[1]. Samadi, H., Mahmoodzadeh, A., Elhag, A. B., Alanazi, A., Alqahtani, A., & Alsubai, S. (2025). Application of hybrid-optimized and stacking-ensemble labeled neural networks to predict water inflow in drill-and-blast tunnels. Tunnelling and Underground Space Technology, 156, 106273. doi: https://doi.org/10.1016/j.tust.2024.106273
[2]. Qi, B., Xu, P., & Wu, C. (2023). Analysis of the infiltration and water storage performance of recycled brick mix aggregates in sponge city construction. Water, 15(2), 363. doi: https://doi.org/10.3390/w15020363
[3]. Fu, Q., Gu, M., Yuan, J., & Lin, Y. (2022). Experimental study on vibration velocity of piled raft supported embankment and foundation for ballastless high speed railway. Buildings, 12(11), 1982. doi: https://doi.org/10.3390/buildings12111982
[4]. Park, K.-H., Owatsiriwong, A., & Lee, J.-G. (2008). Analytical solution for steady-state groundwater inflow into a drained circular tunnel in a semi-infinite aquifer: A revisit. Tunnelling and Underground Space Technology, 23(2), 206-209. doi: https://doi.org/10.1016/j.tust.2007.02.004
[5]. 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. Tunnelling and Underground Space Technology, 24(3), 260-268. doi: https://doi.org/10.1016/j.tust.2008.08.006
[6]. West, G. (1983). Comparisons between real and predicted geology in tunnels: examples from recent cases. Quarterly Journal of Engineering Geology and Hydrogeology, 16(2), 113-126. doi: https://doi.org/10.1144/GSL.QJEG.1983.016.02.04
[7]. Ghorbani, S., Bour, K., & Javdan, R. (2024). Evaluating the Efficiency of Pre-Grouting in Fault Zones of Tunnel and its Effect on Rock Mass Quality: A Case Study. Geotechnical and Geological Engineering, 42(7), 6641-6654. doi: https://doi.org/10.1007/s10706-023-02557-8
[8]. Ghorbani, S., Bour, K., Javdan, R., & Bour, M. (2023). Design of Effective Grouting Pattern in Kerman Water Conveyance Tunnel Using DFN-DEM and Analytical Approaches. International Journal of Geosynthetics and Ground Engineering, 9(2), 19. doi: https://doi.org/10.1007/s40891-023-00441-2
[9]. Ghorbani, S., Bour, K., & Javdan, R. (2024). Investigating the sealing efficiency of grouting in joints: insights from effects of the rheological properties of grout and joint characteristics. Geomechanics and Geoengineering, 19(6), 1021-1037. doi: https://doi.org/10.1080/17486025.2024.2340117
[10]. Ghorbani, S., Bour, K., & Javdan, R. (2025). Applying the PROMETHEE II, WASPAS, and CoCoSo models for assessment of geotechnical hazards in TBM tunneling. Scientific Reports, 15(1), 491. doi: https://doi.org/10.1038/s41598-024-84826-x
[11]. Zhang, D., Fang, Q., & Lou, H. (2014). Grouting techniques for the unfavorable geological conditions of Xiang’an subsea tunnel in China. Journal of Rock Mechanics and Geotechnical Engineering, 6(5), 438-446. doi: https://doi.org/10.1016/j.jrmge.2014.07.005
[12]. Golian, M., Teshnizi, E. S., & Nakhaei, M. (2018). Prediction of water inflow to mechanized tunnels during tunnel-boring-machine advance using numerical simulation. Hydrogeology Journal, 26(8). doi: https://doi.org/10.1007/s10040-018-1835-x
[13]. Li, S., He, P., Li, L., Shi, S., Zhang, Q., Zhang, J., & Hu, J. (2017). Gaussian process model of water inflow prediction in tunnel construction and its engineering applications. Tunnelling and Underground Space Technology, 69, 155-161. doi: https://doi.org/10.1016/j.tust.2017.06.018
[14]. Liu, D., Xu, Q., Tang, Y., & Jian, Y. (2020). Prediction of water inrush in long-lasting shutdown karst tunnels based on the HGWO-SVR model. IEEE Access (Vol. 9). IEEE.
