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<p>The construction industry continues to face persistent challenges, including project delays, cost overruns, safety risks, low productivity, and labor shortages, which are further exacerbated by limited digital integration. Over the period from 2015 to 2025, artificial intelligence (AI) emerged as a promising approach to address these issues, building on its successful application in sectors such as manufacturing and transportation. Core AI techniques, including machine learning, deep learning, computer vision, expert systems, robotics, optimization, and digital twins, have demonstrated increasing potential to support data-driven decision-making, operational efficiency, and safety improvement in construction projects. This study presents a systematic review of AI research in the construction domain over the past decade, analyzing key application areas across the project lifecycle, including design support, progress monitoring, risk and safety management, quality control, resource optimization, and operation and asset management. The review also identifies persistent challenges related to fragmented data, limited interoperability, high implementation costs, workforce skill gaps, and ethical and regulatory concerns. Based on the synthesized findings, future research directions are discussed, emphasizing scalable and interoperable AI solutions, human-centered adoption strategies, and integrated construction ecosystems. The results provide structured insights to support researchers, practitioners, and policymakers in advancing effective and responsible AI adoption in the construction industry.</p>

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A Decade of Artificial Intelligence in the Construction Industry: Trends, Applications, and Future Directions

  • 期刊论文

  • 发表日期：

    2026年4月11日

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  • Moh Nur Sholeh,
  • Jati Utomo Dwi Hatmoko,
  • Vemi Widoanindyawati

摘要

The construction industry continues to face persistent challenges, including project delays, cost overruns, safety risks, low productivity, and labor shortages, which are further exacerbated by limited digital integration. Over the period from 2015 to 2025, artificial intelligence (AI) emerged as a promising approach to address these issues, building on its successful application in sectors such as manufacturing and transportation. Core AI techniques, including machine learning, deep learning, computer vision, expert systems, robotics, optimization, and digital twins, have demonstrated increasing potential to support data-driven decision-making, operational efficiency, and safety improvement in construction projects. This study presents a systematic review of AI research in the construction domain over the past decade, analyzing key application areas across the project lifecycle, including design support, progress monitoring, risk and safety management, quality control, resource optimization, and operation and asset management. The review also identifies persistent challenges related to fragmented data, limited interoperability, high implementation costs, workforce skill gaps, and ethical and regulatory concerns. Based on the synthesized findings, future research directions are discussed, emphasizing scalable and interoperable AI solutions, human-centered adoption strategies, and integrated construction ecosystems. The results provide structured insights to support researchers, practitioners, and policymakers in advancing effective and responsible AI adoption in the construction industry.