Você está aqui: Página Inicial / Seminários / 23/04/2021 - Clustering ultrasonic waves propagation time: a hierarchical polynomial semi-parametric approach - Speaker: Rosineide Fernando da Paz (Universidade Federal do Ceará)

23/04/2021 - Clustering ultrasonic waves propagation time: a hierarchical polynomial semi-parametric approach - Speaker: Rosineide Fernando da Paz (Universidade Federal do Ceará)

Quando 23/04/2021
das 14h00 até 16h00
Adicionar evento ao calendário vCal
iCal
UFSCar/USP joint Seminar

Scheduled for:
April 23, 2021, at 2:00 pm
(GMT-03:00) Brasilia Standard Time - Sao Paulo
 
Click here to watch again.
 
Speaker:
Rosineide Fernando da Paz (Federal University of Ceará)

Title:
Clustering ultrasonic waves propagation time: a hierarchical polynomial semi-parametric approach

Abstract:
In the field of civil engineering, non-destructive 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 semi-parametric 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.
 
Bio:
Rosineide Fernando da Paz is an Assistant Professor of the Universidade Federal do Ceará (UFC), Russas, Brazil. She received her PhD in statistics (2017) at PIPGEs (UFSCar/ICMC-USP). Among other things, she is interested in mixture models, Bayesian analysis and simplex distributions.
Announcement Paz
registrado em: