智能优化算法在结构损伤识别中的应用

利用智能优化算法进行结构的损伤识别是近年来的研究热点,其基本思路是将实测数据和数值模型的输出定义为结构参数的目标函数,通过搜索目标函数的最小值来得到结构参数,并根据这些参数在损伤前后的变化情况来识别损伤.目标函数可以有多种定义方式,智能优化算法也有多种算法可以选择,为了得到最佳的计算效果,利用4种目标函数和3种智能优化算法进行了损伤识别的数值模拟.计算结果表明,由加速度时程响应定义的目标函数与差分进化算法相结合具有很好的识别精度和抗噪能力.     

基于局部优化粒子群算法的背景噪声反演

从背景噪声中提取瑞雷波频散曲线并通过反演获得地下横波速度结构已被广泛应用于大尺度的地下结构探测和小尺度的工程勘探中.基于频散函数的反演目标函数可以有效解决多阶模频散曲线联合反演的模式误判问题,然而其广泛分布的局部极值导致更为严重的多解性,在大范围的参数搜索空间下很难获得最优解,需要搭配全局搜索性能强的优化算法.本文提出局部优化粒子群算法(PSOG),通过粒子迭代过程中引入局部优化方法提高种群多样性,避免陷入局部极值并加快收敛速度.为验证新算法的有效性,结合基于久期函数的目标函数对理论合成数据进行反演,结果表明,局部优化粒子群算法比传统算法的稳定性与准确性都有显著提高.处理了上海苏州河地区的背景噪声数据,成功地对古河道切割造成的软弱层进行成像.PSOG算法与新型反演目标函数的结合在背景噪声勘探的工程应用上具有巨大潜力.     

人工蜂群算法的电震非线性联合反演研究

本文为解决直流电阻率法和瑞雷面波法反演存在的多解性问题,构建了新的电震联合反演目标函数,通过增加不同地球物理响应的反演参数,利用权系数调节各参数对反演结果的影响,实现同步联合反演,从而减少多解性.引入人工蜂群算法求解目标函数,有效解决了传统反演方法将非线性问题线性化处理产生的细节信息丢失问题,提高了反演精度.经过对较为复杂的四层速度递增AA型和四层含低速夹层HA型理论模型和实例数据的反演验证,表明基于人工蜂群算法的非线性联合反演能够有效减少单一方法多解性问题并提高反演精度.     

海洋地磁参量实时测量野值的在线辨识与改正

地磁参量实时测量野值的在线辨识与改正是影响地磁导航算法定位概率和精度的重要因素.地磁导航定位的研究目前主要集中于匹配定位算法,地磁参量实时测量数据处理的研究较少.本文将多层递阶非平稳时间序列预测模型引入海洋地磁参量实时测量野值在线辨识与改正,利用多层递阶模型对实时测量值进行一步预测,根据迪克松准则进行野值辨识,利用中值滤波和一步预测值对野值进行改正.仿真数据和实测数据的实验室仿真结果表明,本文提出的野值在线辨识与改正算法不仅可以检测全部孤立型野值而且对多数斑点型野值也有较好的辨识效果,对产生野值处信号的复原误差小于5%.     

基于物理参数辨识的结构损伤识别方法研究

结构损伤识别是土木工程领域的一个热门研究课题,在近几十年的发展过程中,人们一直在寻找适用于结构整体损伤的识别方法,其基本思路是利用结构的动力响应来辨识结构的力学特征参数,然后再根据这些力学特征参数的变化情况来识别损伤.本文在分析各种力学特征参数对结构损伤的适用性之后,认为结构的物理参数(刚度参数)是具有普遍适用性的损伤敏感参数,并基于此观点深入研究了物理参数辨识的相关理论和方法.     

