Deep Learning–Based Structural Health Assessment of Concrete Structures Using IoT Sensor Networks

Abstract

Concrete structures have also been faced with aging materials, variable operating loads, and extreme environmental exposure, leading to gradual deterioration and possible serviceability issues. Conventional structural health monitoring (SHM) methods based on inspection are primarily reactive and not very suitable for continuous damage evaluation. Emerging technologies such as IoT-based sensing and deep learning have provided novel possibilities for intelligent real-time monitoring of concrete infrastructure. We introduce a real-time SHM framework that combines IoT sensor networks with deep learning techniques. In incremental loading, strain sensors, accelerometers, crack width sensors, and environmental monitoring devices were connected to reinforced concrete beam specimens to obtain synchronized multi-source time-series data. Sensor data were processed and sent to a cloud-based system for signal conditioning and analysis. An intelligent deep learning hybrid model built utilizing Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) units focused on spatial features of sensors and temporal patterns of damage was proposed. The model was compared with classical ML algorithms (Support Vector Machines, Random Forests etc.) for model performance. The proposed CNN–LSTM model exhibited an above 95% accuracy for classification of damage and superior sensitivity to early-stage damage as compared to conventional methods. The framework held up to different loadings, which indicates it had a high degree of reproducibility and adaptivity. The results show that the proposed IoT-driven deep learning framework for continuous and intelligent monitoring of concrete structures can be quite efficient. Such an approach is ideal for early damage detection and predictive maintenance, therefore making it applicable and adaptable for deployment in smart infrastructure and resilient urban systems.