Optimal lower bound of passive earth pressure using finite elements and non-linear programming

The present study pertains to the finding of the lower bound solution, formulating it as a non-linear programming problem using the generalized method developed by Lysmer with certain variations to incorporate the non-linear no-yield condition constraints directly in the analysis. The method considers the family of plane stress fields having the property that all stresses vary linearly within each triangular element of some mesh which covers the soil mass under study. For this type of stress field it is possible to express all equilibrium conditions as a set of linear constraints and the no-yield as a set of non-linear constraints. The boundary condition constraints may be of linear equality or inequality type. By expressing some of the design variables in terms of the remaining variables the linear equality constraints are implicity satisfied. Such a technique minimizes the complexity of the problem by eliminating the equality constraints and reduces the dimensionality of the problem, saving much, computational effort. The optimal lower bound is isolated by formulating it as a non-linear programming (NLP) problem subjected to both linear and non-linear inequality constraints. The sequential unconstrained minimization technique using the extended penalty function method as suggested by Kavlie has been used to isolate the optimal lower bound. The method has successfully been applied to the passive earth pressure and bearing capacity problem. Numerical results are obtained and compared with Lysmer's solution to show the effectiveness of the present approach.     

Potential use of model predictive control for optimizing the column flotation process

A constrained model predictive control (MPC) strategy is proposed to deal with the problem of optimizing flotation column operation using secondary variables. Froth depth, collection zone gas hold-up and bias rate are selected as secondary variables to be controlled whereas tailing, wash-water and gas flow rate are used as manipulated variables. The control problem was formulated in order to minimize the tracking error of the gas hold-up and bias rate by maintaining gas flow rate, wash-water flow rate and bias rate within their operational limits. In particular, a strategy was conceived to optimize the column flotation process based on establishing an unreachable high set point for the gas hold-up (which is equivalent to maximizing the bubble surface area available for particle collection at a given flotation reagent dosage and thus recovery), while simultaneously satisfying operational constraints (such as ensuring a positive bias rate to prevent gangue entrainment and therefore concentrate grade deterioration). Several other operational constraints on wash-water, gas rate, gas hold-up and bias rate were considered, their use being justified from a processing point of view. Since this study deals with the hydrodynamic characteristics of flotation columns, a pilot flotation column working with a two-phase system is sufficient to demonstrate the advantages of using predictive control for this process optimization.     

偏最小二乘回归神经网络的矿坑涌水量预测

影响矿坑充水的因素多且复杂,矿坑涌水量预测模型主要考虑降水、地表水、引水灌溉等影响因素,因变量和自变量的关系比较复杂。将偏最小二乘回归与神经网络耦合,建立了矿坑涌水预报模型。模型将自变量利用偏最小二乘回归处理,提取对因变量影响强的成分,既可以克服变量之间的相关性问题,又可以降低神经网络的输入维数,并能较好地解决非线性问题,提高了模型的学习能力和表达能力。以河南鹤壁八矿涌水量为例,建立了基于偏最小二乘回归和神经网络耦合的矿坑涌水量预测模型。计算验证表明,该类模型具有较高的预报精度和推广应用价值。     

基于偏最小二乘与神经网络耦合的储层参数预测

将偏最小二乘回归（PLS）与神经网络（NN）耦合，建立了储层参数预报模型。利用偏最小二乘对影响储层参数的诸多因素进行分析，提取对因变量影响强的成分，从而克服了变量间的多重相关性问题，降低了神经网络的输入维数；同时，利用神经网络建模可以较好地解决非线性的储层参数预测问题。计算实例表明，本耦合模型的拟合和预报精度优于独立使用神经网络模型的精度。     

Rural organizational impacts,mitigation strategies,and resilience to the 2010 Darfield earthquake,New Zealand

A methodology for evaluating real-time optimal reservoir releases under flooding conditions that minimizes flood damages for a river-reservoir system is described in this paper. The problem is formulated as a discrete-time optimal control problem in which reservoir releases are the control variables, and water surface elevations and discharges are the state variables. Constraints imposed on the reservoir’s water surface elevations and reservoir releases to the downstream reaches are incorporated into an objective function using a penalty function method. The optimal control model consists of the two primary interfaced components: (1) the U.S. Geological Survey Full EQuation routing model to simulate the unsteady flow dynamics of the river-reservoir system and (2) an optimization technique, simulated annealing that optimizes reservoir releases (flood control gate operations) subject to system constraints. The model solves an augmented control problem. The model was applied to the river-reservoir system of Lake Travis on the Lower Colorado River in Texas. The model application to Lake Travis revealed the usefulness of the model in improving a given operation policy, regardless of the type objective function (linear or nonlinear). The methodology and the operation model developed here are unique since they can be applied to any river-reservoir system, do not require simplification of nonlinearities, and guarantee the determination of an optimal or near-global optima.     

