Predicting tunnel water inflow using a machine learning-based solution to improve tunnel construction safety. (May 2023)

• Record Type: Journal Article
• Authors: Mahmoodzadeh, Arsalan; Ghafourian, Hossein; Hussein Mohammed, Adil; Rezaei, Nafiseh; Hashim Ibrahim, Hawkar; Rashidi, Shima
• Is Part Of: Transportation geotechnics. Volume 40(2023)
• Publication Date: 2023-05
• Subjects: Tunneling -- Water inflow -- Safety -- Machine learning -- Gene expression programming; Engineering geology -- Periodicals; Soil mechanics -- Periodicals; Rock mechanics -- Periodicals; Transportation -- Periodicals; 624.15105
• DOI: 10.1016/j.trgeo.2023.100978
• Languages: English
• ISSNs: 2214-3912

Abstract

Highlights: Optimization of the GEP model to generate an estimation equation of water inflow. Development of ML applications in estimating and mitigating risks related to geo-resources in tunnels. Reducing risks related to the phenomenon of water inflow during the construction of tunnels. Reducing uncertainties in the early stages of planning and designing tunnels. Abstract: 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.