Advanced energy‐based analyses of trusses employing hybrid metaheuristics

Total potential optimization using metaheuristic algorithm (TPO/MA) is an alternative method in structural analyses, and it is a black‐box application for nonlinear analyses. In the study, an advanced TPO/MA using hybridization of several metaheuristic algorithms is investigated to solve large‐scale structural analyses problems. The new generation algorithms considered in the study are flower pollination algorithm (FPA), teaching learning‐based optimization, and Jaya algorithm (JA). Also, the proposed methods are compared with methodologies using classic and previously used algorithms such as differential evaluation, particle swarm optimization, and harmony search. Numerical investigations were carried out for structures with four to 150 degrees of freedoms (design variables). It has been seen that in several runs, JA gets trapped into local solutions. For that reason, four different hybrid algorithms using fundamentals of JA and phases of other algorithms, namely, JA using Lévy flights, JA using Lévy flights and linear distribution, JA with consequent student phase, and JA with probabilistic student phase (JA1SP), are developed. It is observed that among the variants tried, JA1SP is seen to be more effective on approaching to the global optimum without getting trapped in a local solution.     

混沌扰动启发式蚁群算法及其在边坡非圆弧临界滑动面搜索中的应用

通过引入混沌扰动算子增加解的多样性和提高全局寻优能力，另外通过构造蚂蚁的启发式搜索方式提高对局部最优解的搜索能力，从而有效地克服了基本蚁群算法容易出现停滞和搜索效率低的缺陷。还利用Spencer法和Janbu法，探讨了所提出的具有混沌扰动算子启发式蚁群算法在边坡稳定性分析中的应用。实例计算和对比分析结果表明，该法有效而又可靠。     

基于v-SVR和MVPSO算法的边坡位移反分析方法及其应用

 针对传统粒子群算法存在搜索空间有限、容易陷入局部最优点的缺陷,通过引入迁徙算子和自适应变异算子,提出基于粒子迁徙和变异的粒子群优化(MVPSO)算法。基准测试函数结果表明,改进的MVPSO算法较传统的粒子群优化算法在收敛效率上有大幅度提高,在处理非线性、多峰值的复杂优化问题中能快速地搜索,得到全局最优解。应用改进的MVPSO算法搜索最佳的支持向量机(v-SVR)模型参数,建立岩体力学参数与岩体位移之间的非线性支持向量机模型,提高v-SVR的预测精度和推广泛化性。然后,利用v-SVR模型的外推预测替代耗时的FLAC正向计算,利用改进的MVPSO算法搜索岩体力学参数的最优组合,提出v-SVR和MVPSO相结合的边坡位移反分析方法(v-SVR-MVPSO算法),与传统的BP-GA算法和v-SVR-GA算法相比,该算法在反演精度和反演效率上均有较大幅度提高。最后,将本文发展的v-SVR-MVPSO算法应用到大岗山水电站右岸边坡岩体参数反演分析,并对边坡后续开挖位移和稳定性进行预测,取得较好的效果。     

Design optimization of truss structures with continuous and discrete variables by hybrid of biogeography‐based optimization and differential evolution methods

This paper presents a hybrid BBO‐DE algorithm by hybridizing biogeography‐based optimization (BBO) and differential evolution (DE) methods for optimum design of truss structures with continuous and discrete variables. In BBO‐DE, the migration operator of BBO method serves as a local exploiter mechanism during the search process. Besides, DE has a role of the global exploration by performing multiple search directions in the search space to preserve more diversity in the population. By embedding of DE algorithm in BBO method as a mutation mechanism, the balance between the exploration and exploitation abilities is further improved. The comparative results with some of the most recently developed methods demonstrate the fast convergence properties of the proposed algorithm and confirm its effectiveness to solve optimum design problems of truss structures with continuous and discrete variables.     

四面体网格修匀算法研究

对有限元分析中网格优化的算法进行了研究。基于单元形状的度量准则,构造了与不可微目标函数等价的可微目标函数,建立了四面体网格修匀的优化模型。为了尽量避免陷入局部优化,采用了BFGS与混沌搜索相结合的求解算法,提高了获得全局最优解的概率。算例结果表明,该优化算法易于实现,稳定性好,效率较高,能够用于实际的网格优化。     

基于模糊动态规划法的关键链缓冲确定新方法

针对当前关键链项目管理技术缓冲确定过于主观,评价因素中定性指标无法定量表示的现状,将模糊优选理论与动态规划最优化原理相结合,提出了基于模糊动态规划法的关键链缓冲确定新方法。该方法在通过专家评议法得出项目中的富余工期后,结合语气算子与定量标度之间的对应关系将定性指标定量化,综合考虑项目工期、质量、成本指标之间矛盾统一的关系,对项目各道工序的工期进行压缩,并根据模糊决策集优越性二元对比矩阵及决策序列相对优属度总和最大法来确定关键链缓冲,并在实例分析中用软件模拟,验证了该方法的有效性。     

