Data-Driven Approach to Structural Health Monitoring Using Statistical Learning Algorithms

Abstract

Condition assessment and prediction of existing infrastructure systems is a major objective in civil engineering. Structural health monitoring (SHM) achieves this goal by continuous data acquisition from an array of sensors deployed on the structure of interest. SHM constructs ubiquitous damage features from the acquired data, ensuring maximum sensitivity to the onset of damage and robustness to noise and variability in environmental and operational conditions. Traditionally, SHM has used a model-based approach, wherein a high-fidelity model of a structure is constructed and studied in detail to aid engineers in detecting the onset of damage based on deviations from an undamaged model of the system. However, given the complexity of structures and the inability to perfectly model all aspects of a system, a data-driven approach becomes an attractive alternative. In data-driven approaches, a surrogate model, constructed using acquired data from a system, is substituted for a real model. Although such models do not necessarily capture all the physics of a system, they are efficient for damage detection purposes. Statistical learning algorithms aid in the construction of such surrogate models, and their use has now been extensively documented in the literature. This chapter provides a brief review of applications of statistical learning algorithms, both supervised and unsupervised, in SHM for real-time condition assessment of civil infrastructure systems.