Direct Field Calibration for Model Simulations of Deep Excavations

深基坑模型模拟的直接现场校准

• 批准号: 0084556
• 负责人: Youssef Hashash
• 金额: $ 15万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-15 至 2003-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0084556&HistoricalAwards=false
• 关键词: Direct; Field; Calibration; Model; Simulations

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.

岩土工程问题的数值模拟通常用于重大建设项目。 这些模型和模拟涉及非线性分析阶段施工明挖，隧道，边坡，和类似的工程结构。 这些计算机模拟中最重要和最困难的部分是土层本构特性的表示，在当前的工程实践中，工程师选择一个现有的本构模型，并校准其参数，以匹配少数实验室材料试验的结果。这些测试不会产生与现场问题相关的土壤行为重要方面的信息。通常，使用校准的本构模型的数值模拟的结果不匹配的现场测量。我们提出了一种新的、功能强大的、系统的方法来直接从现场测量数据中校准土壤行为的本构模型。我们将应用autoprogressive方法，神经网络为基础的方法，已提出的Ghaboussi和他的同事，建模的分阶段施工的深支撑开挖。 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开挖现场行为进行校准。最初，所提出的方法将被应用到综合产生的“现场测量”从数值模拟深基坑。合成数据将包括墙的横向位移和表面沉降。将使用经典的边界面塑性模型来表示粘土行为以生成合成数据。将训练好的神经网络本构模型计算的土体性状与经典土体模型进行比较，验证了所提出的方法的有效性，并将其应用于波士顿中央动脉隧道CA/T工程深基坑的现场测量。 NN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。所提出的方法可以适用于明挖以外的问题。该方法将大大提高岩土工程问题的数值模拟。实地观察和“当地经验”，然后可以直接和系统地纳入数值模型。