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<p>The need for accurate seismic earth pressure solutions in earthquake geotechnical engineering demands a cost-effective method. This paper employs a three-stability-factor approach to determine seismic earth pressures by considering cohesion, surcharge, and unit weight effects that is analogous to Terzaghi’s traditional superposition method for bearing capacity determination proposed by Terzaghi. To achieve this goal, adaptive finite element limit analysis is used to explore seismic earth pressure intricacies using both upper-bound and lower-bound approaches. Numerical findings highlight the influence of internal friction angle, wall roughness, and surcharge pressure on seismic earth pressure factors. Distinct failure mechanisms of smooth and rough retaining walls under seismic loads offer vital insights for practical design. Incorporating cutting-edge machine learning techniques such as Bayesian regularization feed forward neural network and multivariate adaptive regression splines, a series of closed-form solutions for stability factors is established. These data-driven solutions ensure precision, simplicity, and efficiency in determining seismic lateral earth pressure. This approach transcends theoretical boundaries, providing insights for designing stable retaining walls in seismic zones. Rigorous validation against published results confirms the accuracy and reliability of the developed solutions. This research represents a significant advancement in seismic design methodologies, contributing to enhanced infrastructure resilience in the face of seismic challenges.</p>

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Closed-form seismic earth pressure solutions via adaptive limit analysis and hybrid learning models

  • 期刊论文

  • 发表日期：

    2025年12月3日

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  • Tan Nguyen,
  • Jim Shiau,
  • Tram Bui-Ngoc

摘要

The need for accurate seismic earth pressure solutions in earthquake geotechnical engineering demands a cost-effective method. This paper employs a three-stability-factor approach to determine seismic earth pressures by considering cohesion, surcharge, and unit weight effects that is analogous to Terzaghi’s traditional superposition method for bearing capacity determination proposed by Terzaghi. To achieve this goal, adaptive finite element limit analysis is used to explore seismic earth pressure intricacies using both upper-bound and lower-bound approaches. Numerical findings highlight the influence of internal friction angle, wall roughness, and surcharge pressure on seismic earth pressure factors. Distinct failure mechanisms of smooth and rough retaining walls under seismic loads offer vital insights for practical design. Incorporating cutting-edge machine learning techniques such as Bayesian regularization feed forward neural network and multivariate adaptive regression splines, a series of closed-form solutions for stability factors is established. These data-driven solutions ensure precision, simplicity, and efficiency in determining seismic lateral earth pressure. This approach transcends theoretical boundaries, providing insights for designing stable retaining walls in seismic zones. Rigorous validation against published results confirms the accuracy and reliability of the developed solutions. This research represents a significant advancement in seismic design methodologies, contributing to enhanced infrastructure resilience in the face of seismic challenges.