Structural damage detection based on posteriori probability support vector machine and Dempster–Shafer evidence theory

Zhou, Qifeng; Zhou, Hao; Zhou, Qingqing; Yang, Fan; Luo, Lei; Li, Tao

doi:10.1016/j.asoc.2015.06.057

Cited by 50 publications

(23 citation statements)

References 18 publications

(21 reference statements)

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“…The feasibility of the approach was demonstrated using data collected during field test measurements of the Sydney Harbour Bridge. Zhou et al [8] proposed a structural damage detection method based on posteriori probability support vector machine (PPSVM) and Dempster-Shafer (DS) evidence theory adopted to combine the decision level fusion information. The proposed damage detection method was verified by means of experimental analysis of a benchmark structure model.…”

Section: Literature Reviewmentioning

confidence: 99%

Structural health monitoring of bridges: a model-free ANN-based approach to damage detection

Neves

González

Leander

et al. 2017

J Civil Struct Health Monit

173

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As civil engineering structures are growing in dimension and longevity, there is an associated increase in concern regarding the maintenance of such structures. Bridges, in particular, are critical links in today's transportation networks and hence fundamental for the development of society. In this context, the demand for novel damage detection techniques and reliable structural health monitoring systems is currently high. This paper presents a model-free damage detection approach based on machine learning techniques. The method is applied to data on the structural condition of a fictitious railway bridge gathered in a numerical experiment using a three-dimensional finite element model. Data are collected from the dynamic response of the structure, which is simulated in the course of the passage of a train, considering the bridge in healthy and two different damaged scenarios. In the first stage of the proposed method, artificial neural networks are trained with an unsupervised learning approach with input data composed of accelerations gathered on the healthy bridge. Based on the acceleration values at previous instants in time, the networks are able to predict future accelerations. In the second stage, the prediction errors of each network are statistically characterized by a Gaussian process that supports the choice of a damage detection threshold. Subsequent to this, by comparing damage indices with said threshold, it is possible to discriminate between different structural conditions, namely between healthy and damaged. From here and for each damage case scenario, receiver operating characteristic curves that illustrate the trade-off between true and false positives can be obtained. Lastly, based on the Bayes' Theorem, a simplified method for the calculation of the expected total cost of the proposed strategy, as a function of the chosen threshold, is suggested.

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Section: Literature Reviewmentioning

confidence: 99%

Structural health monitoring of bridges: a model-free ANN-based approach to damage detection

Neves

González

Leander

et al. 2017

J Civil Struct Health Monit

173

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“…The Dempster's combination rule possesses several interesting mathematical properties, such as commutativity and associativity, and it plays a very significant role in evidence theory [4]. Nowadays, the evidence theory is applied widely in many fields, like supplier selection ( [5,6]), target recognition ( [7][8][9]), decision making ( [4,10,11]), reliability analysis ( [12][13][14][15]), and so on.…”

Section: Introductionmentioning

confidence: 99%

Weighted Evidence Combination Rule Based on Evidence Distance and Uncertainty Measure: An Application in Fault Diagnosis

Chen

Diao²,

Sang

2018

Mathematical Problems in Engineering

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Conflict management in Dempster-Shafer theory (D-S theory) is a hot topic in information fusion. In this paper, a novel weighted evidence combination rule based on evidence distance and uncertainty measure is proposed. The proposed approach consists of two steps. First, the weight is determined based on the evidence distance. Then, the weight value obtained in first step is modified by taking advantage of uncertainty. Our proposed method can efficiently handle high conflicting evidences with better performance of convergence. A numerical example and an application based on sensor fusion in fault diagnosis are given to demonstrate the efficiency of our proposed method.

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“…The Dempster-Shafer evidence theory refers to a general framework for reasoning with uncertainty, with understood connections to other frameworks such as probability, possibility and imprecise probability theories [20,21]. This theory was proposed by Arthur P. Dempster in the context of statistical inference, and then this theory was extended by Glenn Shafer into a general framework [22].…”

Section: Monitoring Water Quality By Multi-sensor Fusion Based On Demmentioning

confidence: 99%

Water quality monitoring method based on data fusion technology

Liu¹,

Qiao²

2017

MMC_C

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In order to effectively monitor water quality, this paper proposes a data fusion method based on Dempster-Shafer evidence theory to detect pollutants in water. Our proposed water quality monitoring system is organized as a hierarchical structure, and the whole monitoring area is divided into several parts. The water quality monitoring system includes an online monitoring module and an offline monitoring module. In particular, each monitoring area has a cluster that contains several wireless sensor nodes to collect data and communicate with other sensor nodes. Furthermore, multiple water quality parameters are detected in our water quality monitoring system, such as PH, conductivity, temperature, dissolved oxygen, turbidity, etc. The final water quality monitoring decisions are made by fusing various types of water quality indexes using the Dempster-Shafer evidence theory. Finally, experimental results prove that the proposed method can detect pollutants in water with higher accuracy by effectively fusing various types of water quality indexes.

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Structural damage detection based on posteriori probability support vector machine and Dempster–Shafer evidence theory

Cited by 50 publications

References 18 publications

Structural health monitoring of bridges: a model-free ANN-based approach to damage detection

Structural health monitoring of bridges: a model-free ANN-based approach to damage detection

Weighted Evidence Combination Rule Based on Evidence Distance and Uncertainty Measure: An Application in Fault Diagnosis

Water quality monitoring method based on data fusion technology

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