Digital Transformation of Urban Infrastructure with the Help of AI Guardians

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 16 | Issue : 01 | Page : 16 25
    By

    Dev Biswas,

  • Karthik Nagarajan,

  • Raju Narwade,

  1. PG student, Department of Civil Engineering, Pillai HOC College of Engineering and Technology, Rasayani, (affiliated to the University of Mumbai, maharastra, india
  2. Associate Professor, Department of Civil Engineering, Pillai HOC College of Engineering and Technology, Rasayani, (affiliated to the University of Mumbai, Maharashtra, India
  3. Associate Professor, Department of Civil Engineering, Pillai HOC College of Engineering and Technology, Rasayani, (affiliated to the University of Mumbai, Maharashtra, India

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: Artificial intelligence, building information modeling, convolutional neural networks, Internet of Things, natural language processing, quality control, unmanned aerial vehicles

[This article belongs to Recent Trends in Civil Engineering & Technology ]

How to cite this article:
Dev Biswas, Karthik Nagarajan, Raju Narwade. Digital Transformation of Urban Infrastructure with the Help of AI Guardians. Recent Trends in Civil Engineering & Technology. 2026; 16(01):16-25.
How to cite this URL:
Dev Biswas, Karthik Nagarajan, Raju Narwade. Digital Transformation of Urban Infrastructure with the Help of AI Guardians. Recent Trends in Civil Engineering & Technology. 2026; 16(01):16-25. Available from: https://journals.stmjournals.com/rtcet/article=2026/view=239120


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Regular Issue Subscription Original Research
Volume 16
Issue 01
Received 07/01/2026
Accepted 31/01/2026
Published 09/02/2026
Publication Time 33 Days
117

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