禁忌模拟退火复合形法及其在边坡稳定性分析中的应用

对于随机生成的初始复形，首先，选取与其他顶点相似程度最大的顶点进行禁忌退火操作，禁忌退火产生新顶点的区域限于该点和其余各顶点的中心点连线上，若关于该点的禁忌退火没有产生新的顶点，即禁忌退火失败，则选取与其他顶点相似程度次大的顶点进行退火，直至关于复形全部顶点的退火操作均失败；然后，对当前的复形进行改进的复合形法计算，采用与基本复合形法类似的映射收缩算子迭代至收敛。禁忌模拟退火复合形法不仅考虑了目标函数的改进，而且保持了各项点的多样性，避免了基本复合形法容易陷入局部极优的缺点。对2个非均质土坡的临界滑动面进行搜索表明，该方法具有较高的寻优成功率。     

高地应力下大型地下洞室群开挖顺序 与支护参数组合优化的智能方法

 针对高地应力下围岩变形破坏的特殊性以及大型地下洞室群开挖支护优化计算量大的特点,在三维弹塑性数值计算的基础上,采用反映高地应力下脆性岩石变形破坏特点的新本构模型,提出基于弹性释放能、塑性区体积、洞室周边位移与支护费用的地下洞室群开挖顺序与支护参数组合方案的综合优化新指标,综合集成粒子群与支持向量机的智能技术,提出高地应力下地下洞室群开挖顺序与支护参数的智能优化新方法。该方法通过典型施工方案的数值计算构建学习样本,采用支持向量机方法对样本进行学习与预测,建立起施工方案与综合优化指标之间的非线性映射关系,在具有一定约束条件的全局空间下,通过粒子群优化算法搜索出开挖顺序与支护参数的全局最优组合方案。将该方法应用于高地应力区黄河拉西瓦水电站地下厂房洞室群的开挖顺序和支护参数优化分析,结果表明该方法的可行性。     

整数编码遗传算法离散变量优化设计

提出了一种具有较强局部搜索能力的整数编码遗传算法,给出了离散变量结构优化的数学模型和适应度函数,它采用动态边界约束、有限变异算子、( - 1,0,1)规划算子等策略来改进算法。算例表明本文算法收敛速度快、收敛平稳     

AVO岩性参数反演的改进人工鱼群算法研究

为及时获取地震岩性参数,提出了基于动物自治体模型的人工鱼群算法进行参数反演。该方法对鱼群算法采取分阶段策略进行改进,并增加了跳跃与吞食行为,从而使鱼群更容易跳出局部最优得到性能优化。对振幅随炮检距变化(AVO)的实际数据参数反演的结果表明,与标准人工鱼群算法相比,改进的鱼群算法的反演精度与寻优时间都得到很大改进,表现出更强的寻优泛化能力。     

黄土边坡框架预应力锚杆支挡结构的优化设计

在以前开展的有关框架预应力锚杆支挡结构的设计计算基础上，采用整体优化和局部优化相结合的方法对其进行了优化分析设计。文中以单位宽度内主要材料工程总造价为目标函数，以锚杆布置位置、构件截面尺寸、纵向钢筋和箍筋为设计变量，以现行规程的设计计算规定和构造要求为约束条件，建立了框架预应力锚杆支挡结构的优化设计数学模型，并采用复形法求优。实例计算证明，优化设计可获得造价最为经济的最佳设计方案。文中所采用的优化设计思想可为类似支挡结构的优化设计参考。     

基于拟满应力遗传算法的桁架结构形状优化设计

以拟满应力法和改进遗传算法为基础,提出了一种可以解决具有连续变量和离散变量的桁架结构形状优化问题的拟满应力遗传算法。该算法既充分利用了遗传算法全局寻优能力强的特点,也发挥了力学准则法局部寻优长处,具有很高的搜索效率。用拟满应力遗传算法解决15杆桁架结构形状优化问题的结果表明,这是一种解决具有连续、离散混合变量的桁架结构优化设计问题的很有效方法。     

Finite element model updating of a large structure using multi-setup stochastic subspace identification method and bees optimization algorithm

In the present contribution, operational modal analysis in conjunction with bees optimization algorithm are utilized to update the finite element model of a solar power plant structure. The physical parameters which required to be updated are uncertain parameters including geometry, material properties and boundary conditions of the aforementioned structure. To determine these uncertain parameters, local and global sensitivity analyses are performed to increase the solution accuracy. An objective function is determined using the sum of the squared errors between the natural frequencies calculated by finite element method and operational modal analysis, which is optimized using bees optimization algorithm. The natural frequencies of the solar power plant structure are estimated by multi-setup stochastic subspace identification method which is considered as a strong and efficient method in operational modal analysis. The proposed algorithm is efficiently implemented on the solar power plant structure located in Shahid Chamran university of Ahvaz, Iran, to update parameters of its finite element model. Moreover, computed natural frequencies by numerical method are compared with those of the operational modal analysis. The results indicate that, bees optimization algorithm leads accurate results with fast convergence.     

