DEEP LEARNING MODELS FOR REAL-TIME ANOMALY DETECTION IN BRIDGE MONITORING IOT NETWORKS
Keywords:
Deep learning, Anomaly detection, Bridge monitoring, IoT networks, Structural health monitoring, Predictive maintenance, Sensor networks, Infrastructure safety, Real-time processing, Edge computingAbstract
Bridge infrastructure represents critical components of transportation networks worldwide, with structural failures posing significant risks to public safety and economic stability. This research presents a comprehensive deep learning framework designed for real-time anomaly detection in bridge monitoring Internet of Things (IoT) networks, addressing the critical need for early identification of structural deterioration and potential failure modes. The proposed system integrates multiple deep learning architectures including Convolutional Neural Networks (CNNs) for spatial pattern recognition through frequency domain feature extraction and distributed damage pattern detection, Long Short-Term Memory (LSTM) networks for temporal sequence analysis with bidirectional processing and attention mechanisms, and Autoencoder models for unsupervised anomaly detection with bottleneck architecture design across heterogeneous sensor data streams. Through extensive empirical evaluation conducted on 47 bridge monitoring networks encompassing 12,847 heterogeneous IoT sensors and 8.3 million data points collected over 36 months of continuous monitoring, our findings demonstrate exceptional performance in anomaly detection with 94.7% overall accuracy, 89.2% precision, and 91.8% recall rates across diverse structural conditions. The framework achieved remarkable improvements in early warning capabilities with average detection times of 2.8 hours before critical threshold violations and false positive rates reduced to 3.4% compared to traditional rule-based monitoring systems. Additionally, the system demonstrated robust performance under challenging environmental conditions with 87.6% accuracy during extreme weather events and adaptive learning capabilities that improved detection performance by 12.3% over the deployment period through continuous model optimization. The real-time processing capabilities enable continuous monitoring with average response times of 1.7 seconds per sensor reading, scalable edge-cloud architecture supporting up to 50,000 concurrent sensor streams, and 73% bandwidth reduction through intelligent data compression. These results establish deep learning approaches as highly effective solutions for next-generation structural health monitoring systems, contributing significantly to infrastructure safety and predictive maintenance strategies.References
[1] Abshari S. Road Construction, Transportation, and Bridge Construction: Building the Pathways of Modern Civilization. International Journal of Sustainable Applied Science and Engineering, 2024, 1(1): 103-110.
[2] Seibel W. Intended Ignorance: The Collapse of the I-35 W Mississippi River Bridge on 1 August 2007. Collapsing Structures and Public Mismanagement. Cham: Springer International Publishing, 2022: 55-86.
[3] Ogunnowo E O, Adewoyin M A, Fiemotongha J E, et al. Systematic Review of Non-Destructive Testing Methods for Predictive Failure Analysis in Mechanical Systems. IRE Journals, 2020, 4(4): 207-215.
[4] Selvaprasanth P, Malathy R. Revolutionizing Structural Health Monitoring in Marine Environment with Internet of Things: A Comprehensive Review. Innovative Infrastructure Solutions, 2025, 10(2): 62.
[5] Ur Rehman M, Jamshed M A, Kalsoom T. Advances in Multi-modal Intelligent Sensing. Multimodal Intelligent Sensing in Modern Applications, 2024: 1-28.
[6] Islam R U, Hossain M S, Andersson K. A Deep Learning Inspired Belief Rule-based Expert System. IEEE Access, 2020(8): 190637-190651.
[7] Zhu A, Wu Q, Cui R, et al. Exploring a Rich Spatial–Temporal Dependent Relational Model for Skeleton-based Action Recognition by Bidirectional LSTM-CNN. Neurocomputing, 2020(414): 90-100.
[8] Katam R, Pasupuleti V D K, Kalapatapu P. Machine Learning-driven Structural Health Monitoring: STFT-based Feature Extraction for Damage Detection. Structures, 2025(78): 109244.
[9] Sabouni A R. Advances in Reinforced Concrete Integrity and Failure. Advances in Structural Integrity and Failure. IntechOpen, 2023. DOI: 10.5772/intechopen.1002247.
[10] Plevris V, Papazafeiropoulos G. AI in Structural Health Monitoring for Infrastructure Maintenance and Safety. Infrastructures, 2024, 9(12): 225.
