New Method for High Water Pressure Tunnel Design Based on Machine Learning for Smart Grid Energy Management and Groundwater Environmental Balance
In recent years, underwater shield tunnels are being developed according to large-scale sections. The problems of large buried depth and high water pressure have posed major challenges to the safety of segmented structures. The load-bearing capacity and damage of segmented structures under high wate...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2022/4912036 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850232133267750912 |
|---|---|
| author | Xiaoming You Gongxing Yan Zhengqiang Yang |
| author_facet | Xiaoming You Gongxing Yan Zhengqiang Yang |
| author_sort | Xiaoming You |
| collection | DOAJ |
| description | In recent years, underwater shield tunnels are being developed according to large-scale sections. The problems of large buried depth and high water pressure have posed major challenges to the safety of segmented structures. The load-bearing capacity and damage of segmented structures under high water pressure features have always attracted attention. Based on a machine learning approach to smart grid energy management, this paper proposes a design method for high voltage tunnels in a balanced groundwater environment and tests the capacity of the high voltage tunnels. Based on the high water pressure failure test phenomenon of the large-section shield tunnel of the GIL project, this paper analyzes the failure characteristics and laws of the segment structure under high water pressure conditions. On this basis, an evaluation index for the load-bearing performance of the segment structure is proposed, and control suggestions are given based on the research results. According to the fault characteristics and the section structure law, the section performance evaluation index is proposed, and the control parameter recommendations are given based on the test results. Valuable discoveries and breakthroughs have been made in the failure of the prototype segment structure and the difference in the mechanical properties of the segment structure in the form of the high water pressure tunnel assembly. The research results show that under the condition of staggered assembly of high-voltage tunnels, the maximum dislocation amount of the high-voltage tunnel structure during instability failure is 10 mm, and the bolt strength is improved. The more important aspect is the existence of concave and tenon between the rings. In structure, the maximum stress of the bolts between the rings is only 38.6% of the yield stress at the time of instability failure. This indicates that the distributed concave-convex tenon between the segments not only can control the dislocation of the segments but also can ensure that the longitudinal bolts are well protected. It is safe to ensure the pressure resistance of the high water pressure tunnel. |
| format | Article |
| id | doaj-art-0a195d2fef634991b7b028f2cd6b2950 |
| institution | OA Journals |
| issn | 2050-7038 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-0a195d2fef634991b7b028f2cd6b29502025-08-20T02:03:17ZengWileyInternational Transactions on Electrical Energy Systems2050-70382022-01-01202210.1155/2022/4912036New Method for High Water Pressure Tunnel Design Based on Machine Learning for Smart Grid Energy Management and Groundwater Environmental BalanceXiaoming You0Gongxing Yan1Zhengqiang Yang2Chongqing Vocational Institute of EngineeringChongqing Vocational Institute of EngineeringJiangsu Vocational Institute of Architectural TechnologyIn recent years, underwater shield tunnels are being developed according to large-scale sections. The problems of large buried depth and high water pressure have posed major challenges to the safety of segmented structures. The load-bearing capacity and damage of segmented structures under high water pressure features have always attracted attention. Based on a machine learning approach to smart grid energy management, this paper proposes a design method for high voltage tunnels in a balanced groundwater environment and tests the capacity of the high voltage tunnels. Based on the high water pressure failure test phenomenon of the large-section shield tunnel of the GIL project, this paper analyzes the failure characteristics and laws of the segment structure under high water pressure conditions. On this basis, an evaluation index for the load-bearing performance of the segment structure is proposed, and control suggestions are given based on the research results. According to the fault characteristics and the section structure law, the section performance evaluation index is proposed, and the control parameter recommendations are given based on the test results. Valuable discoveries and breakthroughs have been made in the failure of the prototype segment structure and the difference in the mechanical properties of the segment structure in the form of the high water pressure tunnel assembly. The research results show that under the condition of staggered assembly of high-voltage tunnels, the maximum dislocation amount of the high-voltage tunnel structure during instability failure is 10 mm, and the bolt strength is improved. The more important aspect is the existence of concave and tenon between the rings. In structure, the maximum stress of the bolts between the rings is only 38.6% of the yield stress at the time of instability failure. This indicates that the distributed concave-convex tenon between the segments not only can control the dislocation of the segments but also can ensure that the longitudinal bolts are well protected. It is safe to ensure the pressure resistance of the high water pressure tunnel.http://dx.doi.org/10.1155/2022/4912036 |
| spellingShingle | Xiaoming You Gongxing Yan Zhengqiang Yang New Method for High Water Pressure Tunnel Design Based on Machine Learning for Smart Grid Energy Management and Groundwater Environmental Balance International Transactions on Electrical Energy Systems |
| title | New Method for High Water Pressure Tunnel Design Based on Machine Learning for Smart Grid Energy Management and Groundwater Environmental Balance |
| title_full | New Method for High Water Pressure Tunnel Design Based on Machine Learning for Smart Grid Energy Management and Groundwater Environmental Balance |
| title_fullStr | New Method for High Water Pressure Tunnel Design Based on Machine Learning for Smart Grid Energy Management and Groundwater Environmental Balance |
| title_full_unstemmed | New Method for High Water Pressure Tunnel Design Based on Machine Learning for Smart Grid Energy Management and Groundwater Environmental Balance |
| title_short | New Method for High Water Pressure Tunnel Design Based on Machine Learning for Smart Grid Energy Management and Groundwater Environmental Balance |
| title_sort | new method for high water pressure tunnel design based on machine learning for smart grid energy management and groundwater environmental balance |
| url | http://dx.doi.org/10.1155/2022/4912036 |
| work_keys_str_mv | AT xiaomingyou newmethodforhighwaterpressuretunneldesignbasedonmachinelearningforsmartgridenergymanagementandgroundwaterenvironmentalbalance AT gongxingyan newmethodforhighwaterpressuretunneldesignbasedonmachinelearningforsmartgridenergymanagementandgroundwaterenvironmentalbalance AT zhengqiangyang newmethodforhighwaterpressuretunneldesignbasedonmachinelearningforsmartgridenergymanagementandgroundwaterenvironmentalbalance |