Design optimization of tall steel buildings by a modified particle swarm algorithm

Optimal design of tall buildings, as large‐scale structures, is a rather difficult task. To efficiently achieve this task, the computational performance of the employed standard meta‐heuristic algorithms needs to be improved. One of the most popular meta‐heuristics is particle swarm optimization (PSO) algorithm. The main aim of the present study is to propose a modified PSO (MPSO) algorithm for optimization of tall steel buildings. In order to achieve this purpose, PSO is sequentially utilized in a multi‐stage scheme where in each stage an initial swarm is generated on the basis of the information derived from the results of previous stages. Two large‐scale examples are presented to investigate the efficiency of the proposed MPSO. The numerical results demonstrate the computational advantages of the MPSO algorithm. Copyright © 2012 John Wiley & Sons, Ltd.     

Assessment of load carrying capacity of castellated steel beams by neural networks

In this paper, load carrying capacity of simply supported castellated steel beams, susceptible to web-post buckling, is studied. The accuracy of the nonlinear finite element (FE) method to evaluate the load carrying capacity and failure mode of the beams is discussed. In view of the high computational burden of the nonlinear finite element analysis, a parametric study is achieved based on FE and an empirical equation is proposed to estimate the web-posts’ buckling critical load of the castellated steel beams. Also as other alternatives to achieve this task, the traditional back-propagation (BP) neural network and adaptive neuro-fuzzy inference system (ANFIS) are employed. In this case, the accuracy of the proposed empirical equation, BP network and ANFIS are examined by comparing their provided results with those of conventional FE analysis. The numerical results indicate that the best accuracy associates with the ANFIS and the neural network models provide better accuracy than the proposed equations.     

Reflectance-yarn geometry relation of false-twist textured olive hue poly(ethylene terephthalate) mass dyed yarn in visible–near-infrared region

This study was conducted on the visible–near-infrared camouflage properties of olive hue poly(ethylene terephthalate) multifilament mass dyed yarn textured by two texturizing method (simultaneous and conventional). The effect of thermal process and structural changes on the reflectance, mechanical, and crimp properties of these yarns was studied using the DSC, XRD, and birefringence analyses. It was observed that simultaneously textured mass dyed multifilament yarns have higher crimp properties and lower mechanical properties. Furthermore, the effect of yarns’ geometry in the fabric structure on the reflectance properties was investigated using images of fabric structure. The results showed that the reflectance behavior of fabric sample is affected by two major factors, namely structural and geometrical factors; it is clear that the geometrical factor has a significant effect on the reflectance properties.     

Optimal design of arch dams subjected to earthquake loading by a combination of simultaneous perturbation stochastic approximation and particle swarm algorithms

An efficient optimization procedure is introduced to find the optimal shapes of arch dams considering fluid–structure interaction subject to earthquake loading. The optimization is performed by a combination of simultaneous perturbation stochastic approximation (SPSA) and particle swarm optimization (PSO) algorithms. This serial integration of the two single methods is termed as SPSA–PSO. The operation of SPSA–PSO includes three phases. In the first phase, a preliminary optimization is accomplished using the SPSA. In the second phase, an optimal initial swarm is produced using the first phase results. In the last phase, the PSO is employed to find the optimum design using the optimal initial swarm. The numerical results demonstrate the high performance of the proposed strategy for optimal design of arch dams. The solutions obtained by the SPSA–PSO are compared with those of SPSA and PSO. It is revealed that the SPSA–PSO converges to a superior solution compared to the SPSA and PSO having a lower computation cost.     

Developing an expert system for predicting alluvial channel geometry using ANN

Cross section geometry of stable alluvial channels usually is estimated by simple inaccurate empirical equations, because of the complexity of the phenomena and unknown physical processes of regime channels. So, the main purpose of this study is to evaluate the potential of simulating regime channel treatments using artificial neural networks (ANNs). The process of training and testing of this new model is done using a set of available published filed data (371 data numbers). Several statistical and graphical criterions are used to check the accuracy of the model in comparison with previous empirical equations. The multilayer perceptron (MLP) artificial neural network was used to construct the simulation model based on the training data using back-propagation algorithm. The results show a considerably better performance of the ANN model over the available empirical or rational equations. The constructed ANN models can almost perfectly simulate the width, depth and slope of alluvial regime channels, which clearly describes the dominant geometrical parameters of alluvial rivers. The results demonstrate that the ANN can precisely simulate the regime channel geometry, while the empirical, regression or rational equations can’t do this. The presented methodology in this paper is a new approach in establishing alluvial regime channel relations and predicting cross section geometry of alluvial rivers also it can be used to design stable irrigation and water conveyance channels.     

