高层建筑结构抗震选型的智能评价方法

指出了结构整体性能实现的阶段性特征，明确了选型阶段是对结构整体性能进行规划，控制与优化等的关键环节，初步给出了从不同角度表征结构及其相关全系统。全寿命，全性能等客观需求与约束的结构选型性能模型。接着分析介绍了四种智能评价方法（即ES、FIS、ANN、ANNFIS法），给出了集成智能评价支持系统的功能结构框图，在介绍了七种Mamdani模糊推理算法的基础上，将模糊综合评价与模糊推理方法结合。给出了一种兼有两者共同优点的多输入加权模糊推理与去模糊化算法及该算法用于性能评价的过程，最后，通过基于该算法的性能评价实例，给出了结构受力合理性性能评价的七条模糊规则及输入输出参数的评价等级标准，隶属函数及其加权模糊推理的过程与结果，结果表明该方法能较好地表征与利用专家知识，经验与策略，能更好地满足结构选型多性能，多层次的性能评价需要，且评价过程与结果直观，有效。上述工作为表征，量化，控制与优化结构性能，恰当利用与改进各类结构。提高选型质量等奠定了基础。     

评价地质因素对地震灾害风险影响的一个模糊集方法

提出了地质因素对震害风险影响的评价问题 ,分析与选择了有关的地层因素指标。给出了指标的量化表达方式与论域空间 ,从烈度的角度对不同地震、震中距、区域条件下的建筑物震害数据进行了归一化处理。针对邢台、海城等大震的震害与地层资料 ,建立了地层岩性、深度、厚度等因素与震害程度之间的模糊因果律推理模型 ,给出了多维可能性 -概率模糊风险的算法。由模糊信息传递的方式解决了可能性 -概率风险分析过程中地层与震害变量空间分离的问题 ,并在此基础上完成了可以衡量相关因素之“贡献”量的风险评价方法。然后通过实例说明了如何用这种方法针对地层因素进行震害风险评价     

基于一致矩阵排序的混凝土延性柱耗能器性能分析

为确定混凝土延性柱耗能器工程推荐应用类型,以混凝土延性柱耗能器性能模拟实验数据为基础,对5类试件以强度系数、延性系数和破坏形态为评价指标,采用基于模糊一致矩阵的决策方法,进行了混凝土延性柱耗能器的综合评价,建立了混凝土延性柱耗能器类型优选的改进模糊一致矩阵数学模型,为耗能器类型的选择提供了影响因素量化排序的分析方法。实证分析结果表明,改进后的模糊一致矩阵决策方法能更好地反映参评对象的实际情况.     

交通系统地震应急决策模型与方法

为了提高震后应急救援工作的效率,开展交通系统地震应急决策模型与方法研究,建立一套反映地震灾害情况下交通系统应急决策问题特点的方法和模型体系,为开发交通系统地震应急决策辅助支持系统提供模型支持,以实现震后交通调配决策的智能化。把双层规划方法、随机模拟方法以及模糊决策方法引入到震后交通应急调配决策的分析中,重点研究了基于交通系统畅通可靠性分析的应急决策优化问题。论文的主要研究内容概述如下:(1)介绍了四阶段交通需求预测方法及震后修正四阶段法,对震后应急交通需求的特征进行分析,建立了震后应急交通需求预测模型,提出了三阶段震后应急交通需求预测方法;分析了路桥单元的地震易损性,给出了路桥单元的震害等级,推导了路桥单元震害指数的临界区间,提出了路桥单元的地震影响修正系数区间,进而建立了震后路桥可能通行能力的评估模型;对路网容量及其计算方法进行了分析,给出了震后路网容量的计算过程。(2)介绍了单元可靠性和交通系统可靠性的基本概念,阐述了道路网络的布局形式及其性能,分析了交通系统抗震性能影响因素;利用不交化最小路集法对震后交通系统连通可靠性进行分析;依据公路服务水平分级标准,定义了震后公路服务水平阀值,引入了蒙特卡罗法,分析了交通系统的畅通可靠性,进而提出了基于服务水平的交通系统畅通可靠性评估方法,给出了评估标准,并构建了交通系统的畅通可靠性评估优化流程;为震后交通系统应急决策方法的研究提供理论依据。(3)科学的应急决策是震后救灾工作高效开展的有力保证,本文介绍了决策的基本概念、模糊决策分析方法以及数据标准化的处理方法;地震应急服务点选址决策是地震应急救援决策工作的重要基础,本文分析了地震应急服务点选址问题的不确定性,介绍了直觉模糊数和区间直觉模糊数的概念,在分析两者之间关系的基础上,定义了区间直觉模糊数的得分函数和精确函数,提出了它的排序规则;构建了约束中含有区间直觉模糊参数的地震应急服务点选址模型,提出了一种基于区间直觉模糊数排序规则的模型算法。(4)建立了基于模糊聚类分析的单服务点多需求点应急调配优化模型,并给出了模型的解法;论述了用区间数形式描述地震应急物资调配决策信息的可行性,推导了区间数可能度的算法,改进了区间数可能度矩阵,定义了基于区间数可能度的最优调配系数,构建了基于区间数可能度的单服务点多需求点应急调配优化模型,给出了模型的求解过程;考虑到震后应急物资调配是基于时间链的动态过程,假设调配至应急需求点的物资是被连续消耗的,进而建立了基于双层规划的多服务点多需求点应急调配优化模型,给出了双层规划问题的动态优化算法;构建了基于交通系统畅通可靠性分析的配流优化决策方法及体系。(5)引入设定地震法,对交通系统地震应急决策模型与方法进行理论集成研究,提出了基于不确定性分析的地震应急物资服务点选址决策优化方法、基于畅通可靠性分析的震后交通应急调配决策优化方法,并进而构建了基于设定地震的交通系统地震应急决策优化方法及体系,实现了交通系统地震应急决策理论的集成;并以宿城区县乡道路网为研究对象,引入设定地震法,对模型、方法及决策优化理论进行了模拟应用。     