GaRiRO: Gradient and residual integrated rank ordering of stations in rainfall monitoring network

This paper presents a new method that integrates gradient and residual values for rank ordering of stations in a monitoring network (GaRiRO). The innovation is derived from the fact that the parameter (dependent variable) gauged through the monitoring network is modelled using independent variables that influence its measured quantity. And the dependent variable exhibit non-stationary spatial gradient with respect to the independent variables, particularly in complex terrain. GaRiRO technique was developed to prioritize the rain gauge stations for optimizing the existing network and selection of the best locations for relocation or installation of gauges. Although initially aimed to assist hydrologists with a ranking scheme for rain gauge stations, it can be applied to any environmental, meteorological or hydro-meteorological monitoring network. The new procedure is based on deriving gradient and residual value at each station by modeling the spatial relationship of dependent-independent variable using geographically weighted regression (GWR) technique. For the prospective stations with no record, the gradient value is estimated using GWR model and the residual value is derived from the residual map generated by applying kriging technique on the residual derived at all gauged locations. The method combines the decision factor with analytical strength of GIS for prioritizing the stations which results in limited number of trials for installation or relocation of gauges to yield optimized network configuration.     

Designing an optimal multivariate geostatistical groundwater quality monitoring network using factorial kriging and genetic algorithms

The optimal selection of monitoring wells is a major task in designing an information-effective groundwater quality monitoring network which can provide sufficient and not redundant information of monitoring variables for delineating spatial distribution or variations of monitoring variables. This study develops a design approach for an optimal multivariate geostatistical groundwater quality network by proposing a network system to identify groundwater quality spatial variations by using factorial kriging with genetic algorithm. The proposed approach is applied in designing a groundwater quality monitoring network for nine variables (EC, TDS, Cl⁻, Na, Ca, Mg, SO ₄ ²⁻ , Mn and Fe) in the Pingtung Plain in Taiwan. The spatial structure results show that the variograms and cross-variograms of the nine variables can be modeled in two spatial structures: a Gaussian model with ranges 28.5 km and a spherical model with 40 km for short and long spatial scale variations, respectively. Moreover, the nine variables can be grouped into two major components for both short and long scales. The proposed optimal monitoring design model successfully obtains different optimal network systems for delineating spatial variations of the nine groundwater quality variables by using 20, 25 and 30 monitoring wells in both short scale (28.5 km) and long scale (40 km). Finally, the study confirms that the proposed model can design an optimal groundwater monitoring network that not only considers multiple groundwater quality variables but also monitors variations of monitoring variables at various spatial scales in the study area.     

Well placement optimization subject to realistic field development constraints

This work considers the well placement problem in reservoir management and field development optimization. In particular, it emphasizes embedding realistic and practical constraints into a mathematical optimization formulation. Such constraints are a prerequisite for the wider use of mathematical optimization techniques in well placement problems, since constraints are a way to incorporate reservoir engineering knowledge into the problem formulation. There are important design limitations that are used by the field development team when treating the well placement problem, and these limitations need to be articulated and eventually formalized within the problem before conducting the search for optimal well placements. In addition, these design limitations may be explicit or implicit. In this work, various design limitations pertaining to well locations have been developed in close collaboration with a field operator on the Norwegian Continental Shelf. Moreover, this work focuses on developing constraint-handling capability to enforce these various considerations during optimization. In particular, the Particle Swarm Optimization (PSO) algorithm is applied to optimize for the well locations, and various practical well placement constraints are incorporated into the PSO algorithm using two different constraint-handling techniques: a decoder procedure and the penalty method. The decoder procedure maps the feasible search space onto a cube and has the advantage of not requiring parameter tuning. The penalty method converts the constrained optimization problem into an unconstrained one by introducing an additional term, which is called a penalty function, to the objective function. In contrast to the penalty method, only feasible solutions are evaluated in the decoder method. Through numerical simulations, a comparison between the penalty method and the decoder technique is performed for two cases. We show that the decoder technique can easily be implemented for the well placement problem, and furthermore, that it performs better than the penalty method in most of the cases.     

