Showing papers by "Yun Wook Choo published in 2022"
TL;DR: Wang et al. as mentioned in this paper developed a machine learning (ML) method to predict the maximum wall deflection of deep braced excavations in sand, which has not yet received much attention.
3 citations
TL;DR: In this paper , the performance of the fish anchor in silty sand deposits with the aim of potentially using the anchor in supporting floating wind turbines in shallow water sandy seabed was assessed.
TL;DR: In this article , a response-surface-based model updating technique for the nonlinear three-dimensional simulation of the centrifugal testing model of strutted deep excavation in sand is presented.
Abstract: Centrifugal tests provide an efficacious experimental process to predict the behavior of deep excavations, and numerical models are indispensable for demonstrating the test results and analyzing the engineering demand parameters. Uncertainty in material properties can cause simulations to differ from tests; therefore, updating the model becomes inevitable. This study presents a response-surface-based model updating technique for the nonlinear three-dimensional simulation of the centrifugal testing model of strutted deep excavation in sand. An overview of the fundamentals of the response-surface model is provided, including selecting uncertain parameters as input factors, creating a design order for training the model, building a second-order polynomial surface, and updating the input factors through targeted centrifugal results. The bending strains of diaphragm wall panels at multiple points along the depth are used to form the multiobjective function. Response-surface model predictions were well-matched with actual numerical responses, with less than a 0.5% difference. Parametric analyses could be conducted utilizing this updated strutted deep excavation model.