article · International Journal of Advanced Multidisciplinary Research and Studies
Construction projects frequently encounter delays, complex workflows, and communication hurdles. Implementing artificial intelligence techniques, notably machine learning, predictive analytics, and automated data processing, offers practical solutions to streamline core operations. These technologies assist in refining project scheduling, improving risk management, and enhancing planning precision. Deploying such solutions relies on strategies including comprehensive data collection, targeted algorithmic modelling, and cloud computing infrastructure. Realised case studies demonstrate that integrating artificial intelligence yields clear operational gains, notably higher project efficiency, reduced overall expenditure, and enhanced workplace safety. Nevertheless, organisations looking to introduce these digital tools must navigate substantial barriers, particularly concerns surrounding data security and the readiness of the workforce to adopt new systems. Embracing these advanced computational methods enables the broader building sector to mitigate typical disruptions and achieve consistently stronger project outcomes.
Construction projects often run over budget and suffer from major timeline disruptions. Showing how artificial intelligence and predictive data tools can enhance planning, risk evaluation, and workplace safety provides industry practitioners with practical ways to reduce operational costs and improve project delivery reliably across the built environment.
Identified applications target construction managers, planners, and safety coordinators through predictive machine learning models and cloud computing. Supported by case studies demonstrating cost savings and safety gains, the underlying approaches appear applied and tested in practical settings. Near-term commercial deployment depends on addressing specific operational barriers, particularly data security safeguards and workforce resistance to adopting automated digital workflows.
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The construction industry faces challenges such as project complexity, delays, and communication issues. Leveraging AI, particularly through data analysis, predictive analytics, and machine learning, addresses these challenges by optimizing project planning, scheduling, and risk management. This paper outlines strategies for AI integration, including data collection, machine learning algorithms, and cloud computing. Case studies highlight successful implementations, showcasing benefits such as increased efficiency, cost savings, and improved safety. However, challenges like data security and workforce acceptance must be considered. The abstract concludes by discussing future trends and encouraging the construction industry to embrace AI for enhanced project outcomes.
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DOI: 10.62225/2583049x.2024.4.2.2550
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