Big Data-Based Boring Indexes and Their Application during TBM Tunneling
Tunnel boring machine (TBM) tunneling data have been extensively collected to utilize TBM information technology by analyzing and mining the data for achieving a safe and efficient TBM tunneling. Feature extraction of big data could reduce the complexity for problems, but conventional indexes based...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/2621931 |
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| _version_ | 1832561532283650048 |
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| author | Shuangjing Wang Yujie Wang Xu Li Lipeng Liu Hai Xing Yunpei Zhang |
| author_facet | Shuangjing Wang Yujie Wang Xu Li Lipeng Liu Hai Xing Yunpei Zhang |
| author_sort | Shuangjing Wang |
| collection | DOAJ |
| description | Tunnel boring machine (TBM) tunneling data have been extensively collected to utilize TBM information technology by analyzing and mining the data for achieving a safe and efficient TBM tunneling. Feature extraction of big data could reduce the complexity for problems, but conventional indexes based on feature extraction, such as field penetration index (FPI), specific penetration (SP), and boreability index (BI), have some disadvantages. Thus, we present novel boring indexes derived from tunneling data in the Yinchao TBM project. Linear thrust-penetration and torque-penetration relationships in filtered ascending sections (p ≥ 2 mm/r) are proposed using statistical features and through physical mechanism analysis of parameters in the TBM cyclic tunneling process. Boring indexes, such as normal boring difficulty index, initial rock mass fragmentation difficulty index, and tangential boring difficulty index, are defined using the coefficients of the linear thrust-penetration and torque-penetration relationships. Subsequently, the defined boring indexes are verified using performance prediction of 291 cyclic tunneling processes. Finally, a preliminary application of support measure suggestions is conducted using the statistical features of boring indexes, where certain criteria are proposed and verified. The results showed that the criterion of boring indexes for support measure suggestions could achieve a reasonable confirmation, potentially providing quantitative quotas for support measure suggestions in the subsequent construction process. |
| format | Article |
| id | doaj-art-d504d734bb5441f4ab36d9df60f029f2 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-d504d734bb5441f4ab36d9df60f029f22025-02-03T01:24:54ZengWileyAdvances in Civil Engineering1687-80861687-80942021-01-01202110.1155/2021/26219312621931Big Data-Based Boring Indexes and Their Application during TBM TunnelingShuangjing Wang0Yujie Wang1Xu Li2Lipeng Liu3Hai Xing4Yunpei Zhang5State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaState Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaKey Laboratory of Urban Underground Engineering of Ministry of Education, Beijing Jiaotong University, Beijing 100044, ChinaState Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaSinohydro Bureau 6 Co., Ltd., Shenyang 110179, Liaoning, ChinaState Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaTunnel boring machine (TBM) tunneling data have been extensively collected to utilize TBM information technology by analyzing and mining the data for achieving a safe and efficient TBM tunneling. Feature extraction of big data could reduce the complexity for problems, but conventional indexes based on feature extraction, such as field penetration index (FPI), specific penetration (SP), and boreability index (BI), have some disadvantages. Thus, we present novel boring indexes derived from tunneling data in the Yinchao TBM project. Linear thrust-penetration and torque-penetration relationships in filtered ascending sections (p ≥ 2 mm/r) are proposed using statistical features and through physical mechanism analysis of parameters in the TBM cyclic tunneling process. Boring indexes, such as normal boring difficulty index, initial rock mass fragmentation difficulty index, and tangential boring difficulty index, are defined using the coefficients of the linear thrust-penetration and torque-penetration relationships. Subsequently, the defined boring indexes are verified using performance prediction of 291 cyclic tunneling processes. Finally, a preliminary application of support measure suggestions is conducted using the statistical features of boring indexes, where certain criteria are proposed and verified. The results showed that the criterion of boring indexes for support measure suggestions could achieve a reasonable confirmation, potentially providing quantitative quotas for support measure suggestions in the subsequent construction process.http://dx.doi.org/10.1155/2021/2621931 |
| spellingShingle | Shuangjing Wang Yujie Wang Xu Li Lipeng Liu Hai Xing Yunpei Zhang Big Data-Based Boring Indexes and Their Application during TBM Tunneling Advances in Civil Engineering |
| title | Big Data-Based Boring Indexes and Their Application during TBM Tunneling |
| title_full | Big Data-Based Boring Indexes and Their Application during TBM Tunneling |
| title_fullStr | Big Data-Based Boring Indexes and Their Application during TBM Tunneling |
| title_full_unstemmed | Big Data-Based Boring Indexes and Their Application during TBM Tunneling |
| title_short | Big Data-Based Boring Indexes and Their Application during TBM Tunneling |
| title_sort | big data based boring indexes and their application during tbm tunneling |
| url | http://dx.doi.org/10.1155/2021/2621931 |
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