易损性分析考虑结构非线性的输入地震波选取方法研究

对于结构抗震设计和研究,地震易损性分析中输入地震波组的选择至关重要,然而现有地震波选取方法对于地震波组的多样性和离散性未作考虑或不够明确,从而影响易损性分析结果的可靠性和意义.针对这个问题,文章提出一个新的选波策略,基于目标结构的简化模型在地震波组激励下的响应,通过改进的地震动强度指标以及信息熵概念来判断输入地震波组的多样性.建立了一个3跨连续梁桥的有限元模型作为数值算例,通过执行新提出的方法验证了简化模型响应的信息熵与实际有限元模型响应的信息熵之间的关系,初步证明了此策略基于输入地震动多样性的筛选能力,可以作为地震易损性中分析输入地震动选取的简便和可靠的依据.     

Integrated design optimization of base-isolated concrete buildings under spectrum loading

Base isolation has become a practical control strategy for protecting structures against seismic hazards. Most previous studies on the optimum design of base-isolated structures have been focused on the design optimization of either the base isolation or the superstructure. It is necessary to simultaneously optimize both the base isolation and the superstructure as a whole to seek the most cost-efficient design for such structures. This paper presents an effective numerical optimization technique for the seismic design of base-isolated concrete building structures under spectrum loading. Attempts have been made to automate the integrated spectrum analysis and design optimization procedure and to minimize the total cost of the base-isolated building subject to design performance criteria in terms of the interstory drifts of the superstructure and the lateral displacement of the isolation system. In the optimal design problem formulation, the cost of the superstructure can be expressed in terms of concrete member sizes while assuming all these members to be linearly elastic under earthquake actions. However, the isolation system is assumed to behave nonlinearly, and its cost can be related to the effective horizontal stiffness of each isolator. Using the principle of virtual work, the lateral drift responses of concrete base-isolated buildings can be explicitly formulated and the integrated optimization problem can be solved by the optimality criteria method. The technique is capable of achieving the optimal balance between the costs of the superstructure and the isolation system while the design performance criteria can be simultaneously satisfied. One practical building example with and without base isolation is used to illustrate the effectiveness of the optimal design technique.     

A study of suspended roofs for near-source seismic motions

The non-linear behavior of multi-suspended roof systems for seismic loads is studied. The study is based on a formulation that can be easily employed for a preliminary design of multi-suspended roofs subjected to seismic loads. Specifically, applying Lagrange’s equations, the corresponding set of equations of motion for discrete models of multiple suspension roofs is obtained and numerical integration of the equations of motion is performed via the Runge–Kutta scheme. For representative realistic combinations of geometric, stiffness and damping parameters, a non-linear analysis is employed to study the behavior of suspended roofs for near-source and far-field seismic motions. The analysis demonstrates that: (i) code-specified design loads could dramatically underestimate the response of suspended roofs subjected to near-source ground motions and (ii) flexible roofing systems are greatly affected by near-source ground motions, a behavior that is not observed for stiff systems.     

Structural topology optimization using a genetic algorithm with a morphological geometric representation scheme

This paper describes a versatile methodology for solving topology design optimization problems using a genetic algorithm (GA). The key to its effectiveness is a geometric representation scheme that works by specifying a skeleton which defines the underlying topology/connectivity of a structural continuum together with segments of material surrounding the skeleton. The required design variables are encoded in a chromosome which is in the form of a directed graph that embodies this underlying topology so that appropriate crossover and mutation operators can be devised to recombine and help preserve any desirable geometry characteristics of the design through succeeding generations in the evolutionary process. The overall methodology is first tested by solving ‘target matching’ problems—simulated topology optimization problems in each of which a ‘target’ geometry is first created and predefined as the optimum solution, and the objective of the optimization problem is to evolve design solutions to converge towards this target shape. The methodology is then applied to design two path-generating compliant mechanisms—large-displacement flexural structures that undergo some desired displacement paths at some point when given a straight line input displacement at some other point—by an actual process of topology/shape optimization.     

Performance-based optimum seismic design of steel structures by a modified firefly algorithm and a new neural network

Structural optimization for performance-based seismic design (PBSD) in earthquake engineering aims at finding optimum design variables corresponding to a minimum objective function with constraints on performance requirements. In this study, an efficient methodology, consisting of two computational strategies, is presented for performance-based optimum seismic design (PBOSD) of steel moment frames. In the first strategy, a modified firefly algorithm (MFA) is proposed to efficiently find PBOSD at the performance levels. Because that for computing the structural responses at the performance levels a nonlinear static pushover analysis must be conducted, the overall computational time of optimization process is extremely large. In the second strategy, to reduce the computational burden, a new neural network model termed as wavelet cascade-forward back-propagation (WCFBP) is proposed to effectively predict the results of nonlinear pushover analysis during the optimization process. To illustrate the effectiveness of the proposed methodology, 3, 6 and 12 storey planar steel moment resisting frames are optimized for various performance levels. The results demonstrate the effectiveness of the proposed soft computing-based methodology for PBOSD of steel structures spending low computational cost.     

An intelligent adaptive system for the optimal variable selections of R&D and quality supply chains

The cost of research & development (R&D) and quality management are always regarded as two major parts of total cost. The variable performance of R&D and quality design is an important index that will reflect the effectiveness of the cost reduction. This research has attempted to simultaneously vary all of the variables to achieve the global optimum for the optimal variable selections of R&D and quality design. Genetic algorithm (GA) can treat all of the variables for the global search. In this study, fuzzy refinement with orthogonal arrays was effective in improving the performance of the GA, and also showed the benefits of a good chromosome structure on the behavior of GA. It is also proposed the postponement design with temporal concept, to select the effective variables for the cost reduction of R&D and quality management design. The experimental results showed that tempo-postponement design will increase the flexibility and quick response for supply chain management. Hence, this approach can act as a useful guideline for researchers working on the optimization of the key variable selections for R&D and quality model design.     

An intelligent adaptive system for the optimal variable selections of R&D and quality supply chains

The cost of research & development (R&D) and quality management are always regarded as two major parts of total cost. The variable performance of R&D and quality design is an important index that will reflect the effectiveness of the cost reduction. This research has attempted to simultaneously vary all of the variables to achieve the global optimum for the optimal variable selections of R&D and quality design. Genetic algorithm (GA) can treat all of the variables for the global search. In this study, fuzzy refinement with orthogonal arrays was effective in improving the performance of the GA, and also showed the benefits of a good chromosome structure on the behavior of GA. It is also proposed the postponement design with temporal concept, to select the effective variables for the cost reduction of R&D and quality management design. The experimental results showed that tempo-postponement design will increase the flexibility and quick response for supply chain management. Hence, this approach can act as a useful guideline for researchers working on the optimization of the key variable selections for R&D and quality model design.     

Efficient optimum seismic design of reinforced concrete frames with nonlinear structural analysis procedures

Performance-based seismic design offers enhanced control of structural damage for different levels of earthquake hazard. Nevertheless, the number of studies dealing with the optimum performance-based seismic design of reinforced concrete frames is rather limited. This observation can be attributed to the need for nonlinear structural analysis procedures to calculate seismic demands. Nonlinear analysis of reinforced concrete frames is accompanied by high computational costs and requires a priori knowledge of steel reinforcement. To address this issue, previous studies on optimum performance-based seismic design of reinforced concrete frames use independent design variables to represent steel reinforcement in the optimization problem. This approach drives to a great number of design variables, which magnifies exponentially the search space undermining the ability of the optimization algorithms to reach the optimum solutions. This study presents a computationally efficient procedure tailored to the optimum performance-based seismic design of reinforced concrete frames. The novel feature of the proposed approach is that it employs a deformation-based, iterative procedure for the design of steel reinforcement of reinforced concrete frames to meet their performance objectives given the cross-sectional dimensions of the structural members. In this manner, only the cross-sectional dimensions of structural members need to be addressed by the optimization algorithms as independent design variables. The developed solution strategy is applied to the optimum seismic design of reinforced concrete frames using pushover and nonlinear response-history analysis and it is found that it outperforms previous solution approaches.     