[15]. 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. doi: https://doi.org/10.1016/j.trgeo.2023.100978
[16]. Zhou, J., Zhang, Y., Li, C., Yong, W., Qiu, Y., Du, K., & Wang, S. (2023). Enhancing the performance of tunnel water inflow prediction using Random Forest optimized by Grey Wolf Optimizer. Earth Science Informatics, 16(3), 2405-2420. doi: https://doi.org/10.1007/s12145-023-01042-3
[17]. 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. doi: https://doi.org/10.22044/jme.2024.13601.2513
[18]. Shen, Q., Yang, H., Zhou, Z., Chen, Z., & Zhang, Y. (2025). Simulation and parameter identification of water inrush in tunnel construction using physics-informed neural networks. Bulletin of Engineering Geology and the Environment, 84(7), 370. doi: https://doi.org/10.1007/s10064-025-04381-1
[19]. 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. doi: https://doi.org/10.1016/j.autcon.2020.103526
[20]. Ju, S., Ou, G., Peng, T., Wang, Y., Song, Q., & Guan, P. (2025). Tunnel water inflow prediction using explainable machine learning and augmented partially missing dataset. Frontiers in Earth Science, 13, 1590203. doi: https://doi.org/10.3389/feart.2025.1590203
[21]. Khatti, J., & Polat, B. Y. (2024). Assessment of short and long-term pozzolanic activity of natural pozzolans using machine learning approaches. Structures, 68, 107159. doi: https://doi.org/10.1016/j.istruc.2024.107159
[22]. Ghorbani, E., & Yagiz, S. (2024). Estimating the penetration rate of tunnel boring machines via gradient boosting algorithms. Engineering Applications of Artificial Intelligence, 136, 108985. doi: https://doi.org/10.1016/j.engappai.2024.108985
[23]. Chen, T. (2015). Xgboost: extreme gradient boosting. R package version 0.4-2, 1(4).
[24]. Zhu, X., Chu, J., Wang, K., Wu, S., Yan, W., & Chiam, K. (2021). Prediction of rockhead using a hybrid N-XGBoost machine learning framework. Journal of Rock Mechanics and Geotechnical Engineering, 13(6), 1231-1245. doi: https://doi.org/10.1016/j.jrmge.2021.06.012
[25]. Maleki, Z., Farhadian, H., & Nikvar-Hassani, A. (2021). Geological Hazard in Tunnelling: The Example of Gelas Water Conveyance Tunnel in Iran. Quarterly Journal of Engineering Geology and Hydrogeology, 54(1), qjegh2019-114.
[26]. Farhadian, H., & Shahraki, F. B. (2024). Enhancing analytical methods for estimating water inflow to tunnels in the presence of discontinuity areas. Environmental Earth Sciences, 83(10), 339. doi: https://doi.org/10.1007/s12665-024-11651-w
[27]. Hassanpour, J., Ghaedi Vanani, A. A., Rostami, J., & Cheshomi, A. (2016). Evaluation of common TBM performance prediction models based on field data from the second lot of Zagros water conveyance tunnel (ZWCT2). Tunnelling and Underground Space Technology, 52, 147-156. doi: https://doi.org/10.1016/j.tust.2015.12.006
[28]. Farhadian, H., & Katibeh, H. (2017). New empirical model to evaluate groundwater flow into circular tunnel using multiple regression analysis. International Journal of Mining Science and Technology, 27(3), 415-421. doi: https://doi.org/10.1016/j.ijmst.2017.03.005
[29]. Zhou, J., Qiu, Y., Khandelwal, M., Zhu, S., & Zhang, X. (2021). Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations. International Journal of Rock Mechanics and Mining Sciences, 145, 104856. doi: https://doi.org/10.1016/j.ijrmms.2021.104856
[30]. Thamboo, J., Sathurshan, M., & Zahra, T. (2024). Reliable unit strength correlations to predict the compressive strength of grouted concrete masonry. Materials and Structures, 57(7), 151. doi: https://doi.org/10.1617/s11527-024-02417-8
[31]. Alkayem, N. F., Shen, L., Mayya, A., Asteris, P. G., Fu, R., Di Luzio, G., Strauss, A., Cao, M. (2024). Prediction of concrete and FRC properties at high temperature using machine and deep learning: a review of recent advances and future perspectives. Journal of Building Engineering, 83, 108369. doi: https://doi.org/10.1016/j.jobe.2023.108369
[32]. Kocak, B., Pınarcı, İ., Güvenç, U., & Kocak, Y. (2023). Prediction of compressive strengths of pumice-and diatomite-containing cement mortars with artificial intelligence-based applications. Construction and Building Materials, 385, 131516. doi: https://doi.org/10.1016/j.conbuildmat.2023.131516
[33]. Fissha, Y., Khatti, J., Ikeda, H., Grover, K. S., Owada, N., Toriya, H., Adachi, T., Kawamura, Y. (2024). Predicting ground vibration during rock blasting using relevance vector machine improved with dual kernels and metaheuristic algorithms. Scientific Reports, 14(1), 20026. doi: https://doi.org/10.1038/s41598-024-70939-w
[34]. Wu, X., Feng, Z., Liu, J., Chen, H., & Liu, Y. (2024). Predicting existing tunnel deformation from adjacent foundation pit construction using hybrid machine learning. Automation in Construction, 165, 105516. doi: https://doi.org/10.1016/j.autcon.2024.105516
[35]. Ghorbani, S., & Bameri, A. (2025). Hybrid Machine Learning Models to Predict the Uniaxial Compressive Strength of Rocks Based on Non-Destructive Tests. Transportation Infrastructure Geotechnology, 12(5), 148. doi: https://doi.org/10.1007/s40515-025-00603-x
