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Automated Back Analysis of Geotechnical Infrastructure Performance using Optimization

使用优化对岩土基础设施性能进行自动反分析

基本信息

批准号：
1965961
负责人：
金额：
--
依托单位：
University of Sheffield
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1965961
关键词：
Automated Back Analysis Geotechnical Infrastructure

项目摘要

The recent development of powerful, high resolution full-field measurement techniques (e.g. digital imaging) now gives access to a very large quantity of data during an experimental test or when monitoring field construction or field deformations (e.g. earthworks). Pilot studies now indicate that it is possible to utilise this data to directly characterise the properties of the soil in the system without having to sample the soil and test it in the laboratory. This adds a significant additional dataset to any problem and gives the engineer valuable additional capability with which to assess the problem in hand. Such a technique may be applied at laboratory model test level or at full scale in the field. The proposed PhD project is focused on the former.In the context of laboratory model tests, modern digital image correlation techniques allow determination of large dataset of high resolution displacement and strain fields from a set of images. At present these are typically generated using plane strain models with transparent walls. Pilot studies at Sheffield (Gueguin et al., 2015) have shown that it is possible to utilise optimization techniques in combination with these strain fields and the force-displacement response of a structure to reconstruct the stress-strain response of an undrained (clay) soil without any need for a specific analytical or numerical model of the system being tested, i.e. this is a problem agnostic process.This obviates the need to undertake a separate direct measurement of the stress-strain response of the soil by sampling and testing. Such a process introduces disturbance of the soil and uncertainty in results and may be a significant cause of the lack of fit that has been seen between some experimental data and theory. (Sampling and testing can however still be used to provide additional contextual data).The aim of the PhD is to develop and explore this technique further, calibrating it on selected physical model studies and extending it to granular materials. This will be enabled through design and testing of specific physical models using the state of the art 1g and centrifuge physical modelling facilities, together with transparent and photo-elastic soil capabilities in the geotechnics research group. Analytical/numerical developments will take advantage of the leading capabilities of the CMD group in applying optimization in Civil Engineering. Such work will strengthen Sheffield's status in physical modelling and use of optimization.Whilst field application of the technique is not the direct aim of this PhD, industry bodies such as Network Rail have shown interest in the technique where aerial and ground based digital imaging, LIDAR and wireless sensor data can be combined to generate a detailed dataset. One aspect of the research project will be to explore these possibilities in outline in order to provide a wider context for the proposed work.

强大的高分辨率全场测量技术（例如数字成像）的最新发展现在可以在实验测试期间或监测现场施工或现场变形（例如土方工程）时访问非常大量的数据。试点研究现在表明，可以利用这些数据直接分析系统中土壤的特性，而无需对土壤进行采样并在实验室中进行测试。这为任何问题增加了一个重要的额外数据集，并为工程师提供了评估手头问题的宝贵额外能力。这种技术可以应用于实验室模型测试水平或现场全尺寸。在实验室模型试验的背景下，现代数字图像相关技术允许从一组图像中确定高分辨率位移和应变场的大数据集。目前，这些通常使用具有透明壁的平面应变模型生成。在谢菲尔德的初步研究（Gueguin等人，2015）已经表明，可以利用优化技术结合这些应变场和结构的力-位移响应来重建不排水（粘土）土壤的应力-应变响应，而不需要测试系统的特定分析或数值模型，即这是一个问题不可知的过程。这避免了通过取样和测试对土壤的应力-应变响应进行单独的直接测量的需要。这样的过程引入了土壤的扰动和结果的不确定性，并且可能是在一些实验数据和理论之间已经看到的缺乏拟合的重要原因。（采样和测试仍然可以用来提供额外的背景数据）博士学位的目的是进一步开发和探索这种技术，在选定的物理模型研究中校准它，并将其扩展到颗粒材料。这将通过使用最先进的1g和离心物理建模设施设计和测试特定的物理模型，以及岩土工程研究小组的透明和光弹性土壤能力来实现。分析/数值开发将利用CMD集团在土木工程应用优化方面的领先能力。这些工作将加强谢菲尔德在物理建模和优化使用方面的地位。虽然该技术的现场应用不是本博士的直接目标，但行业机构如Network Rail对空中和地面数字成像，LIDAR和无线传感器数据可以结合起来生成详细数据集的技术表现出兴趣。该研究项目的一个方面将是概要探讨这些可能性，以便为拟议的工作提供更广泛的背景。