Seismic energy dissipation-based optimum design of tuned mass dampers

Tuned mass dampers (TMDs) are a subclass of dynamic vibration absorbers that consist of a mass-spring-damper unit that is attached to a structure to adjust its response to seismic and wind loads. The efficacy, performance and optimum design of a TMD strongly depend not only on its mass, stiffness and damping as well as the input energy and the structure characteristics, but also on the structural response parameter(s) that the TMD is intended to mitigate. In that respect, this study evaluates the suitability of four objective functions for the optimum design of the TMD of an inelastic, steel moment-resisting frame (SMRF) under an artificial, white-noise excitation. The objective functions include 1) the maximum roof lateral displacement, 2) the maximum drift, 3) the root mean square of drifts and 4) the cumulative hysteretic energy of the SMRF. The results indicate that the SMRF equipped with a TMD optimized using the cumulative hysteretic energy of the SMRF as the objective function exhibits the best seismic response under the artificial earthquake. Further examining the response of the TMD-equipped SMRF under four historic earthquake records shows that equipping a structure with a TMD optimized using an artificial earthquake will not warrant that the structure will exhibit a better seismic performance in all measures compared with when no TMD is used. Put other way, while the minimization of cumulative hysteretic energy could be the best objective function for a case subjected to an artificial earthquake, under real earthquakes, none of the objective functions consistently results in a better seismic performance. This behavior is attributed to detuning effects arising from major structural damages and significant period shifts that occur during strong earthquakes.     

Optimum topology and shape design of prestressed concrete bridge girders using a genetic algorithm

The purpose of this study is to optimize the topology and shape of prestressed concrete bridge girders. An optimum design approach that uses a genetic algorithm (GA) for this purpose is presented. The cost of girders is the optimum design criterion. The design variables are the cross-sectional dimensions of the prefabricated prestressed beams, the cross-sectional area of the prestressing steel and the number of beams in the bridge cross-section. Stress, displacement and geometrical constraints are considered in the optimum design. AASHTO Standard Specifications for Highway Bridges are taken into account when calculating the loads and designing the prestressed beams. A computer program is coded in Visual Basic for this optimization. Many design examples from various applications have been optimized using this program. Several of these examples are presented to demonstrate the efficiency of the algorithm coded in the study.     

Variable control scheme in the cascade processes

The article considers the variables process control scheme for cascade processes. We construct variable sample sizes and sampling intervals (VSSI) control charts to effectively monitor the input variable and the output variable produced by a cascade process. The performance of the proposed VSSI control charts is measured by the adjusted average time to signal derived by a Markov chain approach. An example of the metallic film thickness of the computer connectors system shows the application and the performance of the proposed VSSI control charts in detecting shifts in means of the cascade process. Furthermore, the performance of the proposed VSSI control charts and the fixed sample sizes and sampling intervals control charts are compared by numerical analysis results. These demonstrate that the former is much faster in detecting small and medium shifts. The optimum VSSI control charts are also proposed using optimization technique when quality engineers cannot specify the values of the variable sample sizes and sampling intervals. It has been found that the optimum VSSI control charts work and are thus suggested whenever quality engineers cannot specify the values of variable sample sizes and sampling intervals. Furthermore, the impacts of misusing Shewhart charts to monitoring the process means on the cascade process are also investigated.     

Truss topology optimization considering local buckling constraints and restrictions on intersection and overlap of bar members

This paper illustrates the application of a two-level approximation method for truss topology optimization with local member buckling constraints and restrictions on member intersections and overlaps. Previously developed for truss topology optimization with stress and displacement constraints, that method is achieved by starting from an initial ground structure, and, combined with genetic algorithm (GA), it can handle both discrete and continuous variables, which denote the existence and cross-sectional areas of bar members respectively in the ground structure. In this work, this method is improved and extended to consider member buckling constraints and restrict intersection and overlap of members for truss topology optimization. The temporary deletion technique is adopted to temporarily remove buckling constraints when related bar members are deleted, and in order to avoid unstable designs, the validity check for truss topology configuration is conducted. By using GA to search in each possible design subset, the singularity encountered in buckling-constrained problems is remedied, and meanwhile, as the required structural analysis is replaced with explicit approximation functions in the process of executing GA, the computational cost is significantly saved. Moreover, for the consideration of restrictions on member intersecting and overlapping, the definition of such phenomena and mathematical expressions to recognize them are presented, and a new fitness function is developed to include such considerations. Numerical examples are presented to show the efficacy of the proposed techniques.     

