膜结构的概念设计与形态优化

膜结构是一种建筑与结构完美融合的结构形式,"形"和"态"是膜结构设计中的重要概念.为实现形态设计的合理性,国内外学者已开展了大量研究,提出多种形态分析方法.但是形态分析并不等同于形态设计,要实现设计合理性还需要在概念设计和分析结果优化两方面开展工作.本文首先介绍了一些典型膜结构的建筑造型和力学特点,探讨了膜结构概念设计的一些基本原则;然后提出了以"刚度最大"、"受荷后应力分布最均匀"和"支反力最小"为优化目标的基于遗传算法的膜结构多目标形态优化方法;最后应用该方法对几种典型的膜结构进行了形态优化分析,得到了一些可用于指导膜结构概念设计的参数较优取值范围.     

基于遗传算法的飞艇结构多目标优化

首先,根据软式飞艇结构的力学特点建立了飞艇结构的多目标优化模型,以外荷载作用下飞艇"应力最小"、"变形最小"及"自重最小"为优化目标,确定膜材厚度、膜材弹性模量、飞艇内气压及飞艇细长比为优化变量。其次通过加权系数法将多目标优化转化为单目标问题,并采用遗传算法进行求解。最后,采用此方法对某一飞艇结构进行多目标优化。研究结果表明:飞艇的优化模型是有效的;结合加权系数法和遗传算法,可以获得各个目标均较理想的优化结果;通过进行多目标优化可以为飞艇结构的设计提供合理建议。     

基于遗传算法的飞艇结构多目标优化

首先,根据软式飞艇结构的力学特点建立了飞艇结构的多目标优化模型,以外荷载作用下飞艇“应力最小”、“变形最小”及“自重最小”为优化目标,确定膜材厚度、膜材弹性模量、飞艇内气压及飞艇细长比为优化变量.其次通过加权系数法将多目标优化转化为单目标问题,并采用遗传算法进行求解.最后,采用此方法对某一飞艇结构进行多目标优化.研究结果表明：飞艇的优化模型是有效的;结合加权系数法和遗传算法,可以获得各个目标均较理想的优化结果;通过进行多目标优化可以为飞艇结构的设计提供合理建议.     

索穹顶结构预应力多目标优化的小生境遗传算法

根据索穹顶结构的柔性结构特点和力学特性,探索以质量最小、刚度最大和支座反力最小为目标函数,以预应力整体可行性准则、荷载态应力控制和位移限值为约束条件的多目标优化模型。结合自适应技术、预选择机制和共享函数小生境技术对传统遗传算法进行改进,利用加权系数法将多目标优化问题转换为单目标Pareto最优解问题。利用向量式有限元对不同预应力水平的结构进行静力分析。最后通过两个不同类型索穹顶算例,采用自编程序的计算结果证明所提出的方法是有效的,为索穹顶结构预应力优化分析提供了新的计算方法。     

脊谷式膜结构的形态优化分析

建立了以刚度最大、应力分布最均匀和支座反力最小为优化目标,以遗传算法为优化算法的膜结构多目标形态优化方法,对脊谷式膜结构进行了形态优化分析.揭示了在不同荷载形式下脊谷式膜结构的最优形态及其对应的结构参数关系,探讨了荷载大小、结构跨度及膜材预应力等参数对最优形态的影响,并给出了雪荷载与风荷载的组合作用下脊谷式膜结构最优设计参数取值范围,为脊谷式膜结构的设计提供了明确的指导.     

基于B样条曲线的自由曲面索支撑空间网格结构多目标形态优化

对自由曲面索支撑空间网格结构进行多目标形态优化研究。首先根据索支撑空间网格结构的建模方法,将优化结构整个曲面转化为优化结构的准线和母线。其次为保证优化后的自由曲面光顺,引进B样条曲线建立曲面,并将B样条曲线已知点作为优化变量。出于不同结构设计需要,选用力学指标和几何指标、或力学指标和经济指标、或不同性能力学指标组合作为多优化目标进行结构优化。得到结论如下:首先,采用本优化方法,优化前后曲面形状变化不大,适用于对给定初始曲面结构进行形态优化。其次,以应变能和用钢量为优化目标,属于融合了截面优化和形态优化的多目标优化,其对于索支撑空间网格结构效果较好,具有较高的工程实用价值。最后,以应变能和杆件长度均方差为优化目标,所得优化结果对杆长均方差和应变能加权系数变化不敏感,可满足杆长均匀性和结构力学要求。     