高层钢筋混凝土结构抗震选型的模糊专家系统

本文在专家调查和文献资料收集事理的基础上，建立了高层钢筋混凝土结构抗震选型的影响因素。因素关系及其模糊量化的知识表示方法；针对结构选型因素的层次性，提出了层因素逐级综合的模糊推理方法；在开发工具Ｃ－ＡＤＶＩＳＯＲ上具体建造了此类结构抗震选型的专家系统。     

基于“投资—效益”准则的结构全寿命总费用模型

作为基于结构性能抗震设计的重要原则,“投资—效益”准则反映了抗震设计思想由只注重结构安全到综合考虑技术、经济、社会等诸多因素影响的转变,而结构性能水平和抗震性能目标是实现这一准则的前提和基础.采用五级结构性能水平细化现行规范给出的三水准设防,以层间位移角为定量指标,建立了结构性能水平与层间位移角限值之间一一对应的关系.提出抗震性能目标优化决策,根据最优设防烈度并引入地震危险性分析,建立了基于“投资—效益”准则的结构全寿命总费用模型.该模型规避了直接求解体系可靠度的复杂过程,将问题转化为求解结构的失效概率.其优点在于既能考虑结构的初始造价,又充分根据结构性能失效的特点考虑结构在各级性能水平下的损失期望,全面注重了结构性能、安全及经济等条件,体现了基于性能的抗震设计理念.     

高层建筑利用悬吊质量摆的减震研究

本文研究了高层建筑利用悬吊质量摆的减振作用，首次提出了利用摆减小高层建筑多个振型反应的方法。文中给出了具有不同悬吊方式的摆－高层建筑结构体系的力学模型及振动方程，利用数值分析方法探讨了摆的不同悬吊方式及摆与结构的质量比等因素对结构地震反应的影响，给出了对于实际工程应用具有参考意义的结果。     

地下管道震害预测实用方法的研究

本文根据震害调查分析和试验研究结果，建立了由环境因素和管道结构参对地下管道震害进行二级多因素模型综合评判的方法，文中给出了计算，评价构成二级评判因素地震烈，场地类型，工作状态和结构种类正态模糊范数ak的方法，并在地下管道震害关联分析和试验基础上，给出了二级模糊评判的权向量，本文为面广量大的已那建下管道的震害预测工作提供了一套方便实用的计算方法。     

超限高层建筑工程数据库及Bayes知识发现

针对上海市超限高层建筑工程实例,收集整理了相关设计信息,构建了能够表征这些设计信息的数据表并形成了数据库。鉴于传统的管理信息系统不能够空间表示,提出了一种新型的基于Arc-GIS的信息系统,同时引入了贝叶斯网络,以数据库样本为基础进行结构选型。实践证明,该系统充分利用了计算机的存贮、计算、统计、共享以及空间显示功能,贝叶斯网络为超限高层建筑结构体型优选提供了一种新的方法,对超限高层建筑工程的设计改进和规范的完善都具有一定的实际意义。     

基于模糊综合评价的道路震害预测评估方法

为给震后紧急救援决策提供科学合理的依据,文中运用模糊综合评价的方法,以道路桥梁、路基与路面、路边环境、公路隧道四个方面为主要因素,建立震后公路系统受损情况评价模型。通过具体实例来对模型建立过程中的一些关键问题,比如隶属函数的确定、评价结果量化等的处理方法进行详细阐述,并将评价结果与实际情况进行对比,以说明评价模型的可行性和有效性。     