Interval analysis based robust truss optimization with continuous and discrete variables using mix-coded genetic algorithm

The problem of optimizing truss structures in the presence of uncertain parameters considering both continuous and discrete design variables is studied. An interval analysis based robust optimization method combined with the improved genetic algorithm is proposed for solving the problem. Uncertain parameters are assumed to be bounded in specified intervals. The natural interval extensions are employed to obtain explicitly a conservative approximation of the upper and lower bounds of the structural response, and hereby the bounds of the objective function and the constraint function. This way the uncertainty design may be performed in a very efficient manner in comparison with the probabilistic analysis based method. A mix-coded genetic algorithm (GA), where the discrete variables are coded with binary numbers while the continuous variables are coded with real numbers, is developed to deal with simultaneously the continuous and discrete design variables of the optimization model. An improved differences control strategy is proposed to avoid the GA getting stuck in local optima. Several numerical examples concerning the optimization of plane and space truss structures with continuous, discrete or mixed design variables are presented to validate the method developed in the present paper. Monte Carlo simulation shows that the interval analysis based optimization method gives much more robust designs in comparison with the deterministic optimization method.     

A new method for modification of ground motions using wavelet transform and enhanced colliding bodies optimization

In this paper a simple and robust approach is presented for spectral matching of ground motions utilizing the wavelet transform and an improved metaheuristic optimization technique. For this purpose, wavelet transform is used to decompose the original ground motions to several levels, where each level covers a special range of frequency, and then each level is multiplied by a variable. Subsequently, the enhanced colliding bodies optimization technique is employed to calculate the variables such that the error between the response and target spectra is minimized. The application of the proposed method is illustrated through modifying 12 sets of ground motions. The results achieved by this method demonstrate its capability in solving the problem. The outcomes of the enhanced colliding bodies optimization (ECBO) are compared to those of the standard colliding bodies optimization (CBO) to illustrate the importance of the enhancement of the algorithm.     

Stacking sequence optimization with genetic algorithm using a two-level approximation

We propose a new method for laminate stacking sequence optimization based on a two-level approximation and genetic algorithm (GA), and establish an optimization model including continuous size variables (thicknesses of plies) and discrete variables (0/1 variables that represent the existence of each ply). To solve this problem, a first-level approximate problem is constructed using the branched multipoint approximate (BMA) function. Since mixed-variables are involved in the first-level approximate problem, a new optimization strategy is introduced. The discrete variables are optimized through the GA. When calculating the fitness of each member in the population of GA, a second-level approximate problem that can be solved by the dual method is established to obtain the optimal thicknesses corresponding to the each given ply orientation sequence. The two-level approximation genetic algorithm optimization is performed starting from a ground laminate structure, which could include relatively arbitrarily discrete set of angles. The method is first applied to cylindrical laminate design examples to demonstrate its efficiency and accuracy compared with known methods. The capacity of the optimization strategy to solve more complex problems is then demonstrated using a design example. With the presented method, the stacking sequence in analytical tools can be directly taken as design variables and no intermediate variables need be adopted.     

Machine learning-aided PSDM for dams with stochastic ground motions

Probabilistic seismic demand models are widely used for structures to establish a relation between the engineering demand parameter (EDP) and ground motion intensity measures (IM). For the complex infrastructures such as dams two challenges in implementation of probabilistic seismic demand models are scarcity of the appropriate real ground motions, and computational limitation to perform hundreds of simulations. This paper addresses both concerns by using a series of stochastic ground motion records as an alternative for real ones, and also employing machine learning algorithms to predict the IM-EDP relation. A 3D concrete arch dam with foundation and reservoir interaction is used as a case study of a demanding system (as opposed to 2D framed building). A large group of about 600 real and stochastic ground motion records are used to analyze the coupled system. In addition, five machine learning algorithms were used to develop the predictive meta-models. This paper also highlights the feasibility of using stochastic ground motions to predict the probabilistic seismic demand meta-models and fragility curves from real records, and vice versa. While the outcomes illustrate promising results, they also show that the existing stochastic ground motion simulation models do not cover all the inherent characteristics of the real records.     

