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1.
Theoretical approaches are of fundamental importance to predict the potential impact of waste disposal facilities on ground water contamination. Appropriate design parameters are generally estimated by fitting theoretical models to data gathered from field monitoring or laboratory experiments. Transient through-diffusion tests are generally conducted in the laboratory to estimate the mass transport parameters of the proposed barrier material. These parameters are usually estimated either by approximate eye-fitting calibration or by combining the solution of the direct problem with any available gradient-based techniques. In this work, an automated, gradient-free solver is developed to estimate the mass transport parameters of a transient through-diffusion model. The proposed inverse model uses a particle swarm optimization (PSO) algorithm that is based on the social behavior of animals searching for food sources. The finite difference numerical solution of the forward model is integrated with the PSO algorithm to solve the inverse problem of parameter estimation. The working principle of the new solver is demonstrated and mass transport parameters are estimated from laboratory through-diffusion experimental data. An inverse model based on the standard gradient-based technique is formulated to compare with the proposed solver. A detailed comparative study is carried out between conventional methods and the proposed solver. The present automated technique is found to be very efficient and robust. The mass transport parameters are obtained with great precision. 相似文献
2.
The Barcelona basic model (BBM) successfully explained many key features of unsaturated soils and received extensive acceptance. It is also one of the few elastoplastic constitutive models for unsaturated soils that have been implemented within finite element codes and applied to the analysis of real boundary value problems. The BBM was proposed in incremental forms according to theories of soil plasticity in which individual aspects of the isotropic virgin behavior are controlled by multiple parameters, whereas at the same time, a single parameter controls more than one aspect of soil behavior. Although a variety of methods have been recently developed for calibrating model parameters for elastoplastic soil models, at present, there are no well‐established, simple, and objective methods for selecting parameter values in the BBM from laboratory tests. This has been one of the major obstacles to the dissemination of this constitutive model beyond the research context. This article presents an optimization approach especially developed for simple and objective identification of material parameters in the BBM. This is achieved by combining a modified state surface approach, recently proposed to model the elastoplastic behavior of unsaturated soils under isotropic stress conditions, with the Newton or quasi‐Newton method to simultaneously determine the five parameters governing isotropic virgin behavior in the BBM. The comparison between results using the proposed method and an existing method for the same laboratory tests was discussed from which the simplicity and objectivity of the proposed method were evaluated. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
3.
Youssef M.A. Hashash Séverine Levasseur Abdolreza Osouli Richard Finno Yann Malecot 《Computers and Geotechnics》2010
Performance observation is a necessary part of the design and construction process in geotechnical engineering. For deep urban excavations, empirical and numerical methods are used to predict potential deformations and their impacts on surrounding structures. Two inverse analysis approaches are described and compared for an excavation project in downtown Chicago. The first approach is a parameter optimization approach based on genetic algorithm (GA). GA is a stochastic global search technique for optimizing an objective function with linear or non-linear constraints. The second approach, self-learning simulations (SelfSim), is an inverse analysis technique that combines finite element method, continuously evolving material models, and field measurements. The optimization based on genetic algorithm approach identifies material properties of an existing soil model, and SelfSim approach extracts the underlying soil behavior unconstrained by a specific assumption on soil constitutive behavior. The two inverse analysis approaches capture well lateral wall deflections and maximum surface settlements. The GA optimization approach tends to overpredict surface settlements at some distance from the excavation as it is constrained by a specific form of the material constitutive model (i.e. hardening soil model); while the surface settlements computed using SelfSim approach match the observed ones due to its ability to learn small strain non-linearity of soil implied in the measured settlements. 相似文献
4.
