Bayesian decision and mixture models for AE monitoring of steel–concrete composite shear walls

This paper presents an approach based on an acoustic emission technique for the health monitoring of steel–concrete (SC) composite shear walls. SC composite walls consist of plain (unreinforced) concrete sandwiched between steel faceplates. Although the use of SC system construction has been studied extensively for nearly 20 years, little-to-no attention has been devoted to the development of structural health monitoring techniques for the inspection of damage of the concrete behind the steel plates. In this work an unsupervised pattern recognition algorithm based on probability theory is proposed to assess the soundness of the concrete infill, and eventually provide a diagnosis of the SC wall’s health. The approach is validated through an experimental study on a large-scale SC shear wall subjected to a displacement controlled reversed cyclic loading.