Optimizing Groundwater Seepage Prediction in Tunnels using Human Mental Search Algorithm: a Cognitive-Inspired Approach to Complex Geotechnical Challenges

This paper introduces the Human Mental Search (HMS) algorithm as a novel and superior optimization technique for predicting groundwater seepage in tunnel environments. Traditional methods for predicting such seepage often struggle with the complexities of subterranean water flow, particularly in heterogeneous geological conditions, and while machine learning approaches have offered improvements, they often require significant computational resources. The HMS algorithm, inspired by human cognitive processes, employs memory recall, adaptive clustering, and strategic selection to efficiently refine solutions. Our results demonstrate that HMS significantly outperforms established algorithms in predicting groundwater seepage, achieving an R² value of ۰.۹۹۸۸, a Mean Squared Error (MSE) of ۰.۰۰۰۲, and a Root Mean Squared Error (RMSE) of ۰.۰۱۳۷. In comparison, the Whale Optimization Algorithm (WOA) achieved an R² of ۰.۹۹۵۱ with much higher MSE and RMSE, and other methods, like Genetic Programming and ANN-PSO, show higher error values. The HMS algorithm also showed a Variance Accounted for (VAF) of ۹۹.۸۸% and a Mean Absolute Error (MAE) of ۰.۰۰۴۱, further validating its high predictive accuracy. This study highlights the HMS algorithm’s superior performance and computational efficiency for optimizing groundwater seepage predictions, positioning it as a powerful tool for geotechnical engineering applications, including real-time monitoring.

کلیدواژه ها:

Groundwater seepage prediction ، Human Mental Search algorithm ، HMS ، tunnel geotechnics