Details
| Original language | English |
|---|---|
| Article number | 2142015 |
| Journal | International Journal of Computational Methods |
| Volume | 19 |
| Issue number | 8 |
| Early online date | 1 Oct 2022 |
| Publication status | Published - Oct 2022 |
| Externally published | Yes |
Abstract
Collapses are the most sensational types of events and frequently the ones that cause the most serious consequences in tunneling operations. It often occurs because of insufficient geological studies and the limitations of experience-based decision-making. To cope with those problems, we proposed a data-driven model based on the tunnel boring machine operational data and the in situ geological information to forecast tunneling-induced ground collapse. In the proposed model, we offered a general data process flow diagram to process engineering data. Three machine learning classifiers, k-nearest neighbors, support vector classifier, and random forests were adopted for collapse prediction. The performance of the three classifiers was verified based on the data from the Yinsong water conveyance tunnel. The results illustrated that the proposed data-driven model was sufficient for the studying task with 90% of the collapsed zones being identified on average. The contributions of this paper are to provide a reliable data process flow diagram to process engineering data and offer an accurate and robust model for identifying collapses.
Keywords
- ground collapse, machine learning classifier, TBM operational data, Tunnel boring machine
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science (miscellaneous)
- Mathematics(all)
- Computational Mathematics
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In: International Journal of Computational Methods, Vol. 19, No. 8, 2142015, 10.2022.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Predicting Tunneling-Induced Ground Collapse Based on TBM Operational Data and Geological Data
AU - Zhu, Mengqi
AU - Zhu, Hehua
AU - Gutierrez, Marte
AU - Ju, J. Woody
AU - Zhuang, Xiaoying
AU - Wu, Wei
N1 - Acknowledgments This work was supported by grants from the National Key Basic Research andDevelopment Program of China (No. 2018YFB2101000), the Special Fund forBasic Research on Scientific Instruments of the National Natural Science Foun-dation of China (No. 41827807), the National Natural Science Foundation ofChina (41902275, U1934212, 4182780021), PowChina Hebei Transportation High-way Investment Development Co., Ltd (TH-201908), the Science and Technologymajor program of Guizhou Province (2018-3011) and the China Scholarship Council (No. 201906260211). The authors would like to thank China Railway EngineeringEquipment Group CO., LTD. and Professor Zuyu Chen from the ChineseInstitute of Water Resources and Hydropower Research for excellent technical support.
PY - 2022/10
Y1 - 2022/10
N2 - Collapses are the most sensational types of events and frequently the ones that cause the most serious consequences in tunneling operations. It often occurs because of insufficient geological studies and the limitations of experience-based decision-making. To cope with those problems, we proposed a data-driven model based on the tunnel boring machine operational data and the in situ geological information to forecast tunneling-induced ground collapse. In the proposed model, we offered a general data process flow diagram to process engineering data. Three machine learning classifiers, k-nearest neighbors, support vector classifier, and random forests were adopted for collapse prediction. The performance of the three classifiers was verified based on the data from the Yinsong water conveyance tunnel. The results illustrated that the proposed data-driven model was sufficient for the studying task with 90% of the collapsed zones being identified on average. The contributions of this paper are to provide a reliable data process flow diagram to process engineering data and offer an accurate and robust model for identifying collapses.
AB - Collapses are the most sensational types of events and frequently the ones that cause the most serious consequences in tunneling operations. It often occurs because of insufficient geological studies and the limitations of experience-based decision-making. To cope with those problems, we proposed a data-driven model based on the tunnel boring machine operational data and the in situ geological information to forecast tunneling-induced ground collapse. In the proposed model, we offered a general data process flow diagram to process engineering data. Three machine learning classifiers, k-nearest neighbors, support vector classifier, and random forests were adopted for collapse prediction. The performance of the three classifiers was verified based on the data from the Yinsong water conveyance tunnel. The results illustrated that the proposed data-driven model was sufficient for the studying task with 90% of the collapsed zones being identified on average. The contributions of this paper are to provide a reliable data process flow diagram to process engineering data and offer an accurate and robust model for identifying collapses.
KW - ground collapse
KW - machine learning classifier
KW - TBM operational data
KW - Tunnel boring machine
UR - http://www.scopus.com/inward/record.url?scp=85132745665&partnerID=8YFLogxK
U2 - 10.1142/S0219876221420159
DO - 10.1142/S0219876221420159
M3 - Article
AN - SCOPUS:85132745665
VL - 19
JO - International Journal of Computational Methods
JF - International Journal of Computational Methods
SN - 0219-8762
IS - 8
M1 - 2142015
ER -