Clustering ultrasonic waves propagation time: A hierarchical polynomial semiparametric approach

Abstract

In the field of civil engineering, nondestructive evaluation (NDE) can be understood as the process of a material or structural element assessment based on techniques that do not introduces damage to the assessed sample. NDE can be used to inspect the quality of materials or to find some damages, as cracks and voids. This way, ultrasonic data monitoring has become one of the most important tools for detecting local damages in civil engineering elements. Concerning to masonry application, this method consists in the measurement of the time that the ultrasonic wave takes to pass through a material and changes in the ultrasonic wave propagation can be used for identifying damages presence and anomaly. Despite statistical methods be widely used for processing large data sets, in the case of ultrasonic waves some issues need to be overcoming, as the analysis of the path traveled by the ultrasonic wave. Thus, the main aim of this work is to analyze the ultrasonic wave propagation behavior through a new approach of a statistical model. Using a Bayesian semiparametric method with Dirichlet process, a data‐driven hierarchical regression model is applied to simulated and experimental data sets. For simulated data sets, the sensibility of the model in clustering profiles in an unknown number of groups and also detecting outliers is observed. The application in experimental data shows the potential use of the model for real case applications due to its efficiency in identifying different behaviors of the wave propagation in materials. Therefore, considering the advanced of automation and Internet of Things, statistical methods based on clustering are important and useful tools for being explored in the masonry conditions monitoring.