自由曲面结构多目标形态优化北大核心CSCD

根据自由曲面的形成机理,建立自由曲面结构的多目标优化模型。以控制点坐标为优化变量,以应变能密度、特征屈曲值为目标函数,运用遗传算法,采用MATLAB与ANSYS分析软件,对自由曲面进行多目标优化。研究表明,所提出的优化方法适用性强、优化效果较好,可用于各种自由曲面的形态优化。     

自由曲面结构多目标形态优化

根据自由曲面的形成机理,建立自由曲面结构的多目标优化模型。以控制点坐标为优化变量,以应变能密度、特征屈曲值为目标函数,运用遗传算法,采用MATLAB与ANSYS分析软件,对自由曲面进行多目标优化。研究表明,所提出的优化方法适用性强、优化效果较好,可用于各种自由曲面的形态优化。     

单层柱面索撑网壳结构的形态优化

索支撑空间网格结构是一种适合于玻璃采光顶结构特点的新型单层网壳结构形式。目前对于索支撑空间网格的研究主要集中在几何建模、网格划分、整体稳定性能和静力性能等方面,在索支撑空间网格结构形态优化方面国内外的研究和应用还相对较少。采用了基于结构整体应变能为目标函数的共轭梯度法对单层柱面索撑网壳结构进行形态优化。首先,介绍了形态优化方法的选取,然后介绍了以应变能为目标函数的共轭梯度法。最后针对单层柱面索撑网壳,以结构的整体应变能为目标函数,曲面高度作为设计变量,结构的位移和矩形钢管的最大应力作为状态变量,采用共轭梯度法,通过调整曲面高度,寻找到了具有最小应变能的高刚度的较优曲面,并对该曲面进行了理论分析。     

基于工程限值约束的结构拓扑优化设计分析

基于固体各向同性材料惩罚密度插值模型,建立近似支撑框架模型,以最大刚度为优化目标,以优化体积比及工程限值为约束条件,对支撑框架结构进行了优化。工程限值约束包括最大、最小尺寸约束,对称性约束以及顶点位移约束等。研究结果表明：当支撑框架中斜撑的斜交角度为39°~59°时,框架具有较好的抗侧刚度;当优化体积比为0.3时,优化后结构顶点位移较原设计域增加19%。引入工程限值约束条件能使拓扑优化后的结果有效应用于实际工程,优化后结构具有框架形态,无不易建造的细微结构,减少了低效材料分布,从而降低造价。     

Multi-objective optimal design of braced frames using hybrid genetic and ant colony optimization

In this article, multi-objective optimization of braced frames is investigated using a novel hybrid algorithm. Initially, the applied evolutionary algorithms, ant colony optimization (ACO) and genetic algorithm (GA) are reviewed, followed by developing the hybrid method. A dynamic hybridization of GA and ACO is proposed as a novel hybrid method which does not appear in the literature for optimal design of steel braced frames. Not only the cross section of the beams, columns and braces are considered to be the design variables, but also the topologies of the braces are taken into account as additional design variables. The hybrid algorithm explores the whole design space for optimum solutions. Weight and maximum displacement of the structure are employed as the objective functions for multi-objective optimal design. Subsequently, using the weighted sum method (WSM), the two objective problem are converted to a single objective optimization problem and the proposed hybrid genetic ant colony algorithm (HGAC) is developed for optimal design. Assuming different combination for weight coefficients, a trade-off between the two objectives are obtained in the numerical example section. To make the final decision easier for designers, related constraint is applied to obtain practical topologies. The achieved results show the capability of HGAC to find optimal topologies and sections for the elements.     

型钢混凝土柱多目标优化设计方法研究

为探讨适用于组合结构构件的多目标优化设计方法,以型钢混凝土柱多目标优化设计为研究背景,在工程结构优化理论及组合结构设计理论的基础上,将优化目标设定为工程造价最小化和斜截面抗剪承载力最大化,采用线性加权法构造评价函数,通过调整加权系数来改变两个优化目标在优化中的重要程度,同时选择灵敏度较高的设计变量,综合考虑各种约束条件,给出了型钢混凝土柱多目标优化设计的数学模型,并借助复形法的优化思想,利用MATLAB编程求解有约束条件的非线性优化问题。优化设计实例表明,所采取的优化方法和优化思路对型钢混凝土柱优化设计是可行、有效的,可为组合结构构件的优化设计及工程应用提供参考。     

Energy-based design optimization of steel building frameworks using nonlinear response history analysis

Presented in this paper is a design optimization method for steel building frameworks subjected to seismic loading using a nonlinear response history analysis procedure. Minimum weight, minimum seismic input energy and maximum hysteretic energy of fuse members are identified as the three design objectives. Design constraints include the limits on inter-story drift and plastic rotation of member sections. The design optimization method employs a multi-objective genetic algorithm to search for optimal member section sizes from among commercially available steel section shapes. The design method is illustrated for a moment-resisting steel frame of a three-story building. It is concluded the proposed optimization methodology is an effective and efficient application of the capacity-design principle to building frameworks under earthquake loading.     

