Special Issue: Real World Application of SHM in Australia

Nguyễn, Andy; Chan, Tommy H.T.; Zhu, Xinqun

doi:10.1177/1475921718823980

Cited by 7 publications

(3 citation statements)

References 30 publications

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“…As depicted in Figure 8, the sensors are positioned on the upper part of the structure (i.e., at levels 4, 6, 8, and 10) that appear to be more sensitive to the ambient vibrations originating from occupants' activities or environmental loads, such as wind load (228). Known as the testbed of Australia's first-ever long-term full-scale SHM system, P block building has interestingly hosted the 8th International Conference on Structural Health Monitoring of Intelligent Infrastructures (SHMII-8) on the first time this prestigious conference in the SHM field was organised in Oceania (229). More details regarding the P-block are presented in (230,231) Figure 8 P-block building(227)…”

Section: P-block Buildingmentioning

confidence: 99%

A Critical Review on Structural Health Monitoring: Definitions, Methods, and Perspectives

Gharehbaghi

Farsangi

Noori

et al. 2021

Arch Computat Methods Eng

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The benefits of tracking, identifying, measuring features of interest from structure responses have endless applications for saving cost, time and improving safety. To date, structural health monitoring (SHM) has been extensively applied in several fields, such as aerospace, automotive, and mechanical engineering. However, the focus of this paper is to provide a comprehensive upto-date review of civil engineering structures such as buildings, bridges, and other infrastructures. For this reason, this article commences with a concise introduction to the fundamental definitions of SHM. The next section presents the general concepts and factors that determine the best strategy to be employed for SHM. Afterward, a thorough review of the most prevalent anomaly detection approaches, from classic techniques to advanced methods, is presented. Subsequently, some popular benchmarks, including laboratory specimens and real structures for validating the proposed methodologies, are demonstrated and discussed. Finally, the advantages and disadvantages of each method are summarized, which can be helpful in future studies.

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Section: P-block Buildingmentioning

confidence: 99%

A Critical Review on Structural Health Monitoring: Definitions, Methods, and Perspectives

Gharehbaghi

Farsangi

Noori

et al. 2021

Arch Computat Methods Eng

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“…In order to ensure the safety and reliability of structures, safety monitoring of structures is significantly important throughout their lifetime. Therefore, structural health monitoring (SHM) technology has been developed at an amazing pace over the past decades (Nguyen et al, 2019). As a key step to deploy SHM technology, structural damage detection (SDD) has attracted increasing attention in related fields.…”

Section: Introductionmentioning

confidence: 99%

A hybrid ant lion optimizer with improved Nelder–Mead algorithm for structural damage detection by improving weighted trace lasso regularization

Chen

2019

Advances in Structural Engineering

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Structural damage detection is the kernel technique in deploying structural health monitoring. The structural damage–detection technique using heuristic algorithms has been developed at an astounding pace over the past years. However, some existing structural damage–detection methods are prone to easily fall into the local optimum and to be unstable when they are applied to complex structures. In order to make full use of advantages of heuristic algorithms and overcome abovementioned shortcomings, a hybrid algorithm, which combines the ant lion optimizer with an improved Nelder–Mead algorithm, is proposed to solve the constrained optimization problem of complex structural damage detection. First, an objective function is established for damage identification using structural modal parameters, that is, frequencies and mode shapes. The solution to the objective function is accurately attained by a newly improved weighted trace lasso which can improve the computing performance and stability of procedure and reduce randomness of weighted coefficients. After assessing the computing performance of the proposed hybrid algorithm using three classical mathematical benchmark functions, two structural damage–detection numerical simulations and a laboratory verification are then conducted to fully assess the structural damage–detection capability of the proposed method. Meanwhile, the equivalent element stiffness-reduction model is introduced to estimate the real damage severities of cracks which are created in laboratory structures and to compare with the structural damage–detection results by the proposed method. The illustrated results show that the proposed hybrid algorithm can locate damage and quantify damage severity more accurately and stably with a good robustness to noise.

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“…an annual workshop for members and practicing engineers to exchange their knowledge and practical developments in Structural Health Monitoring (SHM). The annual workshop series have been organized in around Australia either as a standalone workshop or in partnership with other scientific events including international conferences, and several special issues have been published based on these activities and recent research progresses 1,2 As the guest editors of this special issue, we thank the authors for their contribution and all the anonymous reviewers who provided constructive review comments to the manuscripts submitted to this special issue. We would also like to express our sincere gratitude to Professors C. M. Wang, Y.…”

mentioning

confidence: 99%

Preface: Recent Research Advances on Structural Health Monitoring of Civil Engineering Structures

Chan

Nguyễn

et al. 2020

Int. J. Str. Stab. Dyn.

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Special Issue: Real World Application of SHM in Australia

Cited by 7 publications

References 30 publications

A Critical Review on Structural Health Monitoring: Definitions, Methods, and Perspectives

A Critical Review on Structural Health Monitoring: Definitions, Methods, and Perspectives

A hybrid ant lion optimizer with improved Nelder–Mead algorithm for structural damage detection by improving weighted trace lasso regularization

Preface: Recent Research Advances on Structural Health Monitoring of Civil Engineering Structures

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