Parametric Study on Dynamic Response of FRP Masonry Structures under the Impacts of Debris Flow

Li, Peizhen; Li, Tangzhenhao; Lu, Zheng; Li, Jin

doi:10.1155/2018/4527571

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…Besides permanent and functional loads, there is a wide literature on the seismic behavior of masonry walls, however there are less works on the impact behavior of wall structures. The stress-strain response of reinforced masonry structures due to debris flow impact has been studied by [46]. Wall displacements due to boulder impacts were studied in [47], assuming different wall thicknesses and masonry mechanical characteristics.…”

Section: Wall Damagementioning

confidence: 99%

Deep Neural Networks for the Estimation of Masonry Structures Failures under Rockfalls

et al. 2023

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Although the principal aim of the rockfall management is to prevent rock boulders from reaching the buildings instead of the buildings resisting the boulder impacts, there usually exists a residual risk that has to be assessed, even when structural protection measurements are taken. The evaluation of the expected damage of buildings due to rockfalls using empirical data from past events is not always possible, as transferring and applying damage observations from one area to another can be unrealistic. In order to simulate potential rockfall scenarios and their damage on buildings, numerical methods can be an alternative. However due to their increased requirements in expertise and computational costs, their integration into the risk analysis is limited, and simpler tools to assess the rockfall vulnerability of buildings are needed. This paper focuses on the application of artificial intelligence AI methods for providing the expected damage of masonry walls which are subjected to rockfall impacts. First, a damage database with 672 datasets was created numerically using the particle finite element method and the finite element method. The input variables are the rock volume (VR), the rock velocity (RV), the masonry wall (t) and the masonry tensile strength fm. The output variable is a damage index (DI) equal to the percentage of the damaged wall area. Different AI algorithms were investigated and the ANN LM 4-21-1 model was selected to optimally assess the expected wall damage. The optimum model is provided here (a) as an analytical equation and (b) in the form of contour graphs, mapping the DI value. Known the VR and the RV, the DI can be directly used as an input for the vulnerability of masonry walls into the quantitative rockfall risk assessment equation.

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Section: Wall Damagementioning

confidence: 99%

Deep Neural Networks for the Estimation of Masonry Structures Failures under Rockfalls

et al. 2023

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“…e numerical simulation solves complex interaction problems involving nonlinear behaviors in dynamic conditions [22] and provides an effective way to explore the structural response, making it possible to obtain data required to analyze the vulnerability [23]. Based on the computational formula and experimental results about the flood impact, Xiao and Li [24] carried out a static analysis on the rural building numerically.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Simulation and Vulnerability Analysis of Masonry Structures Impacted by Flash Floods

Fang

Zhong

Zhang

et al. 2021

Shock and Vibration

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The vulnerability of buildings impacted by flash floods is often assessed by empirical approaches. However, the damage-developing mechanisms of buildings remain unclear. This study presented a nonlinear numerical analysis for the interaction between flash floods and masonry structures. A new Structured ALE (S-ALE) solver in an explicit finite element platform LS-DYNA was applied. Thirty different scenarios, including two different FE models, three different water depths, and five different flow velocities, were studied. Nonlinear structural responses and failure mechanisms of masonry structures were analyzed and compared. Results showed that due to the consideration of wall damage, the time evolution of the impact force on the entire building was distributed in a multipeak pattern. Under the impact of flash floods, the building was in a complex bending-shear state, and the overturning moment was the principal reason for the building damage. The critical role played by the structural measure was reaffirmed. Moreover, the physical vulnerability was quantified through a macroscopic damage index, the lateral drift ratio of the ground floor. It can be concluded that the physical vulnerability depends on both the local structural strength (local view) and the structural resistance hierarchies (global view).

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Dynamic response of buildings under debris flow impact

Liu,

Fan,

Tian

et al. 2024

J. Mt. Sci.

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Parametric Study on Dynamic Response of FRP Masonry Structures under the Impacts of Debris Flow

Cited by 4 publications

References 29 publications

Deep Neural Networks for the Estimation of Masonry Structures Failures under Rockfalls

Deep Neural Networks for the Estimation of Masonry Structures Failures under Rockfalls

Nonlinear Simulation and Vulnerability Analysis of Masonry Structures Impacted by Flash Floods

Dynamic response of buildings under debris flow impact

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