A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems

Integrated building design is inherently a multi-objective optimization problem where two or more conflicting objectives must be minimized and/or maximized concurrently.Many multi-objective optimization algorithms have been developed;however few of them are tested in solving building design problems.This paper compares performance of seven commonly-used multi-objective evolutionary optimization algorithms in solving the design problem of a nearly zero energy building(n ZEB) where more than 1.610 solutions would be possible.The compared algorithms include a controlled non-dominated sorting genetic algorithm witha passive archive(p NSGA-II),a multi-objective particle swarm optimization(MOPSO),a two-phase optimization using the genetic algorithm(PR_GA),an elitist non-dominated sorting evolution strategy(ENSES),a multi-objective evolutionary algorithm based on the concept of epsilon dominance(ev MOGA),a multi-objective differential evolution algorithm(sp MODE-II),and a multi-objective dragonfly algorithm(MODA).Several criteria was used to compare performance of these algorithms.In most cases,the quality of the obtained solutions was improved when the number of generations was increased.The optimization results of running each algorithm20 times with gradually increasing number of evaluations indicated that the PR_GA algorithm had a high repeatability to explore a large area of the solution-space and achieved close-to-optimal solutions with a good diversity,followed by the p NSGA-II,ev MOGA and sp MODE-II.Uncompetitive results were achieved by the ENSES,MOPSO and MODA in most running cases.The study also found that 1400-1800 were minimum required number of evaluations to stabilize optimization results of the building energy model.     

基于快速群搜索算法的双层球面网壳多目标优化

提出一种新的群智能算法,即多目标快速群搜索优化（MQGSO）算法,以双层球面网壳为研究对象,进行结构节点最大位移最小化和结构质量最轻化的多目标优化设计,网壳优化后的Pareto前沿分布均匀且范围较广,分别对多目标优化和单目标优化后的结构进行多维地震作用时程响应分析。结果表明:多目标优化得到的结构优于单目标的优化结果,所提出的多目标快速群搜索算法能很好地实现复杂空间结构的多目标优化设计。     

Fuzzy Control of Base-Isolation System Using Multi-Objective Genetic Algorithm

Abstract:   In this study, a friction pendulum system (FPS) and a magnetorheological (MR) damper are employed as the isolator and supplemental damping device, respectively, of a smart base-isolation system. Neuro-fuzzy models are used to represent dynamic behavior of the MR damper and FPS. A fuzzy logic controller (FLC) is used to modulate the MR damper so as to minimize structural acceleration while maintaining acceptable base displacement levels. To this end, a multi-objective optimization scheme that uses a nondominated multi-objective genetic algorithm (NSGA-II) is used to optimize parameters of membership functions and find appropriate fuzzy rules. To demonstrate the effectiveness of the proposed multi-objective genetic algorithm for FLC, a numerical study of a smart base-isolation system is conducted using several historical earthquakes. It is shown that the proposed method can find optimal fuzzy rules and that the NSGA-II-optimized FLC outperforms not only a passive control strategy but also a human-designed FLC and a conventional semiactive control algorithm.     

Topology optimization method of lattice structures based on a genetic algorithm

A two-stage topology optimization method of lattice structures based on a genetic algorithm is proposed. The first stage is the form-finding analysis of lattice structures, and the optimal initial shape was achieved with the numerical inverse hanging method. The second stage is the topology optimization of single-layer lattice structures, which can be realized by changing the mesh size and the tube configurations to minimize the total weight of steel tubes subject to the design requirements. The mesh configuration optimization is realized through the adjustment of the nodal horizontal co-ordinates and the removal of tubes with lower stress. The maximum displacement of the structure, the maximum stress of the circular steel tubes, and the nonlinear buckling load are the state variables, and a genetic algorithm (GA) is the optimization algorithm. Different stress-limiting values used to delete the tubes were discussed. The numerical examples show that the two-stage topology optimization method for lattice structures proposed in this paper is correct and efficient. Furthermore, the forms of the optimized structure are rich, and the structure is lightweight and efficient.