Document Type : Original Research Paper
Authors
1 Department of Mining Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran
2 Faculty of Electrical and Computer Engineering, University of Kashan, Kashan,IR. Iran
Abstract
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 0.9988, a Mean Squared Error (MSE) of 0.0002, and a Root Mean Squared Error (RMSE) of 0.0137. In comparison, the Whale Optimization Algorithm (WOA) achieved an R² of 0.9951 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 99.88% and a Mean Absolute Error (MAE) of 0.0041, 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.
Keywords
Main Subjects
[1]. Bi, J., Jiang, H., & Ding, W. (2023). Analytical solution for calculating the flow rate in a lined tunnel with drainage systems. Tunnelling and Underground Space Technology, 138, 105132.
[2]. Amiri, V., & Asgari-Nejad, M. (2022). Hydrogeological assessment and estimation of groundwater inflow into the water transmission tunnel to Urmia Lake, Northwestern Iran. Bulletin of Engineering Geology and the Environment, 81(3), 111.
[3]. Hassani, A. N., Farhadian, H., & Katibeh, H. (2018). A comparative study on evaluation of steady-state groundwater inflow into a circular shallow tunnel. Tunnelling and Underground Space Technology, 73, 15–25.
[4]. Eftekhari, A., & Aalianvari, A. (2019). An overview of several techniques employed to overcome squeezing in mechanized tunnels; A case study. Geomechanics and Engineering, 18(2), 215-224.
[5]. Farhadian, H., & Nikvar-Hassani, A. (2019). Water flow into tunnels in discontinuous rock: A short critical review of the analytical solution of the art. Bulletin of Engineering Geology and the Environment, 78, 3833–3849.
[6]. Katibeh, H., & Aalianvari, A. (2012). Common Approximations to the water inflow into Tunnels. Drainage systems, 75-88.
[7]. Jahanmiri, S., Aalianvari, A., & Abbaszadeh, M. (2024). Developing GEP tree-based, Neuro-Swarm, and whale Optimization Models for evaluating Groundwater Seepage into Tunnels: A Case Study. Journal of Mining and Environment, 15(4), 1409-1436.
[8]. Jiang, Q., Yao, C., Ye, Z., & Zhou, C. (2013). Seepage flow with free surface in fracture networks. Water Resources Research, 49(1), 176–186.
[9]. Li, X., Li, D., Xu, Y., & Feng, X. (2020). A DFN-based 3D numerical approach for modeling coupled groundwater flow and solute transport in fractured rock mass. International Journal of Heat and Mass Transfer, 149, 119179.
[10]. Liu, D., Xu, Q., Tang, Y., & Jian, Y. (2021). Prediction of water inrush in long-lasting shutdown karst tunnels based on the HGWO-SVR model. IEEE Access, 9, 6368–6378.
[11] Nikvar Hassani, A., Farhadian, H., & Katibeh, H. (2018). A comparative study on evaluation of steady-state groundwater inflow into a circular shallow tunnel. Tunneling and Underground Space Technology, 73, 15–25.
[12]. Rasmussen, T. C., & Yu, G. (2006). Determination of groundwater flownets, fluxes, velocities, and travel times using the complex variable boundary element method. Engineering Analysis with Boundary Elements, 30(12), 1030–1044.
[13]. Jing, L. (2003). A review of techniques, advances, and outstanding issues in numerical modelling for rock mechanics and rock engineering. International Journal of Rock Mechanics and Mining Sciences, 39(4), 283–353.
[14]. Li, D., Li, X., Li, C. C., Huang, B., Gong, F., & Zhang, W. (2009). Case studies of groundwater flow into tunnels and an innovative water-gathering system for water drainage. Tunneling and Underground Space Technology, 24(3), 260–268.
[15]. Nikakhtar, L., & Zare, S. (2020). Evaluation of underground water flow into Tabriz metro tunnel first line by hydro-mechanical coupling analysis. Journal of Rock Mechanics and Geotechnical Engineering, 14(1), 1–7.
[16]. Wu, Q., Wang, M., & Wu, X. (2004). Investigations of groundwater bursting into coal mine seam floors from fault zones. International Journal of Rock Mechanics and Mining Sciences, 41(4), 557–571.