Structural and redox features of La0.7Bi0.3Mn1−xCoxO3 nanoperovskites for ethane combustion and CO oxidation

In this work, structural and catalytic properties of La_0.7Bi_0.3Mn_1?xCo_xO₃ nanocatalysts with x=0.00, 0.25, 0.50, 0.75, and 1.00 prepared by citrate method are investigated. The structural characterization using X'Pert package and Fullprof program is an evidence for structural phase transition. The values of refined unit cell volume obtained from the Rietveld analysis show decreasing and increasing tendencies for values of x≤0.5 and x>0.5, respectively. The catalytic performance tests of the catalysts show that the samples x=0.00 and 0.25 have lower temperature of CO oxidation and C₂H₆ combustion, respectively.     

An improved fireworks algorithm for discrete sizing optimization of steel skeletal structures

The fireworks algorithm (FWA), a newly introduced metaheuristic algorithm, has exhibited remarkable performance in a variety of optimization problems. However, FWA still has some shortcomings that need to be addressed. In the present study, an improved fireworks algorithm (IFWA) is proposed to mitigate the drawbacks of the original algorithm. In the IFWA, the possibility of interaction among different solutions during the optimization process is provided. Moreover, a strategy is considered to decrease the computational effort of the algorithm. The IFWA is used to deal with the discrete structural optimization problems of steel trusses and frames. The results demonstrate the efficiency of the proposed IFWA compared with other well-known metaheuristics in the literature, in terms of the optimum solutions and the convergence rate.     

Optimal design of structures subjected to time history loading by swarm intelligence and an advanced metamodel

This paper proposes a new metamodeling framework that reduces the computational burden of the structural optimization against the time history loading. In order to achieve this, two strategies are adopted. In the first strategy, a novel metamodel consisting of adaptive neuro-fuzzy inference system (ANFIS), subtractive algorithm (SA), self organizing map (SOM) and a set of radial basis function (RBF) networks is proposed to accurately predict the time history responses of structures. The metamodel proposed is called fuzzy self-organizing radial basis function (FSORBF) networks. In this study, the most influential natural periods on the dynamic behavior of structures are treated as the inputs of the neural networks. In order to find the most influential natural periods from all the involved ones, ANFIS is employed. To train the FSORBF, the input–output samples are classified by a hybrid algorithm consisting of SA and SOM clusterings, and then a RBF network is trained for each cluster by using the data located. In the second strategy, particle swarm optimization (PSO) is employed to find the optimum design. Two building frame examples are presented to illustrate the effectiveness and practicality of the proposed methodology. A plane steel shear frame and a realistic steel space frame are designed for optimal weight using exact and approximate time history analyses. The numerical results demonstrate the efficiency and computational advantages of the proposed methodology.     

An intelligent neural system for predicting structural response subject to earthquakes

An efficient method is introduced to predict the time history responses of structures subject to earthquakes employing neural network techniques. In order to achieve this purpose, a new intelligent neural system (INS) is designed by combining competitive and radial basis function (RBF) neural networks. In the INS the input space is classified by a competitive neural network (CNN) based on natural frequencies of the structures. Afterward an RBF network is assigned to each class and is trained by using the data located in the class. Results of illustrative examples demonstrate high performance and computational advantages of INS comparing with the single RBF network.     

Optimized performance data transmission in Mobile IP networks

In a Mobile IP network (MIPN), nodes move. When a node moves, it may go away from other nodes and this decreases available bandwidth and data rate and increases the propagation delay of links. Therefore, nodes’ movement can decrease data delivery and handoff latency; these will reduce network efficiency. Suppose that an MIPN uses an optimal routing algorithm and transmits data from a source node to a destination node optimally. Nodes’ movement can violate the optimality of the data transmission and this will waste bandwidth and network resources. In this paper we present a new parametric optimal unicast multichannel routing algorithm that computes a domain for a mobile node and this domain will hold the optimality of data transmission and prevent network efficiency failure. Our new method determines an optimal domain for each mobile node and does not allow nodes to exit from that optimal domain. Simulation results show that our new method increases data rate and network efficiency.