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<p>Coastal cliffs are vital geological features that support unique ecosystems but are increasingly threatened by rising sea levels and intensified storm activity, which amplify the impact of wave action on coastal dynamics. These extreme vulnerabilities highlight the importance of developing comprehensive frameworks for erosion monitoring and coastal defence, ensuring sustainable management and protection of these critical landscapes. This study presents a fluid–structure interaction framework for analysing cliff stability, integrating light detection and ranging (LiDAR) monitoring, physical experimentation, and numerical modelling. Using the strength reduction method and 3D cliff profile analysis, the framework accurately identifies critical profiles and failure-prone zones. Sensitivity analysis with random parameters highlights density, internal friction angle, and cohesion as the most influential material properties affecting cliff retreat. Validated numerical simulations demonstrate strong agreement with observed deformation patterns, confirming model reliability and predictive capability. The framework also enables the identification of critical cliff profiles based on their shape and structure, and further determines high-risk zones in the profiles by incorporating local material properties and wave conditions. The framework is scalable and adaptable to diverse coastal settings using updated LiDAR, localised wave conditions, and material data. These findings support data-driven strategies for erosion mitigation, offering practical applications for engineering design, coastal planning, and policy development aimed at enhancing shoreline resilience.</p>

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A practical method to predict the critical zone of coastal cliffs at high risk of failure considering the effects of fluid–structure interaction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月23日

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  • Ali Khosravifardshirazi,
  • Akbar A. Javadi,
  • Ali Johari,
  • Mohammad Akrami

摘要

Coastal cliffs are vital geological features that support unique ecosystems but are increasingly threatened by rising sea levels and intensified storm activity, which amplify the impact of wave action on coastal dynamics. These extreme vulnerabilities highlight the importance of developing comprehensive frameworks for erosion monitoring and coastal defence, ensuring sustainable management and protection of these critical landscapes. This study presents a fluid–structure interaction framework for analysing cliff stability, integrating light detection and ranging (LiDAR) monitoring, physical experimentation, and numerical modelling. Using the strength reduction method and 3D cliff profile analysis, the framework accurately identifies critical profiles and failure-prone zones. Sensitivity analysis with random parameters highlights density, internal friction angle, and cohesion as the most influential material properties affecting cliff retreat. Validated numerical simulations demonstrate strong agreement with observed deformation patterns, confirming model reliability and predictive capability. The framework also enables the identification of critical cliff profiles based on their shape and structure, and further determines high-risk zones in the profiles by incorporating local material properties and wave conditions. The framework is scalable and adaptable to diverse coastal settings using updated LiDAR, localised wave conditions, and material data. These findings support data-driven strategies for erosion mitigation, offering practical applications for engineering design, coastal planning, and policy development aimed at enhancing shoreline resilience.