Field validation of controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis squared distance

Nguyen, Theanh, Chan, Tommy H.T., & Thambiratnam, David P. (2014) Field validation of controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis squared distance. Structural Health Monitoring, 13(4), pp. 473-488.

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Abstract

This article presents the field applications and validations for the controlled Monte Carlo data generation scheme. This scheme was previously derived to assist the Mahalanobis squared distance–based damage identification method to cope with data-shortage problems which often cause inadequate data multinormality and unreliable identification outcome. To do so, real-vibration datasets from two actual civil engineering structures with such data (and identification) problems are selected as the test objects which are then shown to be in need of enhancement to consolidate their conditions. By utilizing the robust probability measures of the data condition indices in controlled Monte Carlo data generation and statistical sensitivity analysis of the Mahalanobis squared distance computational system, well-conditioned synthetic data generated by an optimal controlled Monte Carlo data generation configurations can be unbiasedly evaluated against those generated by other set-ups and against the original data. The analysis results reconfirm that controlled Monte Carlo data generation is able to overcome the shortage of observations, improve the data multinormality and enhance the reliability of the Mahalanobis squared distance–based damage identification method particularly with respect to false-positive errors. The results also highlight the dynamic structure of controlled Monte Carlo data generation that makes this scheme well adaptive to any type of input data with any (original) distributional condition.

Impact and interest:

4 citations in Scopus
2 citations in Web of Science®
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ID Code: 73421
Item Type: Journal Article
Refereed: Yes
Keywords: Statistical damage identification, Mahalanobis squared distance, controlled Monte Carlo data generation, field validation, multinormal, sensitivity analysis
DOI: 10.1177/1475921714542892
ISSN: 1475-9217
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500) > Structural Engineering (090506)
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 The Author(s)
Deposited On: 07 Jul 2014 22:29
Last Modified: 22 Jun 2017 01:03

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