Data-Driven Structural Health Monitoring Through Echo State Network Regression

This paper presents a novel data-driven approach to structural health monitoring (SHM) that uses Echo State Network (ESN) regression for continuous damage assessment. In contrast to traditional classification methods that demand extensive labeled data on damaged states, our approach utilizes an ESN, a powerful recurrent neural network, to directly predict a continuous damage metric from sensor data. This regression-based methodology offers two key advantages relevant to data science applications in SHM: (1) Reduced Training Data Dependency: The ESN achieves high accuracy even with limited data on damaged structures, significantly alleviating the data acquisition burden compared to classification-based AI/ML techniques. (2) Enhanced Noise Resilience: The inherent reservoir computing property of ESNs, characterized by a fixed, high-dimensional recurrent layer, makes them more tolerant of sensor noise and environmental variations compared to classification methods, leading to more reliable and robust SHM predictions from noisy data. A comprehensive evaluation demonstrates the effectiveness of the proposed ESN in identifying structural damage, highlighting its potential for practical application in data-driven SHM systems.