Applying Artificial Intelligence and the Internet of Things to the Building Sector to Improve Security and Disaster Prediction

The Intelligent Infrastructure Monitoring System (IIMS) is a data-driven approach that uses Artificial Intelligence (AI) and the Internet of Things (IoT) to enhance civil engineering Disaster Management (DM). To predict future disasters, the system uses a Multi-Tiered Model (MTM) to integrate real-time data from IoT sensors, such as stress, vibration, temperature, humidity, and corrosion levels. The Temporal Graph Convolutional Network Model (TGCNM) processes this data to capture spatial and temporal dependencies across structural components, enabling proactive maintenance and risk mitigation. The TGCNM outperforms baseline models by a significant margin, and hyperparameter sensitivity analysis identifies the optimal configuration for enhanced performance. This data-driven approach is vital for monitoring and securing key infrastructure and enhancing civil engineering DM with AI and IoT.

Authors

Institutions

Publication Details

Journal
Journal of Machine and Computing
Published
2025-04-05
DOI
https://doi.org/10.53759/7669/jmc202505087
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Applying Artificial Intelligence and the Internet of Things to the Building Sector to Improve Security and Disaster Prediction

Sampathirao Suneetha, N Nanthini, Swati Saxena, V. Sabaresan et al.
Journal of Machine and Computing
Traffic Prediction and Management Techniques
article

Applying Artificial Intelligence and the Internet of Things to the Building Sector to Improve Security and Disaster Prediction

Sampathirao Suneetha, N Nanthini, Swati Saxena, V. Sabaresan, S.B. Prakalya, A. T.
article en

Abstract

The Intelligent Infrastructure Monitoring System (IIMS) is a data-driven approach that uses Artificial Intelligence (AI) and the Internet of Things (IoT) to enhance civil engineering Disaster Management (DM). To predict future disasters, the system uses a Multi-Tiered Model (MTM) to integrate real-time data from IoT sensors, such as stress, vibration, temperature, humidity, and corrosion levels. The Temporal Graph Convolutional Network Model (TGCNM) processes this data to capture spatial and temporal dependencies across structural components, enabling proactive maintenance and risk mitigation. The TGCNM outperforms baseline models by a significant margin, and hyperparameter sensitivity analysis identifies the optimal configuration for enhanced performance. This data-driven approach is vital for monitoring and securing key infrastructure and enhancing civil engineering DM with AI and IoT.

Journal of Machine and Computing
St. Joseph's Institute of Technology (IN), Sathyabama Institute of Science and Technology (IN), Saveetha University (IN), Koneru Lakshmaiah Education Foundation (IN)
Openalex Percentile: Top 94%
Traffic Prediction and Management Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Applying Artificial Intelligence and the Internet of Things to the Building Sector to Improve Security and Disaster Prediction — Sampathirao Suneetha, N Nanthini, et al. · Journal of Machine and Computing (2025) | TGRS Research Map | TGRS