Intelligent structural health monitoring using convolutional neural networks and IoT sensor fusion

Abstract

Structural health monitoring (SHM) is essential for ensuring the safety and longevity of critical civil infrastructure such as bridges, buildings, and dams. Traditional SHM approaches rely heavily on manual inspection and threshold-based alarm systems, which are prone to high false alarm rates and limited sensitivity to incipient damage. This paper proposes an intelligent SHM framework that integrates one-dimensional Convolutional Neural Networks (1D-CNN) with Long Short-Term Memory (LSTM) networks for automated damage detection from vibration sensor data acquired through Internet of Things (IoT) sensor networks. The proposed architecture employs a dual-branch feature extraction strategy: the 1D-CNN branch captures local spatial patterns from raw acceleration signals, while the LSTM branch models temporal dependencies across sequential sensor readings. An attention-based sensor fusion module aggregates information from heterogeneous sensor types including accelerometers, strain gauges, and temperature sensors, enabling comprehensive structural state assessment. Transfer learning is applied to adapt models pre-trained on large-scale simulated datasets to real-world bridge monitoring scenarios. Extensive experiments on the LANL structural damage detection dataset, the Z24 bridge benchmark, and a custom simulated structural dataset demonstrate that the proposed method achieves damage detection accuracy of 96.5%, significantly outperforming conventional machine learning baselines including SVM (84.1%), Random Forest (85.5%), and standard MLP (87.2%), while maintaining a false alarm rate below 3.2%. Ablation studies confirm the contribution of each architectural component to the overall performance.