为了解决一般概念性水文模型参数率定结果不稳定的问题,以新安江模型为例,提出了新安江模型日模参数的线性化率定方法。首先通过理想模型将该方法与SCE-UA方法及单纯形方法进行对比研究。率定结果中3种算法所获得的平均目标函数值分别为0.02、0.10、8.39 m3/s,平均循环次数分别为8、637、327,而且由线性化率定方法所获得的各参数值方差也要比其他2种方法小得多;说明线性化率定方法能够找到参数真值,计算精度和效率更高,率定结果更稳定。然后采用建阳和长滩河2个流域的实测资料对该方法进行应用检验,结果表明,同样可以较快地率定出稳定的模型参数优值。建阳流域的10组率定结果的目标函数值皆为100.35 m3/s,循环次数也皆在8次以内,而且2个流域检验期的径流深相对误差皆在9.68%以内,确定性系数皆在0.819以上。因此,线性化参数率定方法确实能够解决非线性模型参数率定结果不稳定的问题,不会产生不相关的局部参数优值,并且不受参数初值影响,计算精度高,循环次数少,是一种可行有效的全局优值参数优选方法。 相似文献
5.
The recent capability of measuring full‐field deformations using advanced imaging techniques provides the opportunity to improve the predictive ability of computational soil mechanics. This paper investigates the effects of imperfect initial specimen geometry, platen‐soil and apparatus compliance, and material heterogeneity on the constitutive model calibration process from triaxial tests with nonlubricated platens. The technique of 3D‐Digital Image Correlation (3D‐DIC) was used to measure, from digital images, full‐field displacements over sand specimen surfaces throughout triaxial compression tests, as well as actual specimen initial shape, and deformations associated with platen and apparatus compliance and bedding settlement. The difference between predicted and observed 3D specimen surface deformations served to quantify an objective function in the optimization algorithm. Four different three‐dimensional finite element models (FEMs), each allowing varying degrees of material variability in the solution of the inverse problem, were used to study the effect of material heterogeneity. Results of the parametric study revealed that properly representing the actual initial specimen geometry significantly improves the optimization efficiency, and that accounting for boundary compliance can be critical for the accurate recovery of the full‐field experimental displacements. Allowing for nonsymmetric material variability had the most significant impact on predicted behavior. A relatively high coefficient of variation in model parameters was found among a statistical ensemble of tests, underscoring the importance of conducting multiple tests for proper material characterization. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
6.
Parameter calibration is one of the most problematic phases of numerical modeling since the choice of parameters affects the model’s reliability as far as the physical problems being studied are concerned. In some cases, laboratory tests or physical models evaluating model parameters cannot be completed and other strategies must be adopted; numerical models reproducing debris flow propagation are one of these. Since scale problems affect the reproduction of real debris flows in the laboratory or specific tests used to determine rheological parameters, calibration is usually carried out by comparing in a subjective way only a few parameters, such as the heights of soil deposits calculated for some sections of the debris flows or the distance traveled by the debris flows using the values detected in situ after an event has occurred. Since no automatic or objective procedure has as yet been produced, this paper presents a numerical procedure based on the application of a statistical algorithm, which makes it possible to define, without ambiguities, the best parameter set. The procedure has been applied to a study case for which digital elevation models of both before and after an important event exist, implicating that a good database for applying the method was available. Its application has uncovered insights to better understand debris flows and related phenomena. 相似文献
7.
对地下水模型进行反演是模拟过程中的一个必要步骤,使用反演程序自动校正模型可快速确定最佳拟合的参数值,分析参数对模拟结果的敏感性,比人工试算-调整法更为优越。UCODE是一款被广泛应用的地下水模型反演程序,它使用高斯牛顿法进行参数优化,反演结果对参数初值有一定的依赖性。通过建立假想的非稳定流模型,进行6组数值试验,验证了UCODE程序的实用性。尽管参数的初始取值会影响反演的进程,但只要取值适当,UCODE就能实现优化参数的目的。 相似文献
8.
