In the rapidly evolving landscape of construction technology, a groundbreaking study has emerged that could reshape how defects are identified and managed in the Architecture, Engineering, and Construction (AEC) sector. Led by Pradnya Desai from the Civil Engineering Department at Symbiosis Institute of Technology, Symbiosis International University in Pune, India, the research delves into the transformative potential of Artificial Intelligence (AI) in Construction Defect Identification Systems (CDIS). Published in the ‘Journal of Studies in Science and Engineering’—which translates to ‘Journal of Research in Science and Engineering’—this study offers a compelling look at how AI can enhance efficiency, safety, and decision-making in construction projects.
The study, which systematically reviewed publications between 2020 and 2024, highlights that AI-driven methods such as pre-trained Convolutional Neural Networks, laser vision, and Operational Modal Analysis significantly outperform traditional methods like grey correlation analysis and visual inspection. “AI-driven methods consistently show a 10–20% higher accuracy in defect detection compared to conventional approaches,” Desai explains. This leap in accuracy is not just a technological marvel but a game-changer for the industry, particularly in the energy sector where construction quality directly impacts operational efficiency and safety.
The commercial implications are substantial. For instance, in the energy sector, where infrastructure projects are often massive and complex, the ability to predict and manage defects proactively can lead to significant cost savings and improved project timelines. “The integration of AI in defect management can establish a proactive approach, enhancing quality standards and promoting sustainable practices,” Desai notes. This shift from reactive to proactive defect management could revolutionize how construction projects are executed, ensuring higher quality and durability of structures.
However, the study also identifies barriers to widespread adoption. While AI excels in identifying and classifying defects, its application in predictive and real-time defect assessment remains fragmented. Desai emphasizes the need for integrated, data-driven frameworks that leverage AI’s predictive capabilities. “The future lies in developing comprehensive systems that can not only detect defects but also predict them before they occur,” she says. This forward-thinking approach could set new benchmarks for quality and safety in the AEC industry.
As the construction sector continues to embrace digital transformation, the findings of this study provide a roadmap for leveraging AI to its fullest potential. The research underscores the importance of investing in AI technologies and fostering collaboration between academia and industry to drive innovation. With the insights gained from this study, the AEC sector is poised to enter a new era of efficiency, safety, and sustainability, ultimately benefiting industries like energy that rely on robust and defect-free infrastructure.