Machine Learning for Promoting Green Building Practices: Implications for Construction Project Delivery in Malaysia

Abstract

The construction industry in Malaysia is undergoing a transformative shift as digital technologies, aligned with Industrial Revolution 4.0 (IR 4.0), are increasingly adopted to enhance sustainability, efficiency, and project delivery. Among these technologies, machine learning (ML), a branch of artificial intelligence, has garnered growing interest due to its potential to support green building practices. However, despite its benefits, the practical implementation of ML within the Malaysian construction sector remains limited. This study examines the impact of machine learning on promoting green building practices and assesses its implications for the delivery of construction projects. A quantitative approach was adopted, with data collected from 125 construction professionals, including contractors, quantity surveyors, engineers, and architects. The analysis employed descriptive statistics, the Relative Importance Index (RII), and the perceived effectiveness of ML applications. The findings highlight the positive impact of ML on key sustainability outcomes, including energy efficiency optimization, sustainable material use, and project planning. Nonetheless, several barriers impede its widespread adoption, including high initial investment costs, inadequate infrastructure, limited stakeholder awareness, and resistance to technological change. The study also identifies future opportunities for ML integration in areas such as predictive analytics for cost and cash flow management, real-time risk mitigation, and enhanced decision support systems. This research contributes to the growing body of knowledge on digital transformation in construction by offering empirical evidence on the benefits and challenges of ML adoption in green building initiatives. The study highlights the strategic value of ML in enhancing sustainable project delivery and offers actionable insights for policymakers, industry stakeholders, and researchers seeking to advance green construction through intelligent technologies.