Reservoir operation using grey fuzzy stochastic dynamic programming

This paper presents an optimal regulation programme, grey fuzzy stochastic dynamic programming (GFSDP), for reservoir operation. It is composed of a grey system, fuzzy theory and dynamic programming. The grey system represents data by covering the whole range without loss of generality, and the fuzzy arithmetic takes charge of the rules of reservoir operation. The GFSDP deals with the multipurpose decision‐making problem by fuzzy optimization theorem. The practicability and effectiveness of the proposed approach is tested on the operation of the Shiman reservoir in Taiwan. The current M5 operating rule curves of this reservoir also are evaluated. The simulation results demonstrate that this new approach, in comparison with the M5 rule curves, has superior performance with regard to the total water deficit and number of monthly deficits. Copyright © 2002 John Wiley & Sons, Ltd.     

Intelligent reservoir operation system based on evolving artificial neural networks

We propose a novel intelligent reservoir operation system based on an evolving artificial neural network (ANN). Evolving means the parameters of the ANN model are identified by the GA evolutionary optimization technique. Accordingly, the ANN model should represent the operational strategies of reservoir operation. The main advantages of the Evolving ANN Intelligent System (ENNIS) are as follows: (i) only a small number of parameters to be optimized even for long optimization horizons, (ii) easy to handle multiple decision variables, and (iii) the straightforward combination of the operation model with other prediction models. The developed intelligent system was applied to the operation of the Shihmen Reservoir in North Taiwan, to investigate its applicability and practicability. The proposed method is first built to a simple formulation for the operation of the Shihmen Reservoir, with single objective and single decision. Its results were compared to those obtained by dynamic programming. The constructed network proved to be a good operational strategy. The method was then built and applied to the reservoir with multiple (five) decision variables. The results demonstrated that the developed evolving neural networks improved the operation performance of the reservoir when compared to its current operational strategy. The system was capable of successfully simultaneously handling various decision variables and provided reasonable and suitable decisions.     

Comparison of grey and phase-space rainfall forecasting models using a fuzzy decision method / Comparaison grâce à une méthode de décision floue des modèles gris et d’espace des phases pour la prévision de pluie

Abstract

Abstract This work applies a fuzzy decision method to compare the performance of the grey model with that of the phase-space model, in forecasting rainfall one to three hours ahead. Four indices and two statistical tests are used to evaluate objectively the performance of the forecasting models. However, a trade-off must be made in choosing a suitable model because various indices may lead to different judgements. Therefore, a fuzzy decision model was applied to solve this problem and to make the optimum decision. The results of fuzzy decision making demonstrate that the grey model outperforms the phase-space model for forecasting one hour ahead, but the phase-space model performs better for forecasting two or three hours ahead.     

Multi-criteria decision making for seismic intensity measure selection considering uncertainty

Seismic intensity measure (IM) selection is associated with consideration of multiple criteria, and there are uncertainties within the selection process. In this paper, a novel multi-criteria decision making (MCDM) approach by incorporating stochastic multi-criteria acceptability analysis (SMAA) with technique for order preference by similarity to ideal solution (TOPSIS) is proposed to solve the stochastic decision making problem of IM selection. TOPSIS provides an alternative rank function, and the SMAA is used to address the uncertainties within the IM selection. The performance criteria (e.g., efficiency, proficiency, practicality, sufficiency, and correlation) are evaluated for the investigated structural components, and the decision matrix is formulated based on the criteria of each IM alternative. Furthermore, the importance of the component to system reliability is quantified in a probabilistic manner using nonlinear time history analysis and serves as the weighting factors in MCDM stage. The holistic acceptability indices indicating the overall acceptability levels of IM alternatives are computed by the proposed approach. Additionally, the effects of different IMs (e.g., average spectral acceleration, peak ground velocity, and spectral acceleration) on probabilistic seismic loss and resilience are investigated to further support the IM selection. The proposed approach is illustrated on a highway bridge, and the results are presented.     

基于区间直觉模糊数的地震应急服务点选址模型

地震应急物资储备服务点选址是地震应急救援决策工作的重要基础。本文分析了地震应急服务点选址问题的不确定性,介绍了直觉模糊数和区间直觉模糊数的概念,在分析两者之间关系的基础上,定义了区间直觉模糊数的得分函数和精确函数,进而提出了基于得分函数和精确函数的区间直觉模糊数的排序规则;行车时间受诸多因素影响,将行车时间看成区间直觉模糊信息,构建了约束中含有区间直觉模糊参数的地震应急服务点选址模型,提出了一种基于区间直觉模糊数排序规则的模型算法,可得到地震应急服务点最优选址方案。通过算例分析验证了该方法的有效性。     

