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Volume 13 | Issue 9 | Year 2026 | Article Id. IJCE-V13I9P104 | DOI : https://doi.org/10.14445/23488352/IJCE-V13I9P104

Monte Carlo and Neural Network-based Prediction of Base Shear Force in Airport Tunnels using the Pushover Method Considering Both Soil-Structure Interaction and Non-Interaction across Different Soil Types

Awatif EL BRIGUI, Mouna EL MKHALET, Nouzha LAMDOUAR

Received Revised Accepted Published
17 Jan 2026 20 Feb 2026 05 Mar 2026 29 Sep 2026

Citation :

Awatif EL BRIGUI, Mouna EL MKHALET, Nouzha LAMDOUAR, "Monte Carlo and Neural Network-based Prediction of Base Shear Force in Airport Tunnels using the Pushover Method Considering Both Soil-Structure Interaction and Non-Interaction across Different Soil Types," International Journal of Civil Engineering, vol. 13, no. 9, pp. 47-76, 2026. Crossref, https://doi.org/10.14445/23488352/IJCE-V13I9P104

Abstract

Artificial Neural Networks (ANNs) are powerful nonlinear function approximators that can learn complicated relationships from data. They offer increasing value in civil, seismic, and geotechnical engineering applications. Recent findings indicate that ANNs can also be applied to structural health monitoring, material behaviour prediction and the evaluation of deep buried structures, whereas their application to airport tunnels remains largely unexplored. This study expands the Monte Carlo framework developed for the seismic assessment of an airport tunnel. It integrates the principles of a neural network. 200 samples are generated for each scenario by incorporating uncertainties of peak ground acceleration and material strength into Monte Carlo simulations. The findings indicate a significant variation in the structure’s roof displacement and base shear and larger dispersion of these parameters is observed as the ground acceleration increases. In the case without SSI, the roof displacement varies between 1.40 and 3.44 cm while the base shear varies from 690.0 to 1,845.1 kN; these are the minimum and maximum values obtained over the 200 Monte Carlo samples and correspond exactly to the figures reported in Table 4. The performance of tunnels is sensitive to the interdependence between seismic input and material. As a whole, the results obtained by combining ANN concepts, nonlinear static procedures, and probabilistic Monte Carlo simulation yield a sound framework for evaluation of performance under uncertainties. A combination of data analysis and modelling would enhance accuracy and strengthen decision-making for airport tunnel design and safety management.

Keywords

Artificial Neural Networks, Monte Carlo simulations, Nonlinear static analyses, Pushover methods, Seismic responses.

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