Accurate prediction of structural forces in segmental tunnel linings is essential for ensuring the safety and longevity of underground infrastructure. This study presents an innovative approach that leverages Finite Element Method (FEM) enhanced artificial neural networks (ANNs) to efficiently predict structural forces in segmental tunnel linings. By integrating the capabilities of FEM with the computational power of ANNs, this method provides a robust and precise predictive model. The FEM-ANN model is trained using a dataset comprising various tunnel geometries, loading conditions, and material properties, ensuring its versatility and applicability to a wide range of tunneling projects. The model's performance is validated through rigorous testing and comparison with traditional analytical methods, demonstrating its superior predictive accuracy and efficiency. The proposed FEM-ANN approach offers a valuable tool for engineers and researchers engaged in tunnel design and construction, enabling them to optimize structural designs and enhance tunneling safety.

Segmental tunnel linings, Structural forces prediction, Finite Element Method (FEM)

Arnau, O., & Molins, C. (2011). Experimental and analytical study of the structural response of segmental tunnel linings based on an in situ loading test. Part 2: Numerical simulation. Tunnelling and Underground Space Technology,26(6), 778-788.

Arnau, O., & Molins, C. (2012). Three dimensional structural response of segmental tunnel linings. Engineering Structures, 44, 210-221.

Augarde, C., & Burd, H. (2001). Three‐dimensional finite element analysis of lined tunnels. International Journal for Numerical and Analytical Methods in Geomechanics, 25(3), 243-262.

Beiki, M., Majdi, A., & Givshad, A. D. (2013). Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks. International Journal of Rock Mechanics and Mining Sciences, 63, 159-169.

Benardos, A., & Kaliampakos, D. (2004). Modelling TBM performance with artificial neural networks. Tunnelling and Underground Space Technology, 19(6), 597-605.

Bilotta, E., & Russo, G. (2013). Internal forces arising in the segmental lining of an earth pressure balance-bored tunnel. Journal of Geotechnical and Geoenvironmental Engineering, 139(10), 1765-1780.

Chen, J., & Mo, H. (2009). Numerical study on crack problems in segments of shield tunnel using finite element method. Tunnelling and Underground Space Technology, 24(1), 91-102.

Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of control, signals and systems, 2(4), 303-314.

Den Hartog, M., Babuška, R., Deketh, H., Grima, M. A., Verhoef, P., & Verbruggen, H. B. (1997). Knowledge-based fuzzy model for performance prediction of a rock-cutting trencher. International Journal of Approximate Reasoning, 16(1), 43-66.

Do, N.-A., Dias, D., Oreste, P., & Djeran-Maigre, I. (2015). 2D numerical investigation of segmental tunnel lining under seismic loading. Soil Dynamics and Earthquake Engineering, 72, 66-76.