[17]. Surinaidu, L., Gurunadha Rao, V. V. S., Srinivasa Rao, N., & Srinu, S. (2014). Hydrogeological and groundwater modeling studies to estimate the groundwater inflows into the coal mines at different mine development stages using MODFLOW, Andhra Pradesh, India. Water Resources and Industry, 7–8, 49–65.
[18]. Gholizadeh, H., Behrouj Peely, A., Karney, B. W., & Malekpour, A. (2020). Assessment of groundwater ingress to a partially pressurized water-conveyance tunnel using a conduit-flow process model: A case study in Iran. Hydrogeology Journal, 28(7), 2573–2585.
[19]. Lianchong, L., Tianhong, Y., Zhengzhao, L., Wancheng, Z., & Chunan, T. (2011). Numerical investigation of groundwater outbursts near faults in underground coal mines. International Journal of Coal Geology, 85(3), 276–288.
[20]. Farhadian, H., Katibeh, H., & Huggenberger, P. (2016). Empirical model for estimating groundwater flow into tunnels in discontinuous rock masses. Environmental Earth Sciences, 75(6), 471. https://doi.org/10.1007/s12665-016-5339-2
[21]. Chen, Y., Selvadurai, A. P. S., & Liang, W. (2019). Computational modelling of groundwater inflow during a longwall coal mining advance: A case study from the Shanxi province, China. Rock Mechanics and Rock Engineering, 52(3), 917–934.
[22]. Li, Q., Ito, K., Wu, Z., Lowry, C. S., & Loheide, S. P. (2009). COMSOL multiphysics: A novel approach to groundwater modeling. Ground Water, 47(4), 480–487.
[23]. Li, S., et al. (2017). Gaussian process model of water inflow prediction in tunnel construction and its engineering applications. Tunneling and Underground Space Technology, 69, 155–161.
[24]. Chen, J., Zhou, M., Zhang, D., Huang, H., & Zhang, F. (2021). Quantification of water inflow in rock tunnel faces via convolutional neural network approach. Automation in Construction, 123, 103526.
[25]. Yang, Z. (2016). Risk prediction of water inrush of karst tunnels based on BP neural network. MMME, (unpublished).
[26]. Donglin, D., Wenjie, S., & Sha, X. (2012). Water-inrush assessment using a GIS-based Bayesian network for the 12-2 coal seam of the Kailuan Donghuantuo Coal Mine in China. Mine Water and the Environment, 31(2), 138–146.
[27]. Mahmoodzadeh, A., et al. (2021). Presenting the best prediction model of water inflow into drill and blast tunnels among several machine learning techniques. Automation in Construction, 127, 103719.
[28]. Chen, S., & Dong, S. (2020). A sequential structure for water inflow forecasting in coal mines integrating feature selection and multi-objective optimization. IEEE Access, 8, 183619–183632.
[29]. Farhadian, H., Nikvar Hassani, A., & Katibeh, H. (2017). Groundwater inflow assessment to Karaj Water Conveyance tunnel, northern Iran. KSCE Journal of Civil Engineering, 21, 2429–2438.
[30]. Zhang, P., Wu, H.-N., Chen, R.-P., & Chan, T. H. T. (2020). Hybrid meta-heuristic and machine learning algorithms for tunneling-induced settlement prediction: A comparative study. Tunneling and Underground Space Technology, 99, 103383.
[31]. Adnan, R. M., Mostafa, R. R., Kisi, O., Yaseen, Z. M., Shahid, S., & Zounemat-Kermani, M. (2021). Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization. Knowledge-Based Systems, 230, 107379.
[32]. Gordan, B., Jahed Armaghani, D., Hajihassani, M., & Monjezi, M. (2016). Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Engineering Computations, 32, 85–97.
[33]. Kashani, A. R., Chiong, R., Mirjalili, S., & Gandomi, A. H. (2021). Particle swarm optimization variants for solving geotechnical problems: Review and comparative analysis. Archives of Computational Methods in Engineering, 28, 1871–1927.
[34]. De Jesus, K. L. M., Senoro, D. B., Dela Cruz, J. C., & Chan, E. B. (2022). Neuro-particle swarm optimization based in-situ prediction model for heavy metals concentration in groundwater and surface water. Toxics, 10(2), 95.
