Computers and Concrete

  • 제33권5호
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  • Pages.545-557
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  • 2024
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Insights from LDPM analysis on retaining wall failure

  • 투고 : 2023.12.01
  • 심사 : 2024.02.01
  • 발행 : 2024.05.25
⟨ 이전 논문 다음 논문 ⟩

초록

A real-case incident occurred where a 9-meter-high segment of a pre-fabricated concrete separation wall unexpectedly collapsed. This collapse was triggered by improperly depositing excavated soil against the wall's back, a condition for which the wall segments were not designed to withstand lateral earth pressure, leading to a flexural failure. The event's analysis, integrating technical data and observational insights, revealed that internal forces at the time of failure significantly exceeded the wall's capacity per standard design. The Lattice Discrete Particle Model (LDPM) further replicates the collapse mechanism. Our approach involved defining various parameter sets to replicate the concrete's mechanical response, consistent with the tested compressive strength. Subsequent stages included calibrating these parameters across different scales and conducting full-scale simulations. These simulations carried out with various parameter sets, were thoroughly analyzed to identify the most representative failure mechanism. We developed an equation from this analysis that quickly correlates the parameters to the wall's load-carry capacity, aligned with the simulation. Additionally, our study examined the wall's post-peak behavior, extending up to the point of collapse. This aspect of the analysis was essential for preventing failure, providing crucial time for intervention, and potentially averting a disaster. However, the reinforced concrete residual state is far from being fully understood. While it's impractical for engineers to depend on the residual state of structural elements during the design phase, comprehending this state is essential for effective response and mitigation strategies after initial failure occurs.

키워드

과제정보

We acknowledge the Ariel HPC Center at Ariel University for providing computing resources that contribute to the research results reported in this paper. Special thanks are extended to Dr. Amit Kashi and Dr. Amir M. Michaelis for their consistent support and expertise in HPC and its applications.

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