Calibration of an elasto-plastic constitutive model by a constrained optimisation procedure

The presented paper deals with a constrained optimisation technique for the calibration of elasto-plastic model parameters in a rational and objective manner. The procedure consists in finding a set of model parameters which minimise the difference between the experimental data and the numerical simulations defined by an objective function. For this purpose, an optimisation routine, termed ParaID, has been developed which combines the quasi-Newton and stochastic methods. The optimisation technique was employed to calibrate a multi-mechanism elasto-plastic constitutive model. Using the results of three isotropically consolidated drained triaxial compression tests, a comparison between numerical and experimental results clearly shows the capability of the optimisation procedure to determine the model parameters correctly.     

结构方程模型及其在地学数据建模中的回顾与展望

结构方程模型是一种建立、 估计和检验因果关系的方法.它可以替代多重回归、 路径分析、 因子分析、 协方差分析等方法,清晰分析单项指标对总体的作用和单项指标间的相互关系,是一种主要应用于验证性模型分析的多元统计建模技术.由于能够通过可观测变量来度量潜变量得分以及分析不同子模型下潜变量之间的协同效应等优点,结构方程模型被广...     

State of the art in the conceptual design of flotation circuits

The present study summarizes reports on optimal flotation circuits design over the period from 1989 to present. A review is made on the resolution characteristics of each study, leading to a classification of the approaches into four different groups, in which conclusions are based on either (A) mathematical solutions without binary variable, (B) mathematical solutions with binary variable, (C) heuristic solutions, or (D) genetic algorithms. These groups aim at a common scheme of resolution, varying in both flotation and milling models employed, characteristics of their superstructures, and functions which each study proposes to optimize, as well as the effects of the application of each strategy on the form of the solution of the circuit (selection of stages, selection of equipment, and dimensions of equipment).Within these either implicit or explicit multi-objective optimization problems there are challenges strongly related to the obtaining of better flotation and milling/classification models, as well as the treatment of uncertainty related to important parameters (kinetics, composition, external economic factors) and changes in previously established configurations using methods in which the designer's criteria influence the application of models and restrictions on the problem. Also, the incorporation of environmental issues needs more attention.     

水位演算模型及其在水位预报中的应用

将单一河道洪水演算系统视为单输入一单输出系统,基于时间序列分析中的ＡＲＭＡＶ（２,１）模型,应用时间序列分析最优控制理论建立了水位演算模型和水位预报方法。证明若采用水位作变量,则可采用无约束的最优化方法确定参数;若采用水深作变量,则可采用以水量平衡为约束的寻优方法确定参数。本文所建立的水位预报方法用于黄河下游水位预报,得到了满意的预报精度。     

Shape optimisation of the support section of a tunnel at great depths

The shape optimisation of a cavity is typically performed without considering the previous support, which may significantly reduce the practical significance of such analyses. Even when an excavation section is optimised, failure of the surrounding rock in a tunnel cannot be prevented in the presence of excessive in situ stress. Thus, a support should be established to protect the stability of a tunnel from the failure of the surrounding rock. This study examines the optimal shape of the support that satisfies the optimisation criterion, which minimises the largest tangential stress along the inner edge of the support, for a specific net tunnel size and support strength. The optimisation process is to solve a series of forward problems using the conformal mapping method for a plane elasticity complex function. The tangential stress along the inner edge of the support is selected as the objective function, and the coefficients of the mapping function are considered as the design variables. The minimum value of the objective function is calculated based on the mixed penalty function method and the optimal support shape that satisfies the given constraints can be obtained. The stress state of an optimally shaped tunnel support is significantly improved compared to non-optimal configurations, and the stress concentration along the inner edge of the support is minimised.     

基于SFLA-GRNN模型的基坑地表最大沉降预测

为可靠预测基坑周边地表沉降的发展趋势,提出了一种基于混合蛙跳算法和广义回归神经网络模型的基坑地表最大沉降预测模型（SFLA-GRNN模型）。首先,在沉降机制分析并初选输入变量集的基础上,利用灰色相关度分析对模型输入、输出变量的相关性进行量化,并剔除与输出变量相关性明显偏小的输入变量;其次,利用混合蛙跳算法（SFLA）对广义回归神经网络模型（GRNN）的平滑因子进行优化确定,减少人为因素对模型精度和泛化能力的不良影响;最后,利用筛选得到的输入变量集建立基坑地表最大沉降预测的广义回归神经网络模型。实例应用及对比计算结果表明,基于灰色相关度的输入变量筛选和基于混合蛙跳算法的平滑因子优化均能够有效提高广义回归神经网络模型的精度和泛化能力,以上结论可为类似变形预测提供参考。     

