ADOPTION OF ARTIFICIAL INTELLIGENCE (AI) AND MACHINE LEARNING (ML) IN PREDICTING CONSTRUCTION PROJECT OUTCOMES

<p dir="ltr"><a href="" target="_blank"><i>Problems with the mismanagement of resources, shortage of workers, productivity limitations, and frequent overruns relative to schedule and cost plague the construction industry</i></a><i>. Artificial intelligence (AI) and machine learning (ML) are emerging technologies that can forecast and optimize project outcomes thus offering enormous benefits to owners, contractors, and project managers. Construction firms are acknowledging the potential of AI and ML to enhance productivity, reduce costs, and improve project outcomes.</i><i> This paper analyses the adoption of AI and ML in the Nigerian construction industry for the prediction and improvement of project outcomes. It also discusses important issues, possible solutions, and the revolutionary potential of these technologies to boost project management and productivity. A comprehensive literature analysis was conducted to determine the applicability of AI and ML in the construction sector. A survey of professionals in the built environment revealed that integrating AI and ML into building processes presents several obstacles to broad adoption, such as a lack of technical know-how, expensive upfront costs, and restricted data availability and quality. integration complexity, algorithmic bias, building sites' unpredictability, and legal and regulatory obstacles. The study offers specific solutions, such as enhancing data integration skills, creating industry-wide training initiatives to increase AI/ML proficiency, creating standardized data formats and processes to expedite AI integration, and encouraging cooperation amongst technology suppliers. The results show how AI and ML have the potential to completely transform the construction industry,</i><i> but addressing these issues should be approached with coordination among different stakeholders. Adoption at a fast rate will facilitate tremendous gains not only in terms of productivity and sustainability but also in project outcomes.</i></p>