Computational Method for Designing the Retaining Reinforcement Concrete Wall Under Hydrodynamic Load in Marine

Abstract

Health monitoring and damage detection for important and special infrastructures, especially marine structures, are one of the important challenges in structural engineering because they are subjected to corrosion and hydrodynamic loads. Simulation of marine structures under corrosion and hydraulic loads is complex; thus, a combination of point cloud data sets, validation finite element model, parametric studies, and machine-learning methods was used in this study to estimate the damaged surface of retaining reinforced concrete walls (RRCWs) and the load-carrying capacity of RRCWs according to design parameters of RRCWs. After validation of the finite element method (FEM), 144 specimens were simulated using the FEM and the obtained displacement-control loading. Compressive strength, thickness of RRCWs, strength of reinforcement bars, and ratio of reinforcement bars were considered as the design parameters. The results show that the thickness of RRCWs has the most effect on decreasing the damaged surface and load-carrying capacity. Furthermore, the results demonstrate that Gene Expression Programming (GEP) performs better than all models and can predict the damaged surface and load-carrying capacity with 99% and 97% accuracy, respectively. Moreover, by decreasing the thickness of RRCWs, the damaged surface is reduced to 2.5%, and by increasing the thickness, the load-carrying capacity is increased to 51%–59%.

Keywords

damage detection / finite element method / machine-learning methods / marine structure / point cloud / retaining reinforced concrete wall

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