Direct Field Calibration for Model Simulations of Deep Excavations

岩土工程问题的数值模拟在重大建设项目中经常使用。 这些模型和模拟涉及明挖、隧道、斜坡和类似工程结构的分阶段施工的非线性分析。 这些计算机模拟中最重要和最困难的部分是土层本构行为的表示。在当前的工程实践中，工程师选择现有的本构模型并校准其参数以匹配少数实验室材料测试的结果。这些测试不会生成与现场问题相关的土壤行为重要方面的信息。使用校准本构模型的数值模拟结果通常与现场测量不匹配。使用临时方法来选择和调整本构模型及其属性，以匹配现场性能。我们提出了一种新颖、强大且系统的方法，可以直接根据现场测量来校准土壤行为的本构模型。我们将应用自动渐进方法； Ghaboussi 和他的同事提出了一种基于神经网络的方法，用于对深支撑挖掘的分阶段施工进行建模。 神经网络（NN）材料模型将代表土壤行为的本构模型，并将使用实验室测试和观察到的挖掘现场行为进行校准。最初，所提出的方法将应用于从深基坑数值模拟中综合生成的“现场测量”。综合数据将包括墙体横向位移和地表沉降。将使用经典的边界表面塑性模型来表示粘土行为以生成合成数据。作为对所提出方法的验证，可以将经过训练的神经网络本构模型计算出的土壤行为与经典土壤模型进行比较。然后，所提出的方法将应用于波士顿中央动脉/隧道 CA/T 项目深基坑的现场测量。 神经网络材料将直接从变形的现场测量中学习土壤行为的本构模型。所提出的方法可以应用于露天挖掘以外的问题。该方法将有可能极大地增强岩土工程问题的数值模拟。然后可以将现场观察和“当地经验”直接系统地纳入数值模型中。

Numerical modeling of geotechnical problems is used routinely in major construction projects. These models and simulations involve nonlinear analysis of staged construction for open-cut excavations, tunnels, slopes, and similar engineered structures. The most important and difficult part of these computer simulations is the representation of the constitutive behavior of the soil strata.In current engineering practice, the engineer selects an existing constitutive model and calibrates its parameters to match the results of few laboratory material tests. The tests do not generate information on important aspects of the soil behavior, which are relevant for a field problem. Often the results of numerical simulation using the calibrated constitutive models do not match the field measurements. Ad hoc methods are used to select and adjust the constitutive model and its properties to match field performance.We propose a novel, powerful and systematic method to calibrate the constitutive model of the soil behavior directly from field measurements. We will apply the autoprogressive method; a neural network based methodology that has been proposed by Ghaboussi and his co-workers, to the modeling of staged construction for a deep braced excavation. A neural network (NN) material model will represent the constitutive model of the soil behavior and will be calibrated using laboratory test and observed field behavior of excavations. Initially, the proposed methodology will be applied to synthetically generated "field measurements" from numerical simulations of deep excavations. The synthetic data will include wall lateral displacements and surface settlements. A classical bounding surface plasticity model will be used to represent clay behavior to generate the synthetic data. As a verification of the proposed approach, the soil behavior computed by the trained NN constitutive model can be compared to the classical soil model.The proposed methodology will then be applied to field measurements from deep excavations in Boston Central Artery/Tunnel CA/T project. The NN material will learn the constitutive model of the soil behavior directly from field measurements of deformations. The proposed approach can be applied to problems other than open-cut excavations. The approach will potentially greatly enhance the numerical modeling of geotechnical problems. Field observations and "local experience" can then be directly and systematically incorporated into numerical models.