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

• Published in: Geosciences (Basel) 2023-06, Vol.13 (6), p.156
• Main Authors: Mavrouli, Olga, Skentou, Athanasia D., Carbonell, Josep Maria, Tsoukalas, Markos Z., Núñez-Andrés, M. Amparo, Asteris, Panagiotis G.
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; Boulders; Building damage; Buildings; Concrete; Cost analysis; Earth science; Earthquakes; Empirical analysis; failure; Finite element method; Graphs; Impact damage; Landslides & mudslides; machine learning; Masonry; masonry structures; Mathematical models; Methods; Neural networks; Numerical analysis; Numerical methods; Optimization; optimization algorithms; Reinforced concrete; Risk analysis; Risk assessment; Risk management; Rock; Rock falls; Rockfall; rockfalls; Rocks; Rockslides; Safety and security measures; Seismic engineering; Tensile strength; Vulnerability; Walls
• ISSN: 2076-3263; 2076-3263
• DOI: 10.3390/geosciences13060156

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.