基于BFGS的乘子优化法在位移反分析中的应用

基于BFGS(Broyden-Fletcher-Goldfarb-Shanno)的乘子优化法是指运用乘子来求解一般约束问题,但在优化过程中运用BFGS方法极小化。在位移反分析中引入该方法进行力学参数的反演,反演结果表明:该方法在位移反分析的优化过程中具有较高的计算精度和计算效率。     

Optimal drift design of tall reinforced concrete buildings with non‐linear cracking effects

This paper presents an efficient, computer‐based technique for the optimum drift design of tall reinforced concrete (RC) buildings including non‐linear cracking effects under service loads. The optimization process consists of two complementary parts: an iterative procedure for the non‐linear analysis of tall RC buildings and a numerical optimality criteria (OC) algorithm. The non‐linear response of tall RC buildings due to the effects of concrete cracking is obtained by a series of linear analyses, the so‐called direct effective stiffness method. In each linear analysis, cracked structural members are first identified and their stiffness modified based on a probability‐based effective stiffness relationship. Stiffness reduction coefficients are introduced as measures of the remaining stiffness for structural elements after cracking. A rigorously derived OC method is developed to solve for the minimum weight/cost design problem subject to multiple drift constraints and member sizing requirements. A shear wall‐frame example is presented to illustrate the application of this optimal design method. The design results of the optimized structure with cracking effects are compared to those of the linear‐elastic structure without concrete cracking. Copyright © 2005 John Wiley & Sons, Ltd.     

Improving repair sequence scheduling methods for postdisaster critical infrastructure systems

The recovery process of postdisaster critical infrastructure systems (CISs) includes multiple phases, among which the repair phase usually takes the longest time and contributes the largest to system recovery rapidity and system resilience. This article formulates the repair sequence scheduling problem for damaged CIS components under limited repair resources in a general form, and then proposes two novel heuristic methods to enhance its solution performance. To demonstrate the efficiencies of the two proposed methods, typical existing methods are also briefly introduced for comparison purposes, including three component importance‐based methods (CIBMs), a genetic algorithm‐based method (GABM), a time index‐based heuristic method (TIBHM), and a component index‐based exact solution method (CIBESM). Those methods are separately applied into thousands of damage scenarios for three systems, and are then compared in terms of the optimality gap and the computational cost. Results show that the two proposed methods have significantly better performance than existing methods, whereas the first proposed method can be adapted for critical time points‐based resilience assessment and optimization, and the second proposed method can be applied to the recovery of large‐scale CISs with extensive disruptions.     

QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency

In recent years, tunnel boring machines (TBMs) have been widely used in tunnel construction. However, the TBM control parameters set based on operator experience may not necessarily be suitable for certain geological conditions. Hence, a method to optimize TBM control parameters using an improved loss function-based artificial neural network (ILF-ANN) combined with quantum particle swarm optimization (QPSO) is proposed herein. The purpose of this method is to improve the TBM performance by optimizing the penetration and cutterhead rotation speeds. Inspired by the regularization technique, a custom artificial neural network (ANN) loss function based on the penetration rate and rock-breaking specific energy as TBM performance indicators is developed in the form of a penalty function to adjust the output of the network. In addition, to overcome the disadvantage of classical error backpropagation ANNs, i.e., the ease of falling into a local optimum, QPSO is adopted to train the ANN hyperparameters (weight and bias). Rock mass classes and tunneling parameters obtained in real time are used as the input of the QPSO-ILF-ANN, whereas the cutterhead rotation speed and penetration are specified as the output. The proposed method is validated using construction data from the Songhua River water conveyance tunnel project. Results show that, compared with the TBM operator and QPSO-ANN, the QPSO-ILF-ANN effectively increases the TBM penetration rate by 14.85% and 13.71%, respectively, and reduces the rock-breaking specific energy by 9.41% and 9.18%, respectively.     

基于改进的遗传模拟退火算法的钢框架优化设计

将遗传算法的全局寻优性能好和模拟退火的局部搜索能力强的优点相结合,提出了用于钢框架优化设计的遗传模拟退火算法,并对其进行了改进。在遗传算法部分提出了基于阈值的动态交叉、变异概率,并且采用联赛精英选择策略和最优保存策略,在种群的整体适应度提高的同时,增强了进化后期的种群多样性,提高了算法的收敛速度。在退火部分,针对钢框架优化的具体问题提出了一种更加紧凑灵活的邻域结构,提高了算法寻优性能。最后,将改进的算法用于工程实例,并与其他优化方法相比较,结果表明,该改进算法是一种用于钢框架结构优化设计的更加行之有效的方法。     

基于响应面的桥梁有限元模型修正

采用试验设计和回归分析方法,以显式的响应面模型逼近特征量与设计参数间复杂的隐式函数关系,得到简化的结构模型(Meta-model),给出有限元模型修正过程。针对复杂的土木工程结构,讨论样本选择、修正参数选取以及如何从众多因素中较合理地建立结构的响应面模型。用数值模拟算例和六跨连续梁桥环境振动试验结果,实现基于响应面模型的土木工程结构有限元模型修正,并与传统的基于灵敏度方法直接对结构有限元模型修正结果进行比较。结果表明,基于响应面方法的有限元模型修正和验证,能显著提高修正的效率,修正过程计算简洁、迭代收敛快,避开每次迭代都需要进行有限元计算,易于工程实际应用。