Multi-source data fusion method for structural safety assessment of water diversion structures

Building safety assessment based on single sensor data has the problems of low reliability and high uncertainty. Therefore, this paper proposes a novel multi-source sensor data fusion method based on Improved Dempster–Shafer (D-S) evidence theory and Back Propagation Neural Network (BPNN). Before da...

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Autores principales:	Sherong Zhang, Ting Liu, Chao Wang
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	bp neural network d-s evidence theory multi-source data fusion safety evaluation structural safety water diversion project Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/3709b302711d41d583722ad0797c6e8c
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id	oai:doaj.org-article:3709b302711d41d583722ad0797c6e8c
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spelling	oai:doaj.org-article:3709b302711d41d583722ad0797c6e8c2021-11-05T17:42:49ZMulti-source data fusion method for structural safety assessment of water diversion structures1464-71411465-173410.2166/hydro.2021.154https://doaj.org/article/3709b302711d41d583722ad0797c6e8c2021-03-01T00:00:00Zhttp://jh.iwaponline.com/content/23/2/249https://doaj.org/toc/1464-7141https://doaj.org/toc/1465-1734Building safety assessment based on single sensor data has the problems of low reliability and high uncertainty. Therefore, this paper proposes a novel multi-source sensor data fusion method based on Improved Dempster–Shafer (D-S) evidence theory and Back Propagation Neural Network (BPNN). Before data fusion, the improved self-support function is adopted to preprocess the original data. The process of data fusion is divided into three steps: Firstly, the feature of the same kind of sensor data is extracted by the adaptive weighted average method as the input source of BPNN. Then, BPNN is trained and its output is used as the basic probability assignment (BPA) of D-S evidence theory. Finally, Bhattacharyya Distance (BD) is introduced to improve D-S evidence theory from two aspects of evidence distance and conflict factors, and multi-source data fusion is realized by D-S synthesis rules. In practical application, a three-level information fusion framework of the data level, the feature level, and the decision level is proposed, and the safety status of buildings is evaluated by using multi-source sensor data. The results show that compared with the fusion result of the traditional D-S evidence theory, the algorithm improves the accuracy of the overall safety state assessment of the building and reduces the MSE from 0.18 to 0.01%. HIGHLIGHTS A new method is proposed to evaluate the safety status of water diversion structures by fusing multi-source heterogeneous sensor data.; A multi-sensor hierarchical data fusion model suitable for the structural characteristics of the water diversion project is established.; The classical D-S evidence theory is improved and combined with BPNN to reduce the uncertainty of sensor data.;Sherong ZhangTing LiuChao WangIWA Publishingarticlebp neural networkd-s evidence theorymulti-source data fusionsafety evaluationstructural safetywater diversion projectInformation technologyT58.5-58.64Environmental technology. Sanitary engineeringTD1-1066ENJournal of Hydroinformatics, Vol 23, Iss 2, Pp 249-266 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	bp neural network d-s evidence theory multi-source data fusion safety evaluation structural safety water diversion project Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
spellingShingle	bp neural network d-s evidence theory multi-source data fusion safety evaluation structural safety water diversion project Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066 Sherong Zhang Ting Liu Chao Wang Multi-source data fusion method for structural safety assessment of water diversion structures
description	Building safety assessment based on single sensor data has the problems of low reliability and high uncertainty. Therefore, this paper proposes a novel multi-source sensor data fusion method based on Improved Dempster–Shafer (D-S) evidence theory and Back Propagation Neural Network (BPNN). Before data fusion, the improved self-support function is adopted to preprocess the original data. The process of data fusion is divided into three steps: Firstly, the feature of the same kind of sensor data is extracted by the adaptive weighted average method as the input source of BPNN. Then, BPNN is trained and its output is used as the basic probability assignment (BPA) of D-S evidence theory. Finally, Bhattacharyya Distance (BD) is introduced to improve D-S evidence theory from two aspects of evidence distance and conflict factors, and multi-source data fusion is realized by D-S synthesis rules. In practical application, a three-level information fusion framework of the data level, the feature level, and the decision level is proposed, and the safety status of buildings is evaluated by using multi-source sensor data. The results show that compared with the fusion result of the traditional D-S evidence theory, the algorithm improves the accuracy of the overall safety state assessment of the building and reduces the MSE from 0.18 to 0.01%. HIGHLIGHTS A new method is proposed to evaluate the safety status of water diversion structures by fusing multi-source heterogeneous sensor data.; A multi-sensor hierarchical data fusion model suitable for the structural characteristics of the water diversion project is established.; The classical D-S evidence theory is improved and combined with BPNN to reduce the uncertainty of sensor data.;
format	article
author	Sherong Zhang Ting Liu Chao Wang
author_facet	Sherong Zhang Ting Liu Chao Wang
author_sort	Sherong Zhang
title	Multi-source data fusion method for structural safety assessment of water diversion structures
title_short	Multi-source data fusion method for structural safety assessment of water diversion structures
title_full	Multi-source data fusion method for structural safety assessment of water diversion structures
title_fullStr	Multi-source data fusion method for structural safety assessment of water diversion structures
title_full_unstemmed	Multi-source data fusion method for structural safety assessment of water diversion structures
title_sort	multi-source data fusion method for structural safety assessment of water diversion structures
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/3709b302711d41d583722ad0797c6e8c
work_keys_str_mv	AT sherongzhang multisourcedatafusionmethodforstructuralsafetyassessmentofwaterdiversionstructures AT tingliu multisourcedatafusionmethodforstructuralsafetyassessmentofwaterdiversionstructures AT chaowang multisourcedatafusionmethodforstructuralsafetyassessmentofwaterdiversionstructures
_version_	1718444094763565056

Multi-source data fusion method for structural safety assessment of water diversion structures

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