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In this paper the parametric study is carried out to investigate the effect of number of cells in optimal cost of the non-prismatic reinforced concrete (RC) box girder bridges. The variables are geometry of cross section, tapered length, concrete strength and reinforcement of the box girders and slabs that are obtained using ECBO metaheuristic algorithm. The design is based on AASHTO standard specification. The constraints are the bending and shear strength, geometric limitations and superstructure deflection. The link of CSiBridge and MATLAB software are used for the optimization process. The methodology carried out for two-cell, three-cell and four-cell box girder bridges. The results show that the total cost of the concrete, bars and formwork for two-cell box girder is less than those of the three- and four-cell box girder bridges.

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In recent years many researchers prefer to use metaheuristic algorithms to reach the optimum design of structures. In this study, an Enhanced Vibrating Particle System (EVPS) is applied to get the minimum weight of large-scale dome trusses under frequency constraints. Vibration frequencies are important parameters, which can be used to control the responses of a structure that is subjected to dynamic excitation. The truss structures were analyzed by finite element method and optimization processes were implemented by the computer program coded in MATLAB. The effectiveness and efficiency of the Enhanced Vibrating Particle System (EVPS) is investigated in three large-scale dome trusses 600-, 1180-, and 1410-bar to obtain the weight optimization with frequency constraints.

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The cycle basis of a graph arises in a wide range of engineering problems and has a variety of applications. Minimal and optimal cycle bases reduce the time and memory required for most of such applications. One of the important applications of cycle basis in civil engineering is its use in the force method to frame analysis to generate sparse flexibility matrices, which is needed for optimal analysis.
In this paper, the simulated annealing algorithm has been employed to form suboptimal cycle basis. The simulated annealing algorithm works by using local search generating neighbor solution, and also escapes local optima by accepting worse solutions. The results show that this algorithm can be used to generate suboptimal and subminimal cycle bases. Compared to the existing heuristic algorithms, it provides better results. One of the advantages of this algorithm is its simplicity and its ease for implementation.
 

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As a novel strategy, Quantum-behaved particles use uncertainty law and a distinct formulation obtained from solving the time-independent Schrodinger differential equation in the delta-potential-well function to update the solution candidates’ positions. In this case, the local attractors as potential solutions between the best solution and the others are introduced to explore the solution space. Also,  the difference between the average and another solution is established as a new step size. In the present paper, the quantum teacher phase is introduced to improve the performance of the current version of the teacher phase of the Teaching-Learning-Based Optimization algorithm (TLBO) by using the formulation obtained from solving the time-independent Schrodinger equation predicting the probable positions of optimal solutions. The results show that QTLBO, an acronym for the Quantum Teaching- Learning- Based Optimization, improves the stability and robustness of the TLBO by defining the quantum teacher phase. The two circulant space trusses with multiple frequency constraints are chosen to verify the quality and performance of QTLBO. Comparing the results obtained from the proposed algorithm with those of the