结构控制技术在桥梁工程中的应用综述

就桥梁结构的被动控制和主动控制以及控制算法等三方面,系统综述了桥梁工程中振动控制技术的应用现状与进展,总结了桥梁结构主、被动控制所取得的一些研究成果和结论,同时,指出目前研究工作中存在的尚待进一步完善的问题,提出了今后的研究方向。     

Estimation of unconfined aquifer parameters by genetic algorithms

Abstract

Unconfined aquifer parameters, viz. transmissivity, storage coefficient, specific yield and delay index from a pumping test are estimated using the genetic algorithm optimization (GA) technique. The parameter estimation problem is formulated as a least-squares optimization, in which the parameters are optimized by minimizing the deviations between the field-observed and the model-predicted time–drawdown data. Boulton's convolution integral for the determination of drawdown is coupled with the GA optimization technique. The bias induced by three different objective functions: (a) the sum of squares of absolute deviations between the observed and computed drawdown; (b) the sum of squares of normalized deviations with respect to the observed drawdown; and (c) the sum of squares of normalized deviations with respect to the computed drawdown, is statistically analysed. It is observed that, when the time–drawdown data contain no errors, the objective functions do not induce any bias in the parameter estimates and the true parameters are uniquely identified. However, in the presence of noise, these objective functions induce bias in the parameter estimates. For the case considered, defining the objective function as the sum of the squares of absolute deviations between the observed and simulated drawdowns resulted in the best possible estimates. A comparison of the GA technique with the curve-matching procedure and a conventional optimization technique, such as the sequential unconstrained minimization technique (SUMT), is made in estimating the aquifer parameters from a reported field pumping test in an unconfined aquifer. For the case considered, the GA technique performed better than the other two techniques in parameter estimation, with the sum-of-squares errors obtained from the GA about one fourth of those obtained by the curve matching procedure, and about half of those obtained by SUMT.

Citation Rajesh, M., Kashyap, D. & Hari Prasad, K. S. (2010) Estimation of unconfined aquifer parameters by genetic algorithms. Hydrol. Sci. J. 55(3), 403–413.     

Seasonal Short‐Term Prediction of Dissolved Oxygen in Rivers via Nature‐Inspired Algorithms

This study challenges the use of three nature‐inspired algorithms as learning frameworks of the adaptive‐neuro‐fuzzy inference system (ANFIS) machine learning model for short‐term modeling of dissolved oxygen (DO) concentrations. Particle swarm optimization (PSO), butterfly optimization algorithm (BOA), and biogeography‐based optimization (BBO) are employed for developing predictive ANFIS models using seasonal 15 min data collected from the Rock Creek River in Washington, DC. Four independent variables are used as model inputs including water temperature (T), river discharge (Q), specific conductance (SC), and pH. The Mallow's Cp and R² parameters are used for choosing the best input parameters for the models. The models are assessed by several statistics such as the coefficient of determination (R²), root‐mean‐square error (RMSE), Nash–Sutcliffe efficiency, mean absolute error, and the percent bias. The results indicate that the performance of all‐nature‐inspired algorithms is close to each other. However, based on the calculated RMSE, they enhance the accuracy of standard ANFIS in the spring, summer, fall, and winter around 13.79%, 15.94%, 6.25%, and 12.74%, respectively. Overall, the ANFIS‐PSO and ANFIS‐BOA provide slightly better results than the other ANFIS models.     

Improving explicit time integration by modal truncation techniques

This paper describes a modal weighting technique that improves the stability characteristics of explicit time-integration schemes used in structural dynamics. The central difference method was chosen as the trial algorithm because of its simplicity, both in terms of formulation and ease of numerical stability and convergence analysis. It is shown how explicit algorithms may be reformulated in order to make them stable for any integration time by attenuating high-frequency oscillation modes that are generated by mesh geometry rather than generic dynamical features. We discuss results from trial calculations obtained from mathematical models that represent hysteretic restoring force elements and an application on a physical, four-degree-of-freedom, base-isolated structure using the pseudodynamic technique. © 1998 John Wiley & Sons, Ltd.     

On the importance of training methods and ensemble aggregation for runoff prediction by means of artificial neural networks

ABSTRACT

Artificial neural networks (ANNs) become widely used for runoff forecasting in numerous studies. Usually classical gradient-based methods are applied in ANN training and a single ANN model is used. To improve the modelling performance, in some papers ensemble aggregation approaches are used whilst in others, novel training methods are proposed. In this study, the usefulness of both concepts is analysed. First, the applicability of a large number of population-based metaheuristics to ANN training for runoff forecasting is tested on data collected from four catchments, namely upper Annapolis (Nova Scotia, Canada), Biala Tarnowska (Poland), upper Allier (France) and Axe Creek (Victoria, Australia). Then, the importance of the search for novel training methods is compared with the importance of the use of a very simple ANN ensemble aggregation approach. It is shown that although some metaheuristics may slightly outperform the classical gradient-based Levenberg-Marquardt algorithm for a specific catchment, none performs better for the majority of the tested ones. One may also point out a few metaheuristics that do not suit ANN training at all. On the other hand, application of even the simplest ensemble aggregation approach clearly improves the results when the ensemble members are trained by any suitable algorithms.

EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR E. Toth     

现代测量数据绘制技术研究

介绍完成几何数据至图像数据的转变的有关算法 ,并讨论了体绘制技术的理论和方法 ,为最终实现测量数据可视化提供了技术基础。     

A memory-matrix-based identification methodology for structural and mechanical systems

This paper uses an associative memory approach to identify the properties of structural and mechanical systems. The methodology differs from standard identification methods in that it uses a set of parameter vectors simultaneously to generate the estimated parameter vector. The method develops a technique for sequentially generating genetically engineered relevant parameter vectors whose use results in accurate identification, while still using small data sets. This makes the approach promising for on-line, rapid, identification of structures and their health monitoring. © 1998 John Wiley & Sons, Ltd.     

A comparison of hillslope drainage area estimation methods using high-resolution DEMs with implications for topographic studies of gullies

Topographic models provide a useful tool for understanding gully occurrence in the landscape but require reliable estimates of gully head drainage areas. Modern high-resolution topography data (collected using structure from motion photogrammetry or light detection and ranging) is increasingly used for topographic studies of gullies, but little work has been done to assess the variability of gully head drainage area estimates using different methods. This study evaluated alternative approaches to using high-resolution digital elevation models (DEMs) so that gully topographic models can be more readily applied to any area with suitably high-resolution data. Specifically, we investigated the impact of single- or multiple-direction flow routing algorithms, DEM hydrologic-enforcement procedures and spatial resolution on gully head drainage area estimation. We tested these methods on a 40 km² site centred on Weany Creek, a low-relief semi-arid landscape draining towards the Great Barrier Reef, Australia. Using a subroutine to separate gully heads into those with divergent or convergent flow patterns upslope, we found that divergent flow conditions occurred at half of 484 studied gullies. Drainage areas estimated by different flow routing algorithms were more variable in these divergent cases than for convergent cases. This variation caused a significant difference between topographic threshold parameters (slope b and intercept k) derived from single- or multiple-direction flow routing algorithms, respectively. Different methods of hydrologic enforcement (filling or breaching) also affected threshold analysis, resulting in estimates of the exponent b being ~188% higher if the DEM was filled than if breached. The testing of the methods to date indicates that a finer resolution (≤2 m) DEM and a multiple-direction flow routing algorithm achieve the most realistic drainage area estimates in low-relief landscapes. For Weany Creek we estimated threshold parameters k = 0.033 and b = 0.189, indicating that it is highly susceptible to gully erosion.     

基于遗传算法地震短期综合预报分类系统在天山地震带的应用

应用“基于遗传算法的地震预报分类系统”建立天山地震带8个区的地震短期综合预报模型。在前人研究基础上对此分类系统的应用方法做了以下改进：以半年或一年样本窗内的地震活动资料预报未来3个月最大地震;采用阈值划分异常,区分高值和低值异常两类参数,再用异常持续时间进行地震环境信息编码;在研究区内,根据一年和半年样本窗和震级错半级的分档形式,形成4种地震环境信息编码方式。计算机自动运行此系统程序,寻找各自的预报模型。对预留样本的检验按震级区间进行规则对应率、虚报率和地震对应率、漏报率的效能评价,结果表明南天山各区检验情况好于北天山;对北天山预测出5级以上、南天山6级以上地震以及能给出有效重叠震级区间的模型加权后认为南天山中西段、柯坪块体区检验情况较好。结果还表明半年样本窗检验效能普遍高于一年窗,对较低震级的检验情况一般要好于较高震级。     

基于鲸鱼优化算法的长短期记忆模型水库洪水预报

洪涝灾害是世界主要自然灾害之一,优化洪水预报方案对防洪决策至关重要,然而传统水文模型存在参数多、调参受人为因素影响,泛化能力弱等问题。针对上述问题,本文提出基于改进的鲸鱼优化算法和长短期记忆网络构建自动优化参数的WOA-LSTM模型,通过优化神经网络结构进一步增强该模型的稳定性和精确度,并且建立不同预见期下的洪水预报模型来分析讨论神经网络结构与预报期之间的关系。以横锦水库流域1986—1997年洪水资料为例,其中以流域7个雨量站点的降雨以及横锦站水文资料为输入,不同预见期下洪水过程作为输出,以1986—1993年作为模型的率定期,1994—1997年作为模型的检验期,研究结果表明:(1)以峰现时差、确定性系数、径流深误差和洪峰流量误差作为评价指标,相比较于LSTM模型和新安江模型对检验期的模拟结果表明WOA-LSTM模型拥有更高的精度、预报结果更稳定;(2)结合置换特征值和SHAP法分析模型特征值重要性,增强了神经网络模型的可解释性;(3)通过改变神经网络结构在一定程度避免由于预见期增加和数据关联性下降而导致的模型预报精度下降的问题,最终实验表明该模型在预见期1~6 h下都可以满足横锦水库的洪水预报要求,可以为当地的防洪决策提供依据。