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A neural network approach to estimating deflection of diaphragm walls caused by excavation in clays

机译：用神经网络方法估算黏土开挖引起的隔板壁挠度

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摘要

An artificial neural network (ANN)-based approach for predicting deflection of diaphragm walls caused by braced excavation in soft to medium clays is presented in this study. Five input variables, including excavation depth, system stiffness, excavation width, shear strength normalized with vertical effective stress, and Young's modulus normalized with vertical effective stress, are adopted as inputs to the ANN. The database for training and testing the ANN is generated from hypothetical cases using finite element method. The performance of the developed ANN reveals that the influence of each input variable on the wall deflection is consistent with the excavation behaviors generally observed in the field. The validation using 12 excavation case histories collected in this study shows that the wall deflection caused by braced excavation can be accurately predicted by the developed ANN.
机译：在这项研究中，提出了一种基于人工神经网络（ANN）的方法，该方法可预测软撑至中型粘土的支撑开挖引起的隔板壁挠度。 ANN的输入采用了五个输入变量，包括开挖深度，系统刚度，开挖宽度，通过垂直有效应力归一化的剪切强度和通过垂直有效应力归一化的杨氏模量。用于训练和测试ANN的数据库是使用有限元方法从假设的案例中生成的。改进的人工神经网络的性能表明，每个输入变量对墙体挠度的影响与现场通常观察到的开挖行为是一致的。使用本研究收集的12个开挖案例历史进行的验证表明，发达的ANN可以准确预测支撑开挖引起的墙体变形。

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