DEEP LEARNING MODELS FOR REAL-TIME ANOMALY DETECTION IN BRIDGE MONITORING IOT NETWORKS

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.