共查询到20条相似文献，搜索用时 328 毫秒
1.
Slope stability analysis of soil with a weak layer sandwiched between two strong layers is considered as a complex geotechnical problem. In this problem, the objective function is non‐convex and discontinuous with the presence of multiple strong local minima. Classical optimization techniques fail to converge to a valid solution unless a proper initial trial is adopted. Even though many new optimization algorithms have emerged, they have not been applied to geotechnical problems yet. In the present study, some recent swarm intelligence algorithms are adopted for some complicated example of slope stability problems and benchmarked with the traditional particle swarm optimization algorithm. From the results, it seems the levy flight krill herd algorithm is the most efficient method over proposed algorithms for this kind of problem. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
2.
A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted. 相似文献
3.
In this contribution an algorithm for parameter identification of geometrically linear Terzaghi–Biot‐type fluid‐saturated porous media is proposed, in which non‐uniform distributions of the state variables such as stresses, strains and fluid pore pressure are taken into account. To this end a least‐squares functional consisting of experimental data and simulated data is minimized, whereby the latter are obtained with the finite element method. This strategy allows parameter identification based on in situ experiments. In order to improve the efficiency of the minimization process, a gradient‐based optimization algorithm is applied, and therefore the corresponding sensitivity analysis for the coupled two‐phase problem is described in a systematic manner. For illustrative purpose, the performance of the algorithm is demonstrated for a slope stability problem, in which a quadratic Drucker–Prager plasticity model for the solid and a linear Darcy law for the fluid are combined. Copyright © 2001 John Wiley & Sons, Ltd. 相似文献
4.
Performance studies on six heuristic global optimization methods in the location of critical slip surface 总被引：5，自引：0，他引：5
Recently, many heuristic global optimization algorithms have evolved with success for treating various types of problems. Majority of these algorithms have not been applied to slope stability problem for which the presence of soft band and convergence problem (discontinuity of the objective function) may create difficulties in the minimization process. In this paper, six heuristic optimization algorithms are applied to some simple and complicated slopes. The effectiveness and efficiency of these algorithms under different cases are evaluated, and it is found that no single method can outperform all the other methods under all cases, as different method has different behavior in different types of problems. For normal cases, the particle swarm method appears to be effective and efficient over various conditions, and this method is recommended to be used. For special cases where the objective function is highly discontinuous, the simulated annealing method appears to be a more stable solution. 相似文献
5.
地下水管理模型求解方法的研究是目前地下水管理领域的热点问题。本文从地下水管理模型传统优化算法和现代智能优化算法等方面进行了评述,着重讨论了目前应用较广泛的求解非线性地下水系统的优化算法,如遗传算法、模拟退火算法、人工神经网络算法等;阐述了地下水监测网优化设计研究以及多目标地下水管理模型的求解方法。最后指出应加强地下水动态规划管理模型和地下水系统随机管理模型的求解技术的研究。 相似文献
6.
Multiobjective optimization deals with mathematical optimization problems where two or more objective functions (cost functions) are to be optimized (maximized or minimized) simultaneously. In most cases of interest, the objective functions are in conflict, i.e., there does not exist a decision (design) vector (vector of optimization variables) at which every objective function takes on its optimal value. The solution of a multiobjective problem is commonly defined as a Pareto front, and any decision vector which maps to a point on the Pareto front is said to be Pareto optimal. We present an original derivation of an analytical expression for the steepest descent direction for multiobjective optimization for the case of two objectives. This leads to an algorithm which can be applied to obtain Pareto optimal points or, equivalently, points on the Pareto front when the problem is the minimization of two conflicting objectives. The method is in effect a generalization of the steepest descent algorithm for minimizing a single objective function. The steepest-descent multiobjective optimization algorithm is applied to obtain optimal well controls for two example problems where the two conflicting objectives are the maximization of the life-cycle (long-term) net-present-value (NPV) and the maximization of the short-term NPV. The results strongly suggest the multiobjective steepest-descent (MOSD) algorithm is more efficient than competing multiobjective optimization algorithms. 相似文献
7.
