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。这些模型和模拟包括对露天开挖、隧道、边坡和类似工程结构的分期施工的非线性分析。这些计算机模拟的最重要和最困难的部分是土的本构行为的表示。在目前的工程实践中，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试验的结果。这些试验不能产生有关土壤行为的重要方面的信息，而这些信息与现场问题有关。采用标定本构模型的数值模拟结果往往与现场测量结果不匹配。采用特设方法选择和调整本构模型及其属性以匹配现场性能。我们提出了一种新的，强大的和系统的方法来校准本构模型的土壤行为直接从现场测量。我们将采用自动递进法；这是一种基于神经网络的方法，由Ghaboussi和他的同事提出，用于为深支撑挖掘的阶段施工建模。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现场开挖行为进行校准。最初，提议的方法将应用于从深挖数值模拟中综合产生的“现场测量”。合成数据将包括墙体横向位移和地表沉降。将采用经典的边界面塑性模型来表示粘土的行为，生成合成数据。作为对该方法的验证，通过训练后的神经网络本构模型计算的土壤行为可以与经典的土壤模型进行比较。然后，所提出的方法将应用于波士顿中央动脉/隧道CA/T项目的深挖现场测量。神经网络材料将直接从变形的现场测量中学习土的本构模型。所提出的方法可以应用于除露天开挖以外的问题。该方法将潜在地大大增强岩土工程问题的数值模拟。然后，实地观测和“当地经验”可以直接和系统地纳入数值模式。

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.