Large sets of soil experimental data (field and laboratory) are becoming increasingly available for calibration of soil constitutive models. A challenging task is to calibrate a potentially large number of model parameters to satisfactorily match many data sets simultaneously. This calibration effort can be facilitated by optimization techniques. The current study aims to explore systematic approaches for exercising optimization and sensitivity analysis in the area of soil constitutive modelling. Analytical, semi‐analytical and numerical optimization techniques are employed to calibrate a multi‐surface‐plasticity sand model. Calibration is based on results from a number of drained triaxial sample tests and a dynamic centrifuge liquefaction test. The analytical and semi‐analytical approaches and associated sensitivity analysis are applied to calibrate the model non‐linear shear stress–strain response. Thereafter, model parameters controlling shear–volume coupling effects (dilatancy) are calibrated using a solid–fluid fully coupled finite element program in conjunction with an advanced numerical optimization code. A related sensitivity study reveals the challenges often encountered in optimizing highly non‐linear functions. Overall, this study demonstrates applicability and limitations of optimization techniques for constitutive model calibration. Copyright © 2003 John Wiley & Sons, Ltd. 相似文献
9.
水文模型参数自动优选方法的比较分析 总被引：12，自引：2，他引：12
模型参数的识别是模糊研制与应用成功与否的关键。介绍了三个自动优选模型参数的方法，以新安江模型为例，应用１４个流域的资料，对罗森布郎克法、改进的单纯形法和基因算法优算法优选模型参数的效果，优化方法和收敛速度及参数初值对优选效果的影响进行了比较分析。 相似文献
10.
Selection of sand models and identification of parameters using an enhanced genetic algorithm 
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下载免费PDF全文 Numerous constitutive models of granular soils have been developed during the last few decades. As a consequence, how to select an appropriate model with the necessary features based on conventional tests and with an easy way of identifying parameters for geotechnical applications has become a major issue. This paper aims to discuss the selection of sand models and parameters identification by using genetic algorithm. A real‐coded genetic algorithm is enhanced for the optimization with high efficiency. Models with gradually varying features (elastic‐perfectly plastic modelling, nonlinear stress–strain hardening, critical state concept and two‐surface concept) are selected from numerous sand models as examples for optimization. Conventional triaxial tests on Hostun sand are selected as the objectives in the optimization. Four key points are then discussed in turn: (i) which features are necessary to be accounted for in constitutive modelling of sand; (ii) which type of tests (drained and/or undrained) should be selected for an optimal identification of parameters; (iii) what is the minimum number of tests that should be selected for parameter identification; and (iv) what is the suitable and least strain level of objective tests to obtain reliable and reasonable parameters. Finally, a useful guide, based on all comparisons, is provided at the end of the discussion. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
11.
通用模型参数率定技术研究 总被引：2，自引：0，他引：2
在介绍当前模型参数优选技术的基础上，设计了模型参数同优选方法相耦合的一系列数据接口定义，实现了人工试错和自动优选相耦合、多模型多参数同时自动优选的模型参数率定功能。应用实例表明，通用模型参数率定功能具有简便、快捷、准确的优点，大大提高了参数率定的效率。 相似文献
12.
13.
水文模型的参数优化率定一直以来是水文预报领域的重要研究内容,当水文模型的结构确定后,水文模型参数的选择对水文模型整体性能和水文预报结果的好坏有着至关重要的影响.针对传统水文模型参数优选采用单一目标不能充分全面挖掘水文观测资料中蕴含的水文特征信息的缺陷,本文以新安江三水源模型为例,尝试采用多目标优化算法优化率定水文模型,算例应用分析表明,通过合理的选择目标函数的种类和数目,采用多目标进化算法优化率定模型参数,可以获得相对于单目标率定模型参数更优的结果.进一步,研究工作针对模型参数优化的结果进行分析,可以明显看出模型参数优化中存在“异参同效”现象,为后续模型参数不确定性分析等相关研究工作的开展做好了铺垫. 相似文献
14.