多角度卫星遥感多阶段目标决策反演研究

利用小波变换, 对多角度卫星遥感图像进行多尺度分解, 在每一尺度上进行多阶段目标决策, 粗尺度的反演结果作为细尺度反演的先验信息, 反演逐步细化, 直到最初的观测尺度. 为实现多阶段目标决策反演策略应用于区域范围的卫星遥感反演实践提出了一种解决的方法. 利用MISR数据, 反演内蒙半干旱草场的植被结构参数, 初步验证表明该方法有效.     

基础隔震结构简化模型的振动参数识别

针对目前隔震工程设计中模型简化方面存在的不足，以隔震体系上部结构层间剪力和隔震层位移这两个主要指标等效为目标函数，用多目标优化算法建立了等代体系刚度及质量的参数识别数字模型。并针对工程设计需要，导出了等代参数的简代计算表达式。工程实例计算表明，用本文简化模型分析得到的上述两个指标结果与原结构的响应时程曲线基本吻合，时域响应峰值的相对误差小于3%。     

抗震结构中考虑离散变量的多目标优化设计

以结构应变能最大和结构造价最低为目标函数，引入离散变量，进行了多目标优化设计，从中找出结构应变能最大、结构造价最低的最优截面。结果表明，所提出的理论和方法是合理的。     

地震作用下结构时变物理参数识别

利用小波分析函数多尺度逼近方法,将剪切型结构在地震作用下时变的阻尼和刚度用尺度函数的线性组合表示,将时变参数的辨识问题转化为由已知的正交尺度函数和系统的输入输出来估计线性组合中的时不变系数问题,用最小二乘法对剪切型框架结构在地震作用下时变的刚度和阻尼进行了有效的辨识。此方法无需事先假定系统参数的时变规律,在有噪声情况下可以用Tikhonov正则化方法减小识别方程的不适定性对识别结果的影响。数值算例表明了该方法的有效性。     

基于混沌-粒子群优化的磁法数据非线性反演方法

磁法反演属于非线性最优化问题,具有多变量、目标函数多极值、反演多解性等特点,因此,需要稳定的和高效的优化反演算法.粒子群优化已开始被用于地球物理反演计算,但是对于高维数、多峰值函数,粒子群的收敛精度不高,容易陷入局部极值.如果将混沌局部搜索和粒子群优化的优势相结合,通过将种群搜索过程对应为混沌轨道的遍历过程,可使标准粒子群优化的搜索过程具有避免陷入局部极小的能力.本文利用混沌-粒子群优化用于磁法反演计算.数据试验结果表明,该方法可以用于磁法数据的地球物理非线性反演,并且在一定程度上优于标准粒子群优化方法.     

Multi-objective autocalibration of SWAT model for improved low flow performance for a small snowfed catchment

ABSTRACT

A reliable modelling framework needs to ensure that the model is simulating reality with limited uncertainty, thus enhancing its predictive ability. In the literature, hydrological model assessment using one or more metrics is reported to be inadequate when the river flow regime is required to be reproduced comprehensively. This research is aimed to: (a) calibrate the Soil and Water Assessment Tool (SWAT) based on the concept of multi-objective optimization by applying the Borg multi-objective evolutionary algorithm (MOEA); (b) apply hydrological signatures as objective functions; and (c) adopt a multi-metric approach for model evaluation. The SWAT model was coupled with a relatively newer and powerful Borg MOEA. The inclusion of hydrological signatures as objective functions along with the conventional statistical functions assisted in improving the performance for low flows by 135% in terms of volume efficiency and 65% for flow time series simulation.     

Probabilistic calibration of a distributed hydrological model for flood forecasting

Abstract

The complexity of distributed hydrological models has led to improvements in calibration methodologies in recent years. There are various manual, automatic and hybrid methods of calibration. Most use a single objective function to calculate estimation errors. The use of multi-objective calibration improves results, since different aspects of the hydrograph may be considered simultaneously. However, the uncertainty of estimates from a hydrological model can only be taken into account by using a probabilistic approach. This paper presents a calibration method of probabilistic nature, based on the determination of probability functions that best characterize different parameters of the model. The method was applied to the Real-time Interactive Basin Simulator (RIBS) distributed hydrological model using the Manzanares River basin in Spain as a case study. The proposed method allows us to consider the uncertainty in the model estimates by obtaining the probability distributions of flows in the flood hydrograph.

