This study explores the seismic performance of discontinuous broken-back block-type gravity quay walls with varying configurations using the finite element method (FEM). For this purpose, plane strain FE meshes are constructed by integrating adaptive meshing and error-based adaptivity techniques. To simulate discontinuities along the wall height and the interaction between the wall and the neighboring medium, interface elements are comprehensively applied between the concrete blocks and between the wall and the surrounding soils. The developed FE models are initially validated against observations from corresponding 1g shaking table tests available in the literature. The primary objective is to evaluate the effectiveness of different inclination angles of the wall body, its base block, and the underlying seabed soil layer in mitigating seismic deformations and lateral earth pressures. FE results reveal that simultaneously inclining the wall body, base block, and seabed layer by 9° significantly reduces permanent horizontal displacement, base sliding, and settlement of the wall. Under a worst-case seismic scenario with a PGA of 0.9g and a frequency of 3 Hz, these deformation parameters decrease by 37.26%, 14.47%, and 48.88%, respectively, compared to the non-inclined quay wall. The suggested mitigation approach improves the overall stability and serviceability of ports and their inland infrastructures during seismic and post-seismic events. This study also develops a predictive polynomial model utilizing Gene Expression Programming (GEP) based on 630 data points derived from validated FE models. The GEP model aims to predict the horizontal displacement of both inclined and non-inclined broken-back block-type quay walls. The novelty of the current research lies in the investigation of inclined configurations as a remediation technique to mitigate deformations and enhance the seismic resilience and performance of broken-back block-type quay walls, utilizing an advanced FE modeling approach. Additionally, a practical machine learning-based model is proposed to estimate the wall's seismic deformations.
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