Leveraging Artificial Intelligence for optimized project management and risk mitigation in construction industry
Department of Construction Management, School of Built Environment, Bowling Green State University, USA.
Review Article
World Journal of Advanced Research and Reviews, 2024, 24(03), 2924-2940
Article DOI: 10.30574/wjarr.2024.24.3.4026
Publication history:
Received on 16 November 2024; revised on 28 December 2024; accepted on 30 December 2024
Abstract:
The construction industry is increasingly adopting artificial intelligence (AI) to optimize project management processes and enhance risk mitigation strategies. As construction projects grow in complexity, with tight deadlines, evolving regulations, and high costs, the traditional approaches to managing projects and assessing risks are often inefficient and prone to human error. AI technologies, particularly machine learning and predictive analytics, offer powerful tools to address these challenges by providing data-driven insights, improving decision-making, and automating various project management tasks. By analysing historical data and real-time project information, AI can predict potential risks, such as delays, cost overruns, and safety hazards, enabling proactive interventions. AI-powered tools help streamline project scheduling, resource allocation, and performance tracking, ensuring that projects stay on track and within budget. Additionally, AI can optimize supply chain management, reducing material waste and ensuring the timely availability of resources. Machine learning algorithms can continuously learn from project data, improving their predictive accuracy over time and adapting to changing conditions. This paper explores the role of AI in transforming construction project management and risk mitigation strategies. It examines the specific AI applications in areas such as risk assessment, safety management, cost estimation, and scheduling optimization. Case studies and examples from the construction industry highlight the successful implementation of AI tools in real-world projects, demonstrating tangible improvements in project outcomes. The paper also addresses the barriers to AI adoption in construction, including data quality, integration challenges, and the need for specialized skills. Ultimately, the integration of AI into construction project management holds the potential to create more efficient, cost-effective, and risk-resilient projects.
Keywords:
Artificial Intelligence; Project Management; Risk Mitigation; Construction Industry; Machine Learning; Predictive Analytics
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0