进化策略是一种新的求解全局最优问题的方法,应用该方法对深基坑支护结构进行了优化设计的初步研究。针对进化策略在处理非线性函数的优化问题及高维优化问题时在收敛速度和收敛性方面存在的不足,将禁止搜索与有向搜索相结合,提出了一种快速进化策略算法,有效地克服了传统进化策略的缺点。工程实例分析研究表明,进化策略不受设计空间的可微性、连续性等限制,特别适合于求解支护工程这类具有离散设计变量和非确定性因素的工程设计问题;改进的进化策略具有更好的收敛性,有极强的避免局部极值的全局优化能力。 相似文献
8.
The parameter identification procedure proposed in this paper is based on the solution of an inverse problem, which relies on the minimization of an error function of least‐squares type. The solution of the ensuing optimization problem, which is a constrained one owing to the presence of physical links between the optimization parameters, is performed by means of a particular technique of the feasible direction type, which is modified and improved when the problem turns to an unconstrained one. The algorithm is particularly efficient in the presence of hierarchical material models. The numerical properties of the proposed procedure are discussed and its behaviour is compared with usual optimization methods when applied to constrained and unconstrained problems. Copyright © 2001 John Wiley & Sons, Ltd. 相似文献
9.
基于遗传算法的边坡稳定性评价的动态聚类法 总被引：5，自引：6，他引：5
针对常规动态聚类方法对初始聚类中心的敏感性以及聚类结果与样本输入次序有关等问题，提出了基于遗传算法的边坡稳定性评价的动态聚类方法，此方法对三峡库岸36个边坡的研究结果表明，该方法是一个具有全局最优解的动态聚类方法，其结果明显好于常规动态聚类方法。 相似文献
10.
天然岩体存在很多裂隙,而水在这些裂隙中的渗流严重影响岩石工程的稳定性,因此确定天然岩体渗透系数具有重大的实际意义。反演方法是确定岩体渗透系数的一种较理想的方法,渗透系数反演可归结为一个复杂的非线性函数优化问题。由于采用传统优化技术存在不少问题,而目前采用的全局优化算法—遗传算法也存在本质的问题,因此,提出仿生算法-免疫进化规划进行岩体渗透系数反演,并用一个大坝坝基工程的算例证明了算法的有效性。结果表明,其方法可以在仅知道水头的条件下,得到接近实际的渗透系数值。 相似文献
11.
Retaining wall optimization using interior search algorithm with different bound constraint handling 
下载免费PDF全文
下载免费PDF全文 Along with the applicability of optimization algorithms, there are lots of features that can affect the functioning of the optimization techniques. The main purpose of this paper is investigating the significance of boundary constraint handling (BCH) schemes on the performance of optimization algorithms. To this end, numbers of deterministic and probabilistic BCH approaches are applied to one of the most recent proposed optimization techniques, named interior search algorithm (ISA). Apart from the implementing different BCH methods, a sensitivity analysis is conducted to find an appropriate setting for the only parameter of ISA. Concrete cantilever retaining wall design as one of the most important geotechnical problems is tackled to declare proficiency of the ISA algorithm, on the one hand, and benchmark the effect of BCH schemes on the final results, on the contrary. As results demonstrate, various BCH approaches have a perceptible impact on the algorithm performance. In like manner, the essential parameter of ISA can also play a pivotal role in this algorithm's efficiency. Copyright © 2017 John Wiley & Sons, Ltd. 相似文献
12.
水文频率分析在参数估计过程中常采用智能优化适线法,如蚁群算法、遗传算法、粒子群算法、模拟退火算法等,但这些算法模型参数难以有效确定,导致寻优结果存在不稳定的不足。为了克服传统优化适线法的缺陷,在系统阐述群居蜘蛛优化算法基本原理的基础上,将群居蜘蛛优化算法用于水文频率曲线的参数确定中,并与传统的参数估计方法(矩法、权函数法、概率权重矩法、遗传算法)加以比较。实例结果表明,该方法搜索效率高,寻优结果稳定,能较好获得参数的最优解。 相似文献
13.
