Smartphone-Based Structural Health Monitoring with Neural Network Regression for Damage & Detection

This paper presents a novel and cost-effective approach for structural health monitoring using smartphones. By using built-in accelerometers, smartphones can collect data on building motion, facilitating the detection of potential damage. Traditional methods often rely on classification techniques, requiring extensive training data encompassing both damaged and undamaged scenarios. However, this proves impractical for smartphones due to their limited computational resources for complex classification tasks. We propose a paradigm shift, transforming the classification problem into a regression problem. This enables robust structural health assessment using a neural network specifically designed for this purpose: the echo state network (ESN). ESNs offer inherent robustness to noise and perturbations, making them ideal for real-world applications with sensor data. Compared to traditional methods, the proposed smartphone-based system offers significant advantages in terms of cost-effectiveness, user-friendliness, and computational efficiency. The effectiveness of the proposed method is evaluated through several experiments, demonstrating its capability in identifying structural damage.