[35]. Onyelowe, K. C., et al. (2023). Selected AI optimization techniques and applications in geotechnical engineering. Cogent Engineering, 10(1), 2153419.
[36]. Chao, Z., Ma, G., He, K., & Wang, M. (2021). Investigating low-permeability sandstone based on physical experiments and predictive modeling. Underground Space, 6(4), 364–378.
[37]. Zhao, S., Wang, L., & Cao, M. (2024). Chaos Game Optimization-Hybridized Artificial Neural Network for predicting blast-induced ground vibration. Applied Sciences, 14(9), 3759.
[38]. Zhou, J., Qi, H., Peng, K., Zhang, Y., & Khandelwal, M. (2024). Comprehensive review and future perspectives on prediction and mitigation of tunnel-induced ground settlement: A bibliometric analysis and methodological overview (2002–2022). Tunneling and Underground Space Technology, 154, 106081.
[39]. Mahmoodzadeh, A., Ghafourian, H., Mohammed, A. H., Rezaei, N., Ibrahim, H. H., & Rashidi, S. (2023). Predicting tunnel water inflow using a machine learning-based solution to improve tunnel construction safety. Transportation Geotechnics, 40, 100978.
[40]. Zhu, L., Qiao, P., Jiang, F., Shen, C., & Jiang, Y. (2024). Detection method of tunnel surrounding rock leakage channel based on improved chaotic particle swarm optimization algorithm. Stavební Obzor - Engineering Journal, 33(3), 321–334.
[41]. Jiang, A., Yang, X., Xu, M., & Jiang, T. (2021). Coupled hydrologic-mechanical-damage analysis and its application to diversion tunnels of hydropower station. Advances in Civil Engineering, 2021, 8341528.
[42]. Mousavirad, S. J., Ebrahimpour-Komleh, H., & Schaefer, G. (2019). Effective image clustering based on human mental search. Applied Soft Computing, 78, 209–220.
[43]. Mousavirad, S. J., & Ebrahimpour-Komleh, H. (2017). Human mental search: A new population-based metaheuristic optimization algorithm. Applied Intelligence, 47, 850–887.
[44]. Mousavirad, S. J., Schaefer, G., & Ebrahimpour-Komleh, H. (2021). The human mental search algorithm for solving optimization problems. In Enabling AI Applications and Data Science (pp. 27–47). Springer.
[45]. Mousavirad, S. J., & Ebrahimpour-Komleh, H. (2020). Human mental search-based multilevel thresholding for image segmentation. Applied Soft Computing, 97, 105427.
[46]. Mousavirad, S. J., Ebrahimpour-Komleh, H., & Schaefer, G. (2020). Automatic clustering using a local search-based human mental search algorithm for image segmentation. Applied Soft Computing, 96, 106604.
[47]. Mousavirad, S. J., Schaefer, G., & Korovin, I. (2019). A global-best guided human mental search algorithm with random clustering strategy. In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) (pp. 3174–3179).
[48]. Mousavirad, S. J., Schaefer, G., Esmaeili, L., & Korovin, I. (2020). On improvements of the human mental search algorithm for global optimisation. In 2020 IEEE Congress on Evolutionary Computation (CEC) (pp. 1–8).
[49]. Mousavirad, S. J., Schaefer, G., Fang, H., Liu, X., & Korovin, I. (2020). Colour quantisation by human mental search. In Advances in Swarm Intelligence: 11th International Conference, ICSI 2020, Belgrade, Serbia, July 14–20, 2020, Proceedings 11 (pp. 130–141).
[50]. Mousavirad, S. J., Schaefer, G., Celebi, M. E., Fang, H., & Liu, X. (2020). Colour quantisation using human mental search and local refinement. In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 3045–3050).
[51]. Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)?—Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247–1250.
[52]. Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79–82.
[53]. Jahanmiri, S., Asadizadeh, M., Alipour, A., & Nowak, S. (2021). Predicting the contribution of the mining sector to the Gross Domestic Product (GDP) index utilizing heuristic approaches. Applied Artificial Intelligence, 00(00), 1–23.
[54]. Marmolin, H. (1986). Subjective MSE measures. IEEE Transactions on Systems, Man, and Cybernetics, SMC-16(3), 486–489.
)