New Method of Experimentation and Constitutive Modelling Using Neural Networks

9505338 NG本研究的目标是：1)通过磁共振成像(MRI)以非侵入性的方式显示合适的颗粒状物质的内部和行为，以及2)验证通过离散单元法(DEM)模拟获得的微观观察结果。现有的基于椭球体随机排列的宏观DEM结果是非常有希望的。它们为大范围的应变、排水和不排水载荷以及静态和循环载荷提供了定性正确的结果和一些良好的定量预测。然而，需要进行3-D观测来验证。随着磁共振成像的普及，本研究是可行的。研究结果将为颗粒土本构关系的模拟提供一般的理论框架。*9505340 Nakagawa本研究的目标是：1)通过磁共振成像(MRI)非侵入性地可视化合适质量的颗粒材料的内部和行为，以及2)验证通过DEM(离散单元法)模拟获得的微观观察。现有的基于椭球体随机排列的宏观DEM结果是非常有希望的。它们为大范围的应变、排水和不排水载荷以及静态和循环载荷提供了定性正确的结果和一些良好的定量预测。然而，需要进行3-D观测来验证。随着磁共振成像的普及，本研究是可行的。研究结果将为颗粒土本构关系的模拟提供一般的理论框架。*9503462 Ghaboussi将开发一种新方法，直接根据土壤试验结果确定土壤的本构模型。主要目的是开发和实验验证这种测定方法，并将其结果应用于根据现场试验和新型实验室试验结果确定土壤的本构性质。在利用神经网络对行为建模并使用有限元建立适当的计算方面之后，将进行一系列实验来模拟现场测试。与传统的土工试验不同，在所提出的方法中，必须使试件处于非均匀应变状态，以便使试件中的不同点受到尽可能多的不同应力路径的影响。将开发新的土壤测试，包括相对简单的打孔测试，并在实验室进行测试。这些结果将被用来训练神经网络。方法论和计算方面的开发将在伊利诺伊大学进行。拟议研究的实验部分将在约翰·霍普金斯大学进行。*9503697 LADE将开发一种新的方法，直接根据土工试验结果确定土壤的本构模型。主要目的是开发和实验验证这种测定方法，并将其结果应用于根据现场试验和新型实验室试验结果确定土壤的本构性质。在利用神经网络对行为建模并使用有限元建立适当的计算方面之后，将进行一系列实验来模拟现场测试。与传统的土工试验不同，在所提出的方法中，必须使试件处于非均匀应变状态，以便使试件中的不同点受到尽可能多的不同应力路径的影响。将开发新的土壤测试，包括相对简单的打孔测试，并在实验室进行测试。这些结果将被用来训练神经网络。方法论和计算方面的开发将在伊利诺伊大学进行。拟议研究的实验部分将在约翰·霍普金斯大学进行。***

9505338 Ng The objective of this research is: 1) to non-invasively visualize the interior and the behavior of a suitable mass of granular material by MRI (Magnetic Resonance Imaging), and 2) to verify the microscopic observations obtained by DEM (Discrete Element Method) simulations. Available macroscopic DEM results on random arrays of ellipsoids produced by others are very promising. They provide qualitatively correct results and some good quantitative predictions for a wide range of strains, drained and undrained loading, and static as well as cyclic loading. However, 3-D observations are needed for verification. This research is feasible with the availability of MRI. The results will provide a general theoretical framework for modeling the constitutive relations of granular soils. *** 9505340 Nakagawa The objective of this research is: 1) to non-invasively visualize the interior and the behavior of a suitable mass of granular material by MRI (Magnetic Resonance Imaging), and 2) to verify the microscopic observations obtained by DEM (Discrete Element Method) simulations. Available macroscopic DEM results on random arrays of ellipsoids produced by others are very promising. They provide qualitatively correct results and some good quantitative predictions for a wide range of strains, drained and undrained loading, and static as well as cyclic loading. However, 3-D observations are needed for verification. This research is feasible with the availability of MRI. The results will provide a general theoretical framework for modeling the constitutive relations of granular soils. *** 9503462 Ghaboussi A new method will be developed to determine constitutive models of soils directly from the results of soil tests. The primary objective is to develop and experimentally verify this method of determination, with the results applied to the determination of the constitutive properties of soils from the results of in situ tests and n ew types of laboratory tests. After developing appropriate computational aspects utilizing neural networks to model behavior and using the FEM, a series of experiments will be conducted to simulate the in situ tests. Unlike conventional soils tests, in the proposed method it is essential that the sample be subjected to a non uniform state of strain so that different points within the sample can be subjected to as many different stress paths as possible. New soils tests, including a relatively simple punched test, will be developed and tested in the laboratory. These results will be used to train the neural network. The development of methodology and the computational aspects will be conducted at the University of Illinois. The experimental part of the proposed research will be conducted at the Johns Hopkins University. *** 9503697 Lade A new method will be developed to determine constitutive models of soils directly from the results of soil tests. The primary objective is to develop and experimentally verify this method of determination, with the results applied to the determination of the constitutive properties of soils from the results of in situ tests and new types of laboratory tests. After developing appropriate computational aspects utilizing neural networks to model behavior and using the FEM, a series of experiments will be conducted to simulate the in situ tests. Unlike conventional soils tests, in the proposed method it is essential that the sample be subjected to a non uniform state of strain so that different points within the sample can be subjected to as many different stress paths as possible. New soils tests, including a relatively simple punched test, will be developed and tested in the laboratory. These results will be used to train the neural network. The development of methodology and the computational aspects will be conducted at the University of Illinois. The experimental part of the proposed rese arch will be conducted at the Johns Hopkins University. ***