Citation Mediero, L., Garrote, L. & Martín-Carrasco, F. J. (2011) Probabilistic calibration of a distributed hydrological model for flood forecasting. Hydrol. Sci. J. 56(7), 1129–1149.     

Diary of forthcoming events

Abstract

Currently, environmental modelling is frequently conducted with the aid of artificial neural networks (ANNs) in an effort to achieve greater accuracy in simulation and forecasting beyond that typically obtained when using solely linear models. For the design of an ANN, modellers must contend with two key issues: (a) the selection of model input and (b) the determination of the number of hidden neurons. A novel approach is introduced to address the optimal design of ANNs based on a multi-objective strategy that enables the user to find a set of feasible ANNs, determined as optimal trade-off solutions between model simplicity and accuracy. This is achieved in a multi-objective fashion by simultaneously minimizing three different cost functions: the model input dimension, the hidden neuron number and the generalization error computed on a validation set of data. The multi-objective approach is based on the Pareto dominance criterion and an evolutionary strategy has been employed to solve the combinatorial optimization problem. From a theoretical perspective, the choice of a multi-objective approach marks an attempt to account for, and overcome, the “curse of dimensionality” and to circumvent the drawbacks of “overfitting” that are inherent in ANNs. Moreover, it is demonstrated that the strategy renders the choice of the ANN more robust, as is evident by “unseen data” in the testing stage, since structure determination is not merely based on the statistical evaluation of the generalization performance. The methodology is tested and the results are reported in a case study relating groundwater level predictions to total monthly rainfall.     

Parameter transferability under changing climate: case study with a land surface model in the Durance watershed,France

Abstract

In physically-based land surface models, the parameters can all be prescribed a priori but calibration can be used to enhance the realism of the simulations in well instrumented domains. In such a case, the transferability of calibrated parameters under non-stationary conditions needs to be addressed, especially in the context of climate change. To this end, we used the Catchment Land Surface Model (CLSM) in the Upper Durance watershed located in the French Alps, which experienced a significant increase in temperature over the last century. The CLSM is forced by a 50-year meteorological dataset of good quality. Four parameters of the CLSM (one related to snow processes and three to soil properties) are calibrated against discharge observations with a multi-objective algorithm. First, the robustness of the CLSM parameterizations is tested by the Differential Split Sample Test (DSST). The simulations show good performances over a wide range of retrospective climatic conditions, except when the parameters are calibrated over a period with a large contribution of snowmelt to annual mean discharge. Then, the use of a climate change scenario reveals that the parameterizations of soil moisture processes in the CLSM are responsible for an increasing dispersion among simulations when facing dry and warm conditions. However, the differences between the simulated changes of river discharge remain very small. This work shows that calibration conveys some uncertainties, but they are moderate in the studied case, and pertain to the most conceptual parameterizations of this physically-based model.     

Evaluating model performance: towards a non-parametric variant of the Kling-Gupta efficiency

ABSTRACT

Goodness-of-fit measures are important for an objective evaluation of runoff model performance. The Kling-Gupta efficiency (R_KG), which has been introduced as an improvement of the widely used Nash-Sutcliffe efficiency, considers different types of model errors, namely the error in the mean, the variability, and the dynamics. The calculation of R_KG is implicitly based on the assumptions of data linearity, data normality, and the absence of outliers. In this study, we propose a modification of R_KG as an efficiency measure comprising non-parametric components, i.e. the Spearman rank correlation and the normalized flow–duration curve. The performances of model simulations for 100 catchments using the new measure were compared to those obtained using R_KG based on a number of statistical metrics and hydrological signatures. The new measure resulted overall in better or comparable model performances, and thus it was concluded that efficiency measures with non-parametric components provide a suitable alternative to commonly used measures.     