There is no gainsaying that determining the optimal number, type, and location of hydrocarbon reservoir wells is a very important aspect of field development planning. The reason behind this fact is not farfetched—the objective of any field development exercise is to maximize the total hydrocarbon recovery, which for all intents and purposes, can be measured by an economic criterion such as the net present value of the reservoir during its estimated operational life-cycle. Since the cost of drilling and completion of wells can be significantly high (millions of dollars), there is need for some form of operational and economic justification of potential well configuration, so that the ultimate purpose of maximizing production and asset value is not defeated in the long run. The problem, however, is that well optimization problems are by no means trivial. Inherent drawbacks include the associated computational cost of evaluating the objective function, the high dimensionality of the search space, and the effects of a continuous range of geological uncertainty. In this paper, the differential evolution (DE) and the particle swarm optimization (PSO) algorithms are applied to well placement problems. The results emanating from both algorithms are compared with results obtained by applying a third algorithm called hybrid particle swarm differential evolution (HPSDE)—a product of the hybridization of DE and PSO algorithms. Three cases involving the placement of vertical wells in 2-D and 3-D reservoir models are considered. In two of the three cases, a max-mean objective robust optimization was performed to address geological uncertainty arising from the mismatch between real physical reservoir and the reservoir model. We demonstrate that the performance of DE and PSO algorithms is dependent on the total number of function evaluations performed; importantly, we show that in all cases, HPSDE algorithm outperforms both DE and PSO algorithms. Based on the evidence of these findings, we hold the view that hybridized metaheuristic optimization algorithms (such as HPSDE) are applicable in this problem domain and could be potentially useful in other reservoir engineering problems. 相似文献
14.
Theoretical connections between optimization algorithms based on an approximate gradient 总被引：1，自引：0，他引：1
Performing a line search method in the direction given by the simplex gradient is a well-known method in the mathematical optimization community. For reservoir engineering optimization problems, both a modification of the simultaneous perturbation stochastic approximation (SPSA) and ensemble-based optimization (EnOpt) have recently been applied for estimating optimal well controls in the production optimization step of closed-loop reservoir management. The modified SPSA algorithm has also been applied to assisted history-matching problems. A recent comparison of the performance of EnOpt and a SPSA-type algorithm (G-SPSA) for a set of production optimization test problems showed that the two algorithms resulted in similar estimates of the optimal net-present-value and required roughly the same amount of computational time to achieve these estimates. Here, we show that, theoretically, this result is not surprising. In fact, we show that both the simplex, preconditioned simplex, and EnOpt algorithms can be derived directly from a modified SPSA-type algorithm where the preconditioned simplex algorithm is presented for the first time in this paper. We also show that the expectation of all these preconditioned stochastic gradients is a first-order approximation of the preconditioning covariance matrix times the true gradient or a covariance matrix squared times the true gradient. 相似文献
15.
基于灰色-进化神经网络的滑坡变形预测研究 总被引：15，自引：3，他引：15
滑坡变形位移预测对滑坡灾害治理具有重要的意义。考虑到滑坡位移单调增长的特殊性，根据位移分解原理，采用灰色系统提取位移趋势，用基于免疫进化规划的新型进化神经网络模型逼近位移偏差，从而提出了1种滑坡位移预测的新型智能方法。并用新滩滑坡的实测位移预测研究证明了所提智能预测方法的有效性及可行性 相似文献
16.
17.
