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This paper is concerned with the economical comparison between two commonly used configurations for double layer grids and determining their optimum span-depth ratio. Two ranges of spans as small and big sizes with certain bays of equal length in two directions and various types of element grouping are considered for each type of square grids. In order to carry out a precise comparison between different systems, optimum design procedure based on the Cuckoo Search (CS) algorithm is developed. The CS is a meta-heuristic algorithm recently developed that is inspired by the behavior of some Cuckoo species in combination with the Lévy flight behavior of some birds and insects. The design algorithm obtains minimum weight grid through appropriate selection of tube sections available in AISC Load and Resistance Factor Design (LRFD). Strength constraints of AISC-LRFD specification and displacement constraints are imposed on grids. The comparison is aimed at finding the depth at which each of the different configurations shows its advantages. The results are graphically presented from which the optimum depth can easily be estimated for each type, while the influence of element grouping can also be realized at the same time.

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Different kinds of meta-heuristic algorithms have been recently utilized to overcome the complex nature of optimum design of structures. In this paper, an integrated optimization procedure with the objective of minimizing the self-weight of real size structures is simply performed interfacing SAP2000 and MATLAB® softwares in the form of parallel computing. The meta-heuristic algorithm chosen here is Cuckoo Search (CS) recently developed as a type of population based algorithm inspired by the behavior of some Cuckoo species in combination with the Lévy flight behavior. The CS algorithm performs suitable selection of sections from the American Institute of Steel Construction (AISC) wide-flange (W) shapes list. Strength constraints of the AISC load and resistance factor design specification, geometric limitations and displacement constraints are imposed on frames. Effective time-saving procedure using simple parallel computing, as well as utilizing reliable analysis and design tool are also some new features of the present study. The results show that the proposed method is effective in optimizing practical structures.

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In the present study, two new hybrid approaches are proposed for predicting peak ground acceleration (PGA) parameter. The proposed approaches are based on the combinations of Adaptive Neuro-Fuzzy System (ANFIS) with Genetic Algorithm (GA), and with Particle Swarm Optimization (PSO). In these approaches, the PSO and GA algorithms are employed to enhance the accuracy of ANFIS model. To develop hybrid models, a comprehensive database from Pacific Earthquake Engineering Research Center (PEER) are used to train and test the proposed models. Earthquake magnitude, earthquake source to site distance, average shear-wave velocity, and faulting mechanisms are used as predictive parameters. The performances of developed hybrid models (PSO-ANFIS-PSO and GA-ANFIS-GA) are compared with the ANFIS model and also the most common soft computing approaches available in the literature. According to the obtained results, three developed models can be effectively used to predict the PGA parameter, but the comparison of models shows that the PSO-ANFIS–PSO model provides better results.

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In the present study, the multivariate adaptive regression splines (MARS) technique is employed to estimate the drying shrinkage of concrete. To this purpose, a very big database (RILEM Data Bank) from different experimental studies is used. Several effective parameters such as the age of onset of shrinkage measurement, age at start of drying, the ratio of the volume of the sample on its drying surface, relative humidity, cement content, the ratio between water and cement contents, the ratio of sand on total aggregate, average compressive strength at 28 days, and modulus of elasticity at 28 days are included in the developing process of MARS model. The performance of MARS model is compared with several codes of practice including ACI, B3, CEB MC90-99, and GL2000. The results confirmed the superior capability of developed MARS model over existing design codes. Furthermore, the robustness of the developed model is also verified through sensitivity and parametric analyses.

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Bridges constitute an expensive segment of construction projects; the optimization of their designs will affect their high cost. Segmental precast concrete bridges are one of the most commonly serviced bridges built for mid and long spans. Genetic algorithm is one of the most widely applied meta-heuristic algorithms due to its ability in optimizing cost. Next to providing cost optimization of these bridge types, the effects of each one of the main three selections, crossover and mutation operators are assessed, and the best operator is determined through the Taguchi experimental design. To validate the functionality of this algorithm, a bridge constructed in the city of Isfahan, Iran (completed in 2017) is optimized, a total of 13% reduction in cost and weight of its superstructure is evident. The efficiency of applying the Taguchi method in determining the type of operators of the genetic algorithm is proved.

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In recent years, soft computing and artificial intelligence techniques such as artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) have been effectively used in various civil engineering applications. This study aims to examine the potential of ANN and ANFIS for modeling the compressive strength of concrete containing expanded perlite powder (EPP). For doing this, a total of forty-five EPP incorporated concrete mixtures were produced and tested for compressive strength at different curing ages of 3, 7, 28, 42 and 90 days. Two different ANN models were developed and the suitable and stable ANN architecture for each model was considered by calculating various statistical parameters. For comparative purposes, two ANFIS models with different membership functions were also trained. According to the results, it can be concluded that the proposed ANN models relatively give a good degree of accuracy in predicting the compressive strength of concrete made with EPP, higher than that of observed from ANFIS models.

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Metaheuristics are considered the first choice in addressing structural optimization problems. One of the complicated structural optimization problems is the highly nonlinear dynamic truss shape and size optimization with multiple natural frequency constraints. On the other hand, natural frequency constraints are useful to control the responses of a dynamically exciting structure. In this regard, this study uses for the first time the water evaporation optimization (WEO) algorithm to address this problem. Four benchmark trusses are considered for experimental investigation of the WEO. Obtained results indicate the comparative performance of WEO to the best-known algorithms in this problem, high performance in comparison to those of different optimization techniques, and high performance in comparison to all algorithms in terms of robustness. The simulation results clearly show a good balance between the global and local exploration abilities of WEO and its potential robust efficiency for other complicated constrained engineering optimization problems.