Artificial Intelligent Applications for Resource Allocation in Sri Lankan Construction Projects

Abstract

The construction industry in Sri Lanka faces persistent challenges in resource allocation, including inefficiencies, cost overruns, delays, and material wastage. These issues hinder project performance and efficiency, exacerbated by not successfully adopting advanced technologies such as Artificial Intelligence (AI). Globally, AI has proven its potential to optimize resource allocation through predictive analytics, automation, and data-driven decision-making. However, its application in Sri Lanka's construction sector remains underexplored. This study aims to evaluate the impact and effectiveness of AI in addressing resource allocation challenges within Sri Lankan construction projects. Using a qualitative methodology, data was gathered through case studies and semi-structured interviews with 10 professionals from five leading construction companies. The stratified sampling approach was used to select large construction companies that use advanced technologies. The findings mainly revealed critical resource allocation issues, such as inadequate planning, technological & competency gaps, and labor mismanagement, while highlighting the potential of AI in improving efficiency, cost savings, timeliness, and decision support. Barriers to AI adoption were also identified, including high implementation costs, lack of expertise, and resistance to change. The research emphasizes the need for strategic integration of AI through pilot projects, collaboration with technology providers, development of a centralized data management system, positive stakeholder engagement, gap analysis, and capacity building tailored to Sri Lanka's unique socio-economic and technological context. This study contributes to the growing discourse on AI in construction by providing insights into its benefits and challenges and offering practical recommendations for effective AI integration to enhance resource allocation practices and project performance in Sri Lanka’s construction industry