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It is well known that damaged structural members may alter the behavior of the structures considerably. Careful observation of these changes has often been viewed as a means to identify and assess the location and severity of damages in structures. Among the responses of a structure, natural frequencies are both relatively easy to obtain and independent from external excitation, and therefore, could be used as a measure of the structure's behavior before and after an extreme event which might have lead to damage in the structure. Inverse problem of detection and assessment of structural damage using the changes in natural frequencies is addressed in this paper. This can be considered as an optimization problem with the location and severity of the damages being its variables. The objective is to set these variables such that the natural frequencies of the finite element model correspond to the experimentally measured frequencies of the actual damaged structure. In practice, although the exact number of damaged elements is unknown, it is usually believed to be small compared to the total number of elements of the structure. In beams and frames particularly, the necessity to divide the structural members into smaller ones in order to detect the location of the cracks more accurately, deepens this difference. This can significantly improve the performance of the optimization algorithms in solving the inverse problem of damage detection. In this paper, the Charged System Search algorithm developed by Kaveh and Talatahari [1] is improved to comprise the above mentioned point. The performance of the improved algorithm is then compared to the standard one in order to emphasize the efficiency of the proposed algorithm in damage detection inverse problems.

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In the precast structures, optimization of structural elements is of great interest mainly due to a more rationalized way that elements are produced. There are several elements of precast prestressed concrete that are objects of study in optimization processes, as the prestressed joist applied in buildings slabs. This article inquires into cost minimization of continuous and simply supported slabs, formed by unialveolar beams and prestressed joist, using Genetic Algorithms (GAs). Comparative analyses of the final costs were made for these two precast elements, previously investigated in Castilho [1] and Castilho [2]. Furthermore, parcels of cost function were analyzed for the cases of prestressed joist and unialveolar beam, and the results show that the production stage of the element matches the largest part of the cost function. Also, although the prestressed joist is more economical, unialveolar beam reaches the market to compete with the other precast elements for slabs.

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The use of fiber reinforced polymer (FRP) U-wrap to rehabilitate concrete beams has increased in popularity over the past few years. As such, many design codes and guidelines have been developed to enable designers to use of FRP for retrofitting reinforced concrete beams. FIB is the only guideline for design which presents a formula for torsional capacity of concrete beams strengthened with FRP. The Rackwitz-Fiessler method was applied to make a reliability assessment on the torsional capacity design of concrete beams retrofitted with U-wrap FRP laminate by this guideline. In this paper, the average of reliability index obtained is 2.92, reflecting reliability of the design procedures. This value is somehow low in comparison to target reliability level of 3.5 used in the guideline calibration and so, optimum resistance factor may be needed in future guideline revisions. From the study on the relation between average reliability index and optimum resistance factor, a value of 0.723 for the optimum resistance factor is suggested.

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The main object of this research is to optimize an end-filled castellated beam. In order to support high shear forces close to the connections, sometimes it becomes necessary to fill certain holes in web opening beam. This is done by inserting steel plates and welding from both sides. Optimization of these beams is carried out using three meta-heuristic methods involves CSS, CBO, and CBO-PSO algorithms. To compare the performance of these algorithms, the minimum cost of the beam is taken as the design objective function. Also, in this study, two common types of laterally supported castellated beams are considered as design problems: beams with hexagonal openings and beams with circular openings. A number of design examples are considered to solve in this case. Comparison of the optimal solution of these methods demonstrates that the hexagonal beams have less cost than cellular beams. It is observed that optimization results obtained by the CBO-PSO for more design examples have less cost in comparison to the results of the other methods.

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This study focuses on using an artificial neural network (ANN) based model for predicting the performance of high strength concrete (HSC) beams strengthened with surface mounted FRP laminates. Eight input parameters such as geometrical properties of the beam and mechanical properties of FRP laminates were considered for this study. Back propagation network with Lavenberg-Marquardt algorithm has been chosen for the proposed network, which has been implemented using the programming package MATLAB. In the present study, comparison has been made between the experimental results and those predicted through neural network modeling. The amount of MAPE and RMSE were predicted and were found to be acceptable range. The statistical indicators such as correlation co-efficient (r) and co-efficient of determination (R²) were also predicted to estimate the accuracy of results obtained through ANN modeling. The results predicted through ANN modeling exhibit good correlation with the experimental results.

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Reducing waste material plays an essential role for engineers in the current world. Nowadays, recycled materials are going to be used in order to manufacture concrete beams. Previous studies concluded that the currently proposed formulas to predict the flexural and shear behavior of the reinforced concrete beams were not appropriate for those manufactured by recycled materials. This study aims to employ the Particle Swarm Optimization Algorithm to suggest the flexural and shear performance of recycled material reinforced concrete beams. For this purpose, the previous experimental outcomes are utilized, and new equations are established to anticipate both flexural and shear behavior of the recycled material concrete beams. Consequently, all findings are compared with those achieved experimentally. The attained significances of this study show that the proposed formulas have high accuracy for the experimental data.

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The lateral-torsional buckling (LTB) strength of cellular steel girders that were subjected to web distortion was rarely examined. Since no formulation has been presented for predicting the capacity of such beams, in the current paper an extensive numerical investigation containing 660 specimens was modeled using finite element analysis (FEA) to consider the ultimate lateral-distortional buckling (LDB) strength of such members. Then, a reliable algorithm based on the artificial neural networks (ANNs) was developed and the most accurate model was chosen to derive an efficient formula to evaluate the LDB capacity of steel cellular beams. The input and target data required in the ANN models were provided using the ANN analyzes. An attempt was made to include the proposed formula in all the variables affecting the LDB of cellular steel beams. In the next step, the validity of the proposed formula was proved by several statistical criteria, and also the most influential input variable was discussed. eventually, a comparison study was executed between the results provided by the ANN-based equation and the AS4100, EC3, and AISC codes. It was revealed that the presented equation is accurate enough and can be used by practical engineers.