The paper presents an optimization routine especially developed for the identification of model parameters in soil plasticity on the basis of different soil tests. Main focus is put on the mathematical aspects and the experience from application of this optimization routine. Mathematically, for the optimization, an objective function and a search strategy are needed. Some alternative expressions for the objective function are formulated. They capture the overall soil behaviour and can be used in a simultaneous optimization against several laboratory tests. Two different search strategies, Rosenbrock's method and the Simplex method, both belonging to the category of direct search methods, are utilized in the routine. Direct search methods have generally proved to be reliable and their relative simplicity make them quite easy to program into workable codes. The Rosenbrock and simplex methods are modified to make the search strategies as efficient and user‐friendly as possible for the type of optimization problem addressed here. Since these search strategies are of a heuristic nature, which makes it difficult (or even impossible) to analyse their performance in a theoretical way, representative optimization examples against both simulated experimental results as well as performed triaxial tests are presented to show the efficiency of the optimization routine. From these examples, it has been concluded that the optimization routine is able to locate a minimum with a good accuracy, fast enough to be a very useful tool for identification of model parameters in soil plasticity. Copyright © 2001 John Wiley & Sons, Ltd. 相似文献
15.
An important component in reliability-based design is the geotechnical property variability. Generic estimates are used often,
but calibration to a local geologic setting is preferable. In this case history, a methodology is shown that employs local
geotechnical data to estimate the total variability, using Ankara Clay for illustration. A literature review is used to estimate
the inherent variability, which is modeled as a random field with coefficient of variation (COV) and scale of fluctuation.
The resulting inherent variability COVs are much smaller than the generic ranges. Local correlations between various laboratory
and field tests and soil strength and compressibility parameters then are developed to quantify the transformation uncertainties.
The various sources of uncertainty are combined through a second-moment method to estimate the total geotechnical variability
as a function of the test type and correlation used. The results show: (1) the COVs for direct laboratory measurements are
significantly smaller than those obtained through correlations, and (2) depending on the geotechnical data available, the
local COVs can be very different from the generic guidelines. These could lead to unconservative designs. These issues are
illustrated by a simple design example. 相似文献
16.
This paper discusses the quality of the procedure employed in identifying soil parameters by inverse analysis. This procedure includes a FEM‐simulation for which two constitutive models—a linear elastic perfectly plastic Mohr–Coulomb model and a strain‐hardening elasto‐plastic model—are successively considered. Two kinds of optimization algorithms have been used: a deterministic simplex method and a stochastic genetic method. The soil data come from the results of two pressuremeter tests, complemented by triaxial and resonant column testing. First, the inverse analysis has been performed separately on each pressuremeter test. The genetic method presents the advantage of providing a collection of satisfactory solutions, among which a geotechnical engineer has to choose the optimal one based on his scientific background and/or additional analyses based on further experimental test results. This advantage is enhanced when all the constitutive parameters sensitive to the considered problem have to be identified without restrictions in the search space. Second, the experimental values of the two pressuremeter tests have been processed simultaneously, so that the inverse analysis becomes a multi‐objective optimization problem. The genetic method allows the user to choose the most suitable parameter set according to the Pareto frontier and to guarantee the coherence between the tests. The sets of optimized parameters obtained from inverse analyses are then used to calculate the response of a spread footing, which is part of a predictive benchmark. The numerical results with respect to both the constitutive models and the inverse analysis procedure are discussed. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
17.
M. A. Hicks 《国际地质力学数值与分析法杂志》2003,27(13):1123-1151
The Monot double‐hardening soil model has previously been implemented within a general purpose finite element algorithm, and used in the analysis of numerous practical problems. This paper reviews experience gained in calibrating Monot to laboratory data and demonstrates how the calibration process may be simplified without detriment to the range of behaviours modelled. It describes Monot's principal features, important governing equations and various calibration methods, including strategies for overconsolidated, cemented and cohesive soils. Based on a critical review of over 30 previous Monot calibrations, for sands and other geomaterials, trends in parameter values have been identified, enabling parameters to be categorized according to their relative importance. It is shown that, for most practical purposes, a maximum of only 5 parameters is needed; for the remaining parameters, standard default values are suggested. Hence, the advanced stress–strain modelling offered by Monot is attainable with a similar number of parameters as would be needed for some simpler, less versatile, models. Copyright © 2003 John Wiley & Sons, Ltd. 相似文献
18.