Rashed Poormirzaee 《Arabian Journal of Geosciences》2016,9(16):673
The refraction microtremor method has been increasingly used as an appealing tool for investigating near surface S-wave structure. However, inversion, as a main stage in processing refraction microtremor data, is challenging for most local search methods due to its high nonlinearity. With the development of data optimization approaches, fast and easier techniques can be employed for processing geophysical data. Recently, particle swarm optimization algorithm has been used in many fields of studies. Use of particle swarm optimization in geophysical inverse problems is a relatively recent development which offers many advantages in dealing with the nonlinearity inherent in such applications. In this study, the reliability and efficiency of particle swarm optimization algorithm in the inversion of refraction microtremor data were investigated. A new framework was also proposed for the inversion of refraction microtremor Rayleigh wave dispersion curves. First, particle swarm optimization code in MATLAB was developed; then, in order to evaluate the efficiency and stability of proposed algorithm, two noise-free and two noise-corrupted synthetic datasets were inverted. Finally, particle swarm optimization inversion algorithm in refraction microtremor data was applied for geotechnical assessment in a case study in the area in city of Tabriz in northwest of Iran. The S-wave structure in the study area successfully delineated. Then, for evaluation, the estimated Vs profile was compared with downhole data available around of the considered area. It could be concluded that particle swarm optimization inversion algorithm is a suitable technique for inverting microtremor waves. 相似文献
18.
Displacement back analysis is a common method to identify mechanical geo‐material parameters using the monitored displacement. How to obtain a global optimum solution in large space search of highly non‐linear multimodal is a key point of optimum back analysis. The paper presents a new back analysis that is an integration of evolutionary support vector machines (SVMs), numerical analysis and genetic algorithm. The non‐linear relationship between the mechanical geo‐material parameters to be identified and the corresponding displacement values of key points is learned and represented by evolutionary SVMs in global optimum. Numerical analysis is used to create training and testing samples for recognition of SVMs. Then, performing a global optimum search on the obtained SVMs using genetic algorithm can identify the mechanical geo‐material parameters. The proposed algorithm is tested by back analysis of an elastic plate and an elastic–plastic plate and used to recognize mechanical parameters of subclay, strongly weathered tuff and weakly weathered tuff of Bachimen slope, Funing expressway, Fujian, China. The results indicate that applicability of the proposed algorithm with enough accuracy. Copyright © 2004 John Wiley & Sons, Ltd. 相似文献
19.
大型洞室群稳定性与优化的进化有限元方法研究 总被引：11，自引：3，他引：11
随着地下洞室群规模的日益扩大，迫切需要一种更新的全局优化方法以提高效率、优化结构在保证工程稳定性的同时能最大限度地减小工程造价。遗传算法的最新发现使得大型洞室群稳定性的最优建模和获得全局最优解成为可能。针对某地下洞室群工程，提出了进化有有限元方法，对工程软岩置换方案进行了优化。将有限元与遗传进化算法相结合，由遗传算法产生一组初始可行方案，以洞室开挖引起的破损区体积大小与参考值的增量比为评价指标，经过遗传变异操作，产生一组新的软岩置换方案，对每种方案进行应力分析，确定破损区大小，最终得到损坏区体积最小的方案即为最优软岩置换方案。这种方法可以优化得到全局最优解，并且搜索速度较快，较目前其它方法更易于在微机上实现。运用于实例中，得出了合理的置换方案，并提出了施工的合理化建议。 相似文献
20.
Contact between stiff structural elements and soil is encountered in many applications in geotechnical engineering. Modelling of such contact is challenging as it often involves impact that would lead to large deformation and failure of the soil. The Material Point Method (MPM) is a mesh‐free method that has been applied to simulate such phenomena. However, the frictional contact algorithm commonly used in MPM only supports Coulomb friction and cannot model fully or partially rough contact conditions in terms of geotechnical engineering. Moreover, because of very different stiffness of contacting materials, the contact force predicted by the previous frictional contact algorithms usually suffers from severe oscillation when applied in structure–soil interaction. This paper presents a new contact algorithm, termed Geo‐contact, designed for geotechnical engineering. In Geo‐contact, a penalty function is incorporated to reduce the oscillation in contact computation, and a limited shear stress is specified along the contact interface. The proposed Geo‐contact algorithm has been implemented to simulate smooth, partially rough and rough contact in typical large deformation penetration problems. The resistance–displacement curves obtained using the Geo‐contact are compared with analytical solutions of limit analysis and large deformation finite element results to verify the accuracy and robustness of the proposed contact algorithm. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献