Wavelet–copula‐based mutual information for rainfall forecasting applications

Under a climate change, the physical factors that influence the rainfall regime are diverse and difficult to predict. The selection of skilful inputs for rainfall forecasting models is, therefore, more challenging. This paper combines wavelet transform and Frank copula function in a mutual information‐based input variable selection (IVS) for non‐linear rainfall forecasting models. The marginal probability density functions (PDFs) of a set of potential rainfall predictors and the rainfall series (predictand) were computed using a wavelet density estimator. The Frank copula function was applied to compute the joint PDF of the predictors and the predictand from their marginal PDFs. The relationship between the rainfall series and the potential predictors was assessed based on the mutual information computed from their marginal and joint PDFs. Finally, the minimum redundancy maximum relevance was used as an IVS stopping criterion to determine the number of skilful input variables. The proposed approach was applied to four stations of the Nigerien Sahel with rainfall series spanning the period 1950–2016 by considering 24 climate indices as potential predictors. Adaptive neuro‐fuzzy inference system, artificial neural networks, and random forest‐based forecast models were used to assess the skill of the proposed IVS method. The three forecasting models yielded satisfactory results, exhibiting a coefficient of determination between 0.52 and 0.69 and a mean absolute percentage error varying from 13.6% to 21%. The adaptive neuro‐fuzzy inference system performed better than the other models at all the stations. A comparison made with KDE‐based mutual information showed the advantage of the proposed wavelet–copula approach.     

Evaluation of coalbed methane reservoirs from geophysical log data using an improved fuzzy comprehensive decision method and a homologous neural network

The evaluation of coalbed methane reservoirs using log data is an important approach in the exploration and development of coalbed methane reservoirs. Most commonly, regression techniques, fuzzy recognition and neural networks have been used to evaluate coalbed methane reservoirs. It is known that a coalbed is an unusual reservoir. There are many difficulties in using regression methods and empirical qualitative recognition to evaluate a coalbed, but fuzzy recognition, such as the fuzzy comprehensive decision method, and neural networks, such as the back-propagation (BP) network, are widely used. However, there are no effective methods for computing weights for the fuzzy comprehensive decision method, and the BP algorithm is a local optimization algorithm, easily trapped in local minima, which significantly affect the results. In this paper, the recognition method for coal formations is developed; the improved fuzzy comprehensive decision method, which uses an optimization approach for computing weighted coefficients, is developed for the qualitative recognition of coalbed methane reservoirs. The homologous neural network, using a homologous learning algorithm, which is a global search optimization, is presented for the quantitative analysis of parameters for coalbed methane reservoirs. The applied procedures for these methods and some problems related to their application are also discussed. Verification of the above methods is made using log data from the coalbed methane testing area in North China. The effectiveness of the methods is demonstrated by the analysis of results for real log data.     

神经网络反演双侧向电阻率测井曲线的物理约束

以一种新型高分辨率双侧向测井仪器在二维轴对称地层模型中的模拟响应为训练集训练BP神经网络，得到了针对该双侧向测井仪的反演网络模型。在训练中，神经网络结构的确定一般采用交叉验证法，但这种验证法经验性的成分偏重，不能完全解决网络结构的范化问题。为此，本文在模型的训练中，在交叉验证的基础上，根据双侧向测井的原理和仪器响应特性，提出了一种新的物理约束方法；反演地层电阻率的误差应随着侵入半径的增加而加大，违反此规律的模型不予采纳。理论研究结果表明，由此得到的神经网络模型具有很好的范化能力。     

Seismic response analysis using characteristic ground motion records for risk‐based decision‐making (3R method)

The concept of intensity‐based assessment for risk‐based decision‐making is introduced. It is realized by means of the so‐called 3R method (response analysis, record selection and risk‐based decision‐making), which can be used to check the adequacy of design of a new building or of the strengthening of an existing building by performing conventional pushover analysis and dynamic analysis for only a few ground motions, which are termed characteristic ground motions. Because the objective of the method is not a precise assessment of the seismic risk, a simple decision model for risk acceptability can be introduced. The engineer can decide that the reliability of a no‐collapse requirement is sufficient when collapse is observed in the case of less than half of, for example, seven characteristic ground motions. From the theoretical point of view, it is shown that the accuracy of the method is acceptable if the non‐linear response history analyses are performed at a low percentile of limit‐state intensity, which is also proven by means of several examples of multi‐storey reinforced concrete frame buildings. The 3R method represents a compromise between the exclusive use of either pushover analysis or dynamic analysis and can be easily introduced into building codes provided that its applicability is further investigated (e.g. asymmetric structures and other performance objectives) and that the procedure for the selection of characteristic ground motions is automated and readily available to engineers (www.smartengineering.si).