Optimization techniques for identifying soil parameters in geotechnical engineering: Comparative study and enhancement 下载免费PDF全文
下载免费PDF全文 A comparative study of optimization techniques for identifying soil parameters in geotechnical engineering was first presented. The identification methodology with its 3 main parts, error function, search strategy, and identification procedure, was introduced and summarized. Then, current optimization methods were reviewed and classified into 3 categories with an introduction to their basic principles and applications in geotechnical engineering. A comparative study on the identification of model parameters from a synthetic pressuremeter and an excavation tests was then performed by using 5 among the mostly common optimization methods, including genetic algorithms, particle swarm optimization, simulated annealing, the differential evolution algorithm and the artificial bee colony algorithm. The results demonstrated that the differential evolution had the strongest search ability but the slowest convergence speed. All the selected methods could reach approximate solutions with very small objective errors, but these solutions were different from the preset parameters. To improve the identification performance, an enhanced algorithm was developed by implementing the Nelder‐Mead simplex method in a differential algorithm to accelerate the convergence speed with strong reliable search ability. The performance of the enhanced optimization algorithm was finally highlighted by identifying the Mohr‐Coulomb parameters from the 2 same synthetic cases and from 2 real pressuremeter tests in sand, and ANICREEP parameters from 2 real pressuremeter tests in soft clay. 相似文献
19.
Cone Penetration Test (CPT) is widely utilized to gain regular geotechnical parameters such as compression modulus, cohesion coefficient and internal friction angle by transformation model in the site investigation. However, it is challenging to obtain simultaneously the unknown coefficients and error of a transformation model, given the intrinsic uncertainty (i.e., spatial variability) of geomaterial and the epistemic uncertainty of geotechnical investigation. A Bayesian approach is therefore proposed calibrating the transformation model based on spatial random field theory. The approach consists of three key elements: (1) three-dimensional anisotropic spatial random field theory; (2) classifications of measurement and error, and the uncertainty propagation diagram of geotechnical investigation; and (3) the unknown coefficients and error calibration of the transformation model given Bayesian inverse modeling method. The massive penetration resistance data from CPT, which is denoted as a spatial random field variable to account for the spatial variability of soil, are classified as type A data. Meanwhile, a few laboratory test data such as the compression modulus are defined as type B data. Based on the above two types of data, the unknown coefficients and error of the transformation model are inversely calibrated with consideration of intrinsic uncertainty of geomaterial, epistemic uncertainties such as measurement errors, prior knowledge uncertainty of transformation model itself, and computing uncertainties of statistical parameters as well as Bayesian method. Baseline studying indicates the proposed approach is applicable to calibrate the transformation model between CPT data and regular geotechnical parameter within spatial random field theory. Next, the calibrated transformation model was compared with classical linear regression in cross-validation, and then it was implemented at three-dimensional site characterization of the background project. 相似文献
20.
Planning Geotechnical Investigation Using ANFIS 总被引：2，自引：2，他引：0
Bojan Žlender Primož Jelušič Djamalddine Boumezerane 《Geotechnical and Geological Engineering》2012,30(4):975-989
Engineering experience may be written in mathematical form by using adaptive network-based fuzzy inference system (ANFIS). In this article we propose a method to use engineering experience and build a model, which can be used as a systematic decision support tool for engineers dealing with new problems. Planning geotechnical investigations is based on experience, which are used to obtain optimal number of investigation points, field and laboratory tests. To achieve this objective we define minimum number of investigation points and several input parameters which could increase or decrease the number of investigation points. The expert’s evaluations were put in a table, from which we generate the basis of the system. The paper presents a concept for planning geotechnical investigation for buildings using ANFIS and practical examples show its usefulness. 相似文献