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 infrastruct...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-07-01
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| Series: | Journal of Electrical Systems and Information Technology |
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| Online Access: | https://doi.org/10.1186/s43067-025-00237-6 |
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| author | Parsa Parsafar |
| author_facet | Parsa Parsafar |
| author_sort | Parsa Parsafar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-30251ca6297d4fa68c17191eb640dab7 |
| institution | DOAJ |
| issn | 2314-7172 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Electrical Systems and Information Technology |
| spelling | doaj-art-30251ca6297d4fa68c17191eb640dab72025-08-20T03:04:22ZengSpringerOpenJournal of Electrical Systems and Information Technology2314-71722025-07-0112112010.1186/s43067-025-00237-6Urban sentinel: advancing structural health monitoring for building damage measurement in districts through IoT integration and self-optimizing machine learningParsa Parsafar0Department of Computer and Electrical Engineering, University of MazandaranAbstract 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.https://doi.org/10.1186/s43067-025-00237-6Tructural health monitoringInternet of thingsIntegrated web applicationPolynomial regressionSelf-optimizing AI system |
| spellingShingle | Parsa Parsafar Urban sentinel: advancing structural health monitoring for building damage measurement in districts through IoT integration and self-optimizing machine learning Journal of Electrical Systems and Information Technology Tructural health monitoring Internet of things Integrated web application Polynomial regression Self-optimizing AI system |
| title | Urban sentinel: advancing structural health monitoring for building damage measurement in districts through IoT integration and self-optimizing machine learning |
| title_full | Urban sentinel: advancing structural health monitoring for building damage measurement in districts through IoT integration and self-optimizing machine learning |
| title_fullStr | Urban sentinel: advancing structural health monitoring for building damage measurement in districts through IoT integration and self-optimizing machine learning |
| title_full_unstemmed | Urban sentinel: advancing structural health monitoring for building damage measurement in districts through IoT integration and self-optimizing machine learning |
| title_short | Urban sentinel: advancing structural health monitoring for building damage measurement in districts through IoT integration and self-optimizing machine learning |
| title_sort | urban sentinel advancing structural health monitoring for building damage measurement in districts through iot integration and self optimizing machine learning |
| topic | Tructural health monitoring Internet of things Integrated web application Polynomial regression Self-optimizing AI system |
| url | https://doi.org/10.1186/s43067-025-00237-6 |
| work_keys_str_mv | AT parsaparsafar urbansentineladvancingstructuralhealthmonitoringforbuildingdamagemeasurementindistrictsthroughiotintegrationandselfoptimizingmachinelearning |