Robust optimisation of CO2 sequestration strategies under geological uncertainty using adaptive sparse grid surrogates

Geologic CO₂ sequestration in deep saline aquifers is a promising technique to mitigate the effect of greenhouse gas emissions. Designing optimal CO₂ injection strategy becomes a challenging problem in the presence of geological uncertainty. We propose a surrogate assisted optimisation technique for robust optimisation of CO₂ injection strategies. The surrogate is built using Adaptive Sparse Grid Interpolation (ASGI) to accelerate the optimisation of CO₂ injection rates. The surrogate model is adaptively built with different numbers of evaluation points (simulation runs) in different dimensions to allow automatic refinement in the dimension where added resolution is needed. This technique is referred to as dimensional adaptivity and provides a good balance between the accuracy of the surrogate model and the number of simulation runs to save computational costs. For a robust design, we propose a utility function which comprises the statistical moment of the objective function. Numerical testing of the proposed approach applied to benchmark functions and reservoir models shows the efficiency of the method for the robust optimisation of CO₂ injection strategies under geological uncertainty.     

一种以有序变量为因变量的四归分析方法及其地质应用

在这里所研究的回归分析中,因变量是n个样品的序号——有序变量。我们用迭代法求出m个自变量的线性组合,使其各分量的大小顺序与原给的样品序号尽可能一致,文中用具体实例说明了这种方法在矿床统计预测中的应用。     

Adaptive neuro-fuzzy selection of the optimal parameters of protective spur dike

This study proposes a new approach for determining optimum dimensions of protective spur dike to mitigate scour amount around existing spur dikes. The main objective of this article was to predict the most optimum values of the protective spur dikes to reach the best performance. To predict the protective spur dike parameters for scour controlling around spur dikes, this paper constructed a process which selects the optimal protective spur dike parameters in regard to actual length of the protective spur dike, actual length of the main spur dikes, distance between the protective spur dike and the first spur dike, angle between protective spur dike and flow direction, flow intensity and median size of bed sediments with adaptive neuro-fuzzy (ANFIS) method. To build a protective spur dike with the best features, it is desirable to select and analyze factors that are truly relevant or the most influential to the spur dike. This procedure is typically called variable selection, and it corresponds to finding a subset of the full set of recorded variables that exhibits good predictive abilities. In this study, architecture for modeling complex systems in function approximation and regression was used, based on using ANFIS. Variable searching using the ANFIS network was performed to determine how the five factors affect the protective spur dike. Experimental model of the protective spur dike was used to generate training and checking data for the ANFIS network.     

Modelling chronic winter hazards as a function of precipitation and temperature

In previous research, winter weather conditions like precipitation and cold were recognized as factors affecting the change in traffic volume, and a simulation has been conducted to model the effect of weather factor on variations in traffic flow. A winter weather hazard model considering natural chronic hazards in the winter season has been proposed. To achieve this purpose, this research formulated a dummy variable winter weather traffic model while considering explanatory variables such as expected daily volume, snowfall, and temperature. This model was derived using six-year traffic data that were collected on a weigh-in-motion site on Highway 44, in Alberta, Canada. Precipitation and cold data collected from a weather station were linked with traffic data to model traffic variations caused by weather factors. The performance of the model, on the ground of temporal transferability and model specification, was tested using data from different years. The temporal transferability test confirmed that the model can be successfully applied regardless of the year. It was also revealed that each vehicle class prefers a different model specification in estimating correct traffic volume.

     

A derivative-free methodology with local and global search for the constrained joint optimization of well locations and controls

In oil field development, the optimal location for a new well depends on how it is to be operated. Thus, it is generally suboptimal to treat the well location and well control optimization problems separately. Rather, they should be considered simultaneously as a joint problem. In this work, we present noninvasive, derivative-free, easily parallelizable procedures to solve this joint optimization problem. Specifically, we consider Particle Swarm Optimization (PSO), a global stochastic search algorithm; Mesh Adaptive Direct Search (MADS), a local search procedure; and a hybrid PSO–MADS technique that combines the advantages of both methods. Nonlinear constraints are handled through use of filter-based treatments that seek to minimize both the objective function and constraint violation. We also introduce a formulation to determine the optimal number of wells, in addition to their locations and controls, by associating a binary variable (drill/do not drill) with each well. Example cases of varying complexity, which include bound constraints, nonlinear constraints, and the determination of the number of wells, are presented. The PSO–MADS hybrid procedure is shown to consistently outperform both stand-alone PSO and MADS when solving the joint problem. The joint approach is also observed to provide superior performance relative to a sequential procedure.