Dynamic motion planning of overarm throw for a biped human multibody system

We propose a motion planning formulation of overarm throw for a 55-degree-of-freedom biped human multibody system. The unique characteristics of the throwing task—highly redundant, highly nonlinear, and highly dynamic—make the throwing motion simulation challenging in the literature and are addressed within the framework of multibody dynamics and optimization. To generate physically feasible throwing motions in a fully predictive method without input reference from motion capture or animation, rigorous dynamics modeling, such as dynamic balance based on Zero-Moment Point (ZMP) and ground reaction loads, is associated with the constraints. Given the target location and the object mass, the algorithm outputs the motion, required actuator torques, release conditions, and projectile and flight time of the object. Realistic human-like motions of throwing are generated for different input parameters, which demonstrate valid cause–effect relations in terms of both kinematic and kinetic outputs.     

A mixed GA-PSO-based approach for performance-based design optimization of 2D reinforced concrete special moment-resisting frames

A mixed genetic algorithm and particle swarm optimization in conjunction with nonlinear static and dynamic analyses as a smart and simple approach is introduced for performance-based design optimization of two-dimensional (2D) reinforced concrete special moment-resisting frames. The objective function of the problem is considered to be total cost of required steel and concrete in design of the frame. Dimensions and longitudinal reinforcement of the structural elements are considered to be design variables and serviceability, special moment-resisting and performance conditions of the frame are constraints of the problem. First, lower feasible bond of the design variables are obtained via analyzing the frame under service gravity loads. Then, the joint shear constraint has been considered to modify the obtained minimum design variables from the previous step. Based on these constraints, the initial population of the genetic algorithm (GA) is generated and by using the nonlinear static analysis, values of each population are calculated. Then, the particle swarm optimization (PSO) technique is employed to improve keeping percent of the badly fitted populations. This procedure is repeated until the optimum result that satisfies all constraints is obtained. Then, the nonlinear static analysis is replaced with the nonlinear dynamic analysis and optimization problem is solved again between obtained lower and upper bounds, which is considered to be optimum result of optimization solution with nonlinear static analysis. It has been found that by mixing the analyses and considering the hybrid GA-PSO method, the optimum result can be achieved with less computational efforts and lower usage of materials.     

Process parameter optimization of lap joint fillet weld based on FEM–RSM–GA integration technique

This study introduces a welding process design tool to determine optimal arc welding process parameters based on Finite Element Method (FEM), Response Surface Method (RSM) and Genetic Algorithms (GA). Here, a sequentially integrated FEM–RSM–GA framework has been developed and implemented to reduce the weld induced distortion in the final welded structure. It efficiently incorporates finite element based numerical welding simulations to investigate the desired responses and the effect of design variables without expensive trial experiments. To demonstrate the effectiveness of the proposed methodology, a lap joint fillet weld specimen has been used in this paper. Four process parameters namely arc voltage, input current, welding speed and welding direction have been optimized to minimize the distortion of the structure. The optimization results revealed the effectiveness of the methodology for welding process design with reduced cost and time.     

Comparison study between probabilistic and possibilistic methods for problems under a lack of correlated input statistical information

In most industrial applications, only limited statistical information is available to describe the input uncertainty model due to expensive experimental testing costs. It would be unreliable to use the estimated input uncertainty model obtained from insufficient data for the design optimization. Furthermore, when input variables are correlated, we would obtain non-optimum design if we assume that they are independent. In this paper, two methods for problems with a lack of input statistical information—possibility-based design optimization (PBDO) and reliability-based design optimization (RBDO) with confidence level on the input model—are compared using mathematical examples and an Abrams M1A1 tank roadarm example. The comparison study shows that PBDO could provide an unreliable optimum design when the number of samples is very small. In addition, PBDO provides an optimum design that is too conservative when the number of samples is relatively large. Furthermore, the obtained PBDO designs do not converge to the optimum design obtained using the true input distribution as the number of samples increases. On the other hand, RBDO with confidence level on the input model provides a conservative and reliable optimum design in a stable manner. The obtained RBDO designs converge to the optimum design obtained using the true input distribution as the number of samples increases.