Predicting tunnel water inflow using a machine learning-based solution to improve tunnel construction safety

被引：15

作者：

Mahmoodzadeh, Arsalan ^{[1
]}

Ghafourian, Hossein ^{[2
]}

Mohammed, Adil Hussein ^{[3
]}

Rezaei, Nafiseh ^{[4
]}

Ibrahim, Hawkar Hashim ^{[5
]}

Rashidi, Shima ^{[6
]}

机构：

[1] Tarbiat Modares Univ, Sch Engn, Rock Mech Div, Tehran, Iran

[2] Univ Massachusetts Amherst, Dept Civil & Environm Engn, Amherst, MA USA

[3] Cihan Univ Erbil, Fac Engn, Dept Commun & Comp Engn, Erbil, Kurdistan, Iraq

[4] Univ Qom, Fac Engn, Dept Civil Engn, Qom, Iran

[5] Salahaddin Univ Erbil, Coll Engn, Dept Civil Engn, 44002 Erbil, Kurdistan Reg, Erbil, Kurdistan, Iraq

[6] Univ Human Dev, Coll Sci & Technol, Dept Comp Sci, Sulaymaniyah, Kurdistan, Iraq

来源：

TRANSPORTATION GEOTECHNICS | 2023年 / 40卷

关键词：

Tunneling; Water inflow; Safety; Machine learning; Gene expression programming; MODEL; FLOW;

D O I：

10.1016/j.trgeo.2023.100978

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

Water inflow is a typical and complicated geological hazard that may have a significant effect on both the building timeline and the safety of a tunnel under construction. Therefore, accurate water inflow estimation in tunneling is a key factor for the project's success. Such information is critical for the early conceptual and design phases, when key choices must be made. For this purpose, an optimized model based on the gene expression programming (GEP) method was proposed to estimate the water inflow in tunnels. An equation was generated for the optimized GEP model through the best fit of the predictions. Finally, by comparing the equation's outputs with the actual ones and comparing its behavior with practice, its potential ability for estimating the water inflow of tunnels was approved. This model can reduce the uncertainties about tunnels and give machine learning development in tunnel planning.

引用

页数：15

共 51 条

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