) and factor analysis (Principal Component Analysis (PCA)) in IBM SPSS 27. The results indicate that technological factors, such as relative advantage, trialability, and perceived usefulness, are the primary drivers of adoption. Organisational and ecosystem supports act as enabling conditions but remain underdeveloped, while market-related factors have minimal influence, suggesting an early stage of digital transformation. In terms of benefits, AI and ML are mainly associated with improvements in time, cost, and quality performance, reflecting a productivity-driven adoption pattern. However, safety and environmental performance are less prioritised. The study further reveals that all benefits collectively contribute to overall project lifecycle success, highlighting a gap between short-term priorities and long-term value. A conceptual framework is proposed to support more holistic and sustainable adoption in the construction industry.

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