Urban sentinel: advancing structural health monitoring for building damage measurement in districts through IoT integration and self-optimizing machine learning

Abstract In the contemporary urban landscape, ensuring the structural health and resilience of buildings and infrastructure is paramount for sustainable development and the well-being of citizens. This paper proposes a novel approach, termed Urban Sentinel, aimed at revolutionizing urban infrastructure management through the integration of Internet of Things (IoT) sensor networks and regression AI systems. This integration is still in its early stages of practical application, marking Urban Sentinel as a significant step forward in urban infrastructure management. Urban Sentinel encompasses a comprehensive system architecture designed to monitor and predict the health of buildings and infrastructure in cities or any other integrated district. Central to this architecture is the deployment of a proposed sensor set, strategically installed within buildings to capture critical data related to structural integrity, environmental conditions, and operational performance. These sensors transmit data using LoRaWAN wireless technology to a centralized management system, where a regression AI model harnesses the power of machine learning algorithms to analyze the data and predict the health status of the buildings. This system offers several advantages over traditional monitoring methods. By leveraging IoT technology, Urban Sentinel enables real-time data collection, allowing for the timely detection of anomalies and potential risks. The integration of regression AI systems enhances the predictive capabilities of the management system, enabling proactive maintenance and optimization of urban infrastructure. Additionally, this paper thoroughly addresses potential challenges and offers corresponding solutions to mitigate them effectively. By embracing innovative technologies and holistic approaches to infrastructure management, Urban Sentinel paves the way for smarter and more resilient cities of the future.

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Publication Details

Journal
Journal of Electrical Systems and Information Technology
Published
2025-07-30
DOI
https://doi.org/10.1186/s43067-025-00237-6
Primary Topic
Infrastructure Maintenance and Monitoring
Type
article
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article

Urban sentinel: advancing structural health monitoring for building damage measurement in districts through IoT integration and self-optimizing machine learning

Parsa Parsafar
Journal of Electrical Systems and Information Technology
Infrastructure Maintenance and Monitoring
article

Urban sentinel: advancing structural health monitoring for building damage measurement in districts through IoT integration and self-optimizing machine learning

Parsa Parsafar
article en

Abstract

Abstract In the contemporary urban landscape, ensuring the structural health and resilience of buildings and infrastructure is paramount for sustainable development and the well-being of citizens. This paper proposes a novel approach, termed Urban Sentinel, aimed at revolutionizing urban infrastructure management through the integration of Internet of Things (IoT) sensor networks and regression AI systems. This integration is still in its early stages of practical application, marking Urban Sentinel as a significant step forward in urban infrastructure management. Urban Sentinel encompasses a comprehensive system architecture designed to monitor and predict the health of buildings and infrastructure in cities or any other integrated district. Central to this architecture is the deployment of a proposed sensor set, strategically installed within buildings to capture critical data related to structural integrity, environmental conditions, and operational performance. These sensors transmit data using LoRaWAN wireless technology to a centralized management system, where a regression AI model harnesses the power of machine learning algorithms to analyze the data and predict the health status of the buildings. This system offers several advantages over traditional monitoring methods. By leveraging IoT technology, Urban Sentinel enables real-time data collection, allowing for the timely detection of anomalies and potential risks. The integration of regression AI systems enhances the predictive capabilities of the management system, enabling proactive maintenance and optimization of urban infrastructure. Additionally, this paper thoroughly addresses potential challenges and offers corresponding solutions to mitigate them effectively. By embracing innovative technologies and holistic approaches to infrastructure management, Urban Sentinel paves the way for smarter and more resilient cities of the future.

Journal of Electrical Systems and Information TechnologyVol. 12(1)
University of Mazandaran (IR)
Openalex Percentile: Top 80%
Infrastructure Maintenance and Monitoring
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