Quality Evaluation of Rock Mass Using RMR14 Based on Multi-Source Data Fusion

The uncertainties in quality evaluations of rock mass are embedded in the underlying multi-source data composed by a variety of testing methods and some specialized sensors. To mitigate this issue, a proper method of data-driven computing for quality evaluation of rock mass based on the theory of mu...

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Autores principales:	Qi Zhang, Qing Jiang, Yuanhai Li, Ning Wang, Lei He
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	rock mass quality evaluation data-driven computing multi-source data fusion D-S evidence theory belief reinforcement Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/8627d1e2361241589174ea7fc1027d5c
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id	oai:doaj.org-article:8627d1e2361241589174ea7fc1027d5c
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spelling	oai:doaj.org-article:8627d1e2361241589174ea7fc1027d5c2021-11-11T19:07:03ZQuality Evaluation of Rock Mass Using RMR14 Based on Multi-Source Data Fusion10.3390/s212171081424-8220https://doaj.org/article/8627d1e2361241589174ea7fc1027d5c2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7108https://doaj.org/toc/1424-8220The uncertainties in quality evaluations of rock mass are embedded in the underlying multi-source data composed by a variety of testing methods and some specialized sensors. To mitigate this issue, a proper method of data-driven computing for quality evaluation of rock mass based on the theory of multi-source data fusion is required. As the theory of multi-source data fusion, Dempster–Shafer (D-S) evidence theory is applied to the quality evaluation of rock mass. As the correlation between different rock mass indices is too large to be ignored, belief reinforcement and Murphy’s average belief theory are introduced to process the multi-source data of rock mass. The proposed method is designed based on RMR14, one of the most widely used quality-evaluating methods for rock mass in the world. To validate the proposed method, the data of rock mass is generated randomly to realize the data fusion based on the proposed method and the conventional D-S theory. The fusion results based on these two methods are compared. The result of the comparison shows the proposed method amplifies the distance between the possibilities at different ratings from 0.0666 to 0.5882, which makes the exact decision more accurate than the other. A case study is carried out in Daxiagu tunnel in China to prove the practical value of the proposed method. The result shows the rock mass rating of the studied section of the tunnel is in level III with the maximum possibility of 0.9838, which agrees with the geological survey report.Qi ZhangQing JiangYuanhai LiNing WangLei HeMDPI AGarticlerock massquality evaluationdata-driven computingmulti-source data fusionD-S evidence theorybelief reinforcementChemical technologyTP1-1185ENSensors, Vol 21, Iss 7108, p 7108 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	rock mass quality evaluation data-driven computing multi-source data fusion D-S evidence theory belief reinforcement Chemical technology TP1-1185
spellingShingle	rock mass quality evaluation data-driven computing multi-source data fusion D-S evidence theory belief reinforcement Chemical technology TP1-1185 Qi Zhang Qing Jiang Yuanhai Li Ning Wang Lei He Quality Evaluation of Rock Mass Using RMR14 Based on Multi-Source Data Fusion
description	The uncertainties in quality evaluations of rock mass are embedded in the underlying multi-source data composed by a variety of testing methods and some specialized sensors. To mitigate this issue, a proper method of data-driven computing for quality evaluation of rock mass based on the theory of multi-source data fusion is required. As the theory of multi-source data fusion, Dempster–Shafer (D-S) evidence theory is applied to the quality evaluation of rock mass. As the correlation between different rock mass indices is too large to be ignored, belief reinforcement and Murphy’s average belief theory are introduced to process the multi-source data of rock mass. The proposed method is designed based on RMR14, one of the most widely used quality-evaluating methods for rock mass in the world. To validate the proposed method, the data of rock mass is generated randomly to realize the data fusion based on the proposed method and the conventional D-S theory. The fusion results based on these two methods are compared. The result of the comparison shows the proposed method amplifies the distance between the possibilities at different ratings from 0.0666 to 0.5882, which makes the exact decision more accurate than the other. A case study is carried out in Daxiagu tunnel in China to prove the practical value of the proposed method. The result shows the rock mass rating of the studied section of the tunnel is in level III with the maximum possibility of 0.9838, which agrees with the geological survey report.
format	article
author	Qi Zhang Qing Jiang Yuanhai Li Ning Wang Lei He
author_facet	Qi Zhang Qing Jiang Yuanhai Li Ning Wang Lei He
author_sort	Qi Zhang
title	Quality Evaluation of Rock Mass Using RMR14 Based on Multi-Source Data Fusion
title_short	Quality Evaluation of Rock Mass Using RMR14 Based on Multi-Source Data Fusion
title_full	Quality Evaluation of Rock Mass Using RMR14 Based on Multi-Source Data Fusion
title_fullStr	Quality Evaluation of Rock Mass Using RMR14 Based on Multi-Source Data Fusion
title_full_unstemmed	Quality Evaluation of Rock Mass Using RMR14 Based on Multi-Source Data Fusion
title_sort	quality evaluation of rock mass using rmr14 based on multi-source data fusion
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8627d1e2361241589174ea7fc1027d5c
work_keys_str_mv	AT qizhang qualityevaluationofrockmassusingrmr14basedonmultisourcedatafusion AT qingjiang qualityevaluationofrockmassusingrmr14basedonmultisourcedatafusion AT yuanhaili qualityevaluationofrockmassusingrmr14basedonmultisourcedatafusion AT ningwang qualityevaluationofrockmassusingrmr14basedonmultisourcedatafusion AT leihe qualityevaluationofrockmassusingrmr14basedonmultisourcedatafusion
_version_	1718431595334991872

Quality Evaluation of Rock Mass Using RMR14 Based on Multi-Source Data Fusion

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