LE TRITIUM DANS LES PRÉCIPITATIONS DE L'ASIE DU SUD-EST ET LE PHÉNOMÉNE DE LA MOUSSON EN 1966 ET EN 1967

Abstract

The problem of non-steady flow of water in a soil-plant system can be described by adding a sink term to the continuity equation for soil water flow. In this paper the sink term is defined in two different ways. Firstly it is considered to be dependent on the hydraulic conductivity of the soil, on the difference in pressure head between the soil and the root-soil interface and some root effectiveness function. Secondly the sink is taken to be a prescribed function of the soil water content. The partial differential equation applying to the first problem is solved by both a finite difference (FD 1) and a finite element (FE 1) technique, that applying to the second problem by a finite difference approach (FD 2). The purpose of this paper is to verify the numerical models against field measurements, to compare the results obtained by the three numerical methods and to show how the finite element method can be applied to complex but realistic two-dimensional flow situations. Two examples are given. The first concerns one-dimensional flow and it compares numerical results with those obtained experimentally in the field from water balance studies on red cabbage (Brassica oleracea L. ‘Rode Herfst’) grown on a clay soil in the presence of a water table. The second example describes two-dimensional flow in a complex field situation in the Netherlands where flow takes place under cropped field conditions through five anisotropic layers. Water is supplied to the system by infiltration from two unlined ditches and is withdrawn from the system by evapotranspiration and by leakage to an underlying pumped aquifer.     

基于多目标优化的有限角度图像重建算法及实现

本文综合考虑了图像局部和整体的平滑性,以及最大熵准则,重新建立新的多目标优化模型,并在求解过程中引入同伦参数加快收敛速度。通过仿真实验,验证了所提算法能较好地改善有限角度下图像重建的质量及提高重建速度。     

Life-history- and ecosystem-driven variation in composition and residence pattern of seabream species (Perciformes: Sparidae) in two Mediterranean coastal lagoons

Species composition and length-frequency distributions of six sparid fish species were investigated in two central Mediterranean coastal lagoons off the western coast of Italy: Fogliano and Caprolace. In the former, the sparid fauna was dominated by the gilt-head seabream (Sparus aurata), whereas in Caprolace, species composition was more homogeneous across all six species. Size structure varied considerably among species: S. aurata, Diplodus puntazzo and Diplodus vulgaris had a single-cohort structure in both lagoons, whereas in Diplodus annularis and Diplodus sargus at least two cohorts were identified. In Lithognathus mormyrus inter-lagoon variation was detected, with a single-cohort structure in Fogliano and a two-cohort structure in Caprolace. While inter-specific differences can be explained by variation in life-history strategies among species, intra-specific variation in L. mormyrus is likely to be determined by the known differences between the two habitats: Fogliano being a more confined lagoon, and Caprolace more extensively influenced by the sea.     

A multi-objective calibration approach using in-situ soil moisture data for improved hydrological simulation of the Prairies

ABSTRACT

Traditionally, hydrological models are only calibrated to reproduce streamflow regime without considering other hydrological state variables, such as soil moisture and evapotranspiration. Limited studies have been performed on constraining the model parameters, despite the fact that the presence of a large number of parameters may provide large degree of freedom, resulting in equifinality and poor model performance. In this study, a multi-objective optimization approach is adopted, and both streamflow and soil moisture data are calibrated simultaneously for an experimental study basin in the Saskatchewan Prairies in western Canada. The results of this study show that the multi-objective calibration improves model fidelity compared to the single objective calibration. Moreover, the study demonstrates that single objective calibration performed against only streamflow can fairly mimic the streamflow hydrograph but does not yield realistic estimation of other fluxes such as evapotranspiration and soil moisture (especially in deeper soil layers).