STM Journals Peer-reviewed research from Consortium e-Learning Network

Recent Trends in Civil Engineering & Technology Original Research

Digital Transformation of Urban Infrastructure with the Help of AI Guardians

  1. Dev Biswas Department of Civil Engineering, Pillai HOC College of Engineering and Technology, Rasayani, (affiliated to the University of Mumbai
  2. Karthik Nagarajan Department of Civil Engineering, Pillai HOC College of Engineering and Technology, Rasayani, (affiliated to the University of Mumbai
  3. Raju Narwade Department of Civil Engineering, Pillai HOC College of Engineering and Technology, Rasayani, (affiliated to the University of Mumbai

Abstract

The construction industry continues to face challenges related to quality control, safety protocols, and meeting project deadlines. These issues often result in significant cost overruns and project delays. Traditional inspection and site management approaches rely heavily on manual work and individual judgment. As a result, human errors can easily occur, and these methods provide only limited snapshots of site conditions over time. This paper presents a comprehensive framework that uses artificial intelligence to transform quality assurance and monitoring in construction. It shifts from reactive, occasional inspections to continuous and proactive supervision. The framework integrates computer vision tools that process images from multiple sources. Fixed cameras provide consistent site views, worker-worn sensors capture close-range details, and drones supply overhead perspectives. Deep learning models form the core of the system. Convolutional neural networks are trained to automatically detect anomalies, identify mismatches with building information models, and recognize safety violations and quality defects in real time. These include issues such as improper rebar placement, inadequate concrete surface preparation, and incorrect use of personal protective equipment. Natural language processing is also used to analyze daily logs and incident reports for indicators of future risks. Predictive tools utilize historical project data together with current performance metrics to forecast potential quality problems and schedule delays. This enables timely interventions before major issues arise.

Keywords

References (10)

  1. Elmousalami H, Maxy M, Hui FKP, Aye L. AI in automated sustainable construction engineering management. Automation in Construction. 2025;175:106202. doi:10.1016/j.autcon.2025.106202
  2. AI-Driven Quality Control in Manufacturing and Construction: Enhancing Precision and Reducing Human Error. Applied IT & Engineering. 2025;3(1):1-10. doi:10.25163/engineering.3110270
  3. Zadeh SS, Birgani SA, Khorshidi M, Kooban F. Concrete surface crack detection with convolutional-based deep learning models [Preprint]. 2024. arXiv:2401.07124. doi:10.48550/ 2401.07124.
  4. Tarun Parmar. Artificial Intelligence in High-tech Manufacturing: A Review of Applications in Quality Control and Process Optimization. International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences. 2022;10(6). doi:10.37082/ijirmps.v10.i6.231961
  5. Swarna RA, Hossain MM, Khatun MR, Rahman MM, Munir A. Concrete Crack Detection and Segregation: A Feature Fusion, Crack Isolation, and Explainable AI-Based Approach. Journal of Imaging. 2024;10(9):215. doi:10.3390/jimaging10090215
  6. Gupta H, Goyal N, Choudhary V. Concrete surface crack detection with convolutional neural network. Iconic Res Eng J. 2022;6(6):199–203. ISSN: 2456-8880.
  7. Rajadurai RS, Kang ST. Automated Vision-Based Crack Detection on Concrete Surfaces Using Deep Learning. Applied Sciences. 2021;11(11):5229. doi:10.3390/app11115229
  8. Ai D, Jiang G, Lam SK, He P, Li C. Computer vision framework for crack detection of civil infrastructure—A review. Engineering Applications of Artificial Intelligence. 2023;117:105478. doi:10.1016/j.engappai.2022.105478
  9. Li S, Zhao X, Zhou G. Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network. Comput Aided Civ Infrastruct Eng. 2019;34(7):616–634. doi:10.1111/ 12433.
  10. Cha YJ, Choi W, Büyüköztürk O. Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks. Computer-Aided Civil and Infrastructure Engineering. 2017;32(5):361-378. doi:10.1111/mice.12263
Support