[11] Wang F, Zhang M, Wang X, et al. Deep Learning for Edge Computing Applications: A State-of-the-Art Survey. IEEE Access, 2020(8): 58322-58336.
[12] Malekloo A, Ozer E, AlHamaydeh M, et al. Machine Learning and Structural Health Monitoring Overview with Emerging Technology and High-Dimensional Data Source Highlights. Structural Health Monitoring, 2022, 21(4): 1906-1955.
[13] Russo S A. Robust Anomaly Detection for Time Series Data in Sensor-based Critical Systems. PhD Thesis, Università degli Studi di Trieste, Italy. 2025.
[14] Borah S S, Khanal A, Sundaravadivel P. Emerging Technologies for Automation in Environmental Sensing. Applied Sciences, 2024, 14(8): 3531.
[15] Khan M A M, Kee S H, Pathan A S K, et al. Image Processing Techniques for Concrete Crack Detection: A Scientometrics Literature Review. Remote Sensing, 2023, 15(9): 2400.
[16] Taye M M. Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions. Computers, 2023, 12(5): 91.
[17] Jia J, Li Y. Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends. Sensors, 2023, 23(21): 8824.
[18] Li H, Lou J, Li F, et al. A Deep Learning Framework for Deformation Monitoring of Hydraulic Structures with Long-Sequence Hydrostatic and Thermal Time Series. Water, 2025, 17(12): 1814.
[19] Mao J, Wang H, Spencer B F Jr. Toward Data Anomaly Detection for Automated Structural Health Monitoring: Exploiting Generative Adversarial Nets and Autoencoders. Structural Health Monitoring, 2021, 20(4): 1609-1626.
[20] Eltouny K, Gomaa M, Liang X. Unsupervised Learning Methods for Data-Driven Vibration-Based Structural Health Monitoring: A Review. Sensors, 2023, 23(6): 3290.
[21] Abdulkarem M, Samsudin K, Rokhani F Z, et al. Wireless Sensor Network for Structural Health Monitoring: A Contemporary Review of Technologies, Challenges, and Future Direction. Structural Health Monitoring, 2020, 19(3): 693-735.
[22] Abdulhussain S H, Mahmmod B M, Alwhelat A, et al. A Comprehensive Review of Sensor Technologies in IoT: Technical Aspects, Challenges, and Future Directions. Computers, 2025, 14(8): 342.
[23] Peng Z, Li J, Hao H, et al. Smart Structural Health Monitoring Using Computer Vision and Edge Computing. Engineering Structures, 2024(319): 118809.
[24] Bablu T A, Rashid M T. Edge Computing and Its Impact on Real-Time Data Processing for IoT-Driven Applications. Journal of Advanced Computing Systems, 2025, 5(1): 26-43.
[25] Wang Y, Yu Z, Wu J, et al. Adaptive Knowledge Distillation-Based Lightweight Intelligent Fault Diagnosis Framework in IoT Edge Computing. IEEE Internet of Things Journal, 2024, 11(13): 23156-23169.
[26] Ni J, Tang H, Haque S T, et al. A Survey on Multimodal Wearable Sensor-Based Human Action Recognition. arXiv preprint, 2024. https://arxiv.org/html/2404.15349v1.
[27] Wu R T, Jahanshahi M R. Data Fusion Approaches for Structural Health Monitoring and System Identification: Past, Present, and Future. Structural Health Monitoring, 2020, 19(2): 552-586.
[28] Broer A A, Benedictus R, Zarouchas D. The Need for Multi-Sensor Data Fusion in Structural Health Monitoring of Composite Aircraft Structures. Aerospace, 2022, 9(4): 183.
[29] Jayasinghe S, Mahmoodian M, Alavi A, et al. Application of Machine Learning for Real-Time Structural Integrity Assessment of Bridges. CivilEng, 2025, 6(1): 2.
[30] Azimi M, Eslamlou A D, Pekcan G. Data-Driven Structural Health Monitoring and Damage Detection Through Deep Learning: State-of-the-Art Review. Sensors, 2020, 20(10): 2778.
[31] Mehedi S T, Anwar A, Rahman Z, et al. Dependable Intrusion Detection System for IoT: A Deep Transfer Learning Based Approach. IEEE Transactions on Industrial Informatics, 2022, 19(1): 1006-1017.