Sensors, Volume (20), No (8), Year (2020-4) , Pages (2328-2344)

Title : ( Big Data Analytics and Structural Health Monitoring: A Statistical Pattern Recognition-Based Approach )

Authors: Alireza Entezami , Hassan Sarmadi , Behshid Behkamal , Stefano Mariani ,

Citation: BibTeX | EndNote

Abstract

برای دانلود از شناسه و رمز عبور پرتال پویا استفاده کنید.

@article{paperid:1080369,
author = {Entezami, Alireza and Sarmadi, Hassan and Behkamal, Behshid and Stefano Mariani},
title = {Big Data Analytics and Structural Health Monitoring: A Statistical Pattern Recognition-Based Approach},
journal = {Sensors},
year = {2020},
volume = {20},
number = {8},
month = {April},
issn = {1424-8220},
pages = {2328--2344},
numpages = {16},
keywords = {Recent advances in sensor technologies and data acquisition systems opened up the era of big data in the field of structural health monitoring (SHM). Data-driven methods based on statistical pattern recognition provide outstanding opportunities to implement a long-term SHM strategy; by exploiting measured vibration data. However; their main limitation; due to big data or high-dimensional features; is linked to the complex and time-consuming procedures for feature extraction and/or statistical decision-making. To cope with this issue; in this article we propose a strategy based on autoregressive moving average (ARMA) modeling for feature extraction; and on an innovative hybrid divergence-based method for feature classification. Data relevant to a cable-stayed bridge are accounted for to assess the eectiveness and eciency of the proposed method. The results show that the oered hybrid divergence-based method; in conjunction with ARMA modeling; succeeds in detecting damage in cases strongly characterized by big data.},
}

[Download]

%0 Journal Article
%T Big Data Analytics and Structural Health Monitoring: A Statistical Pattern Recognition-Based Approach
%A Entezami, Alireza
%A Sarmadi, Hassan
%A Behkamal, Behshid
%A Stefano Mariani
%J Sensors
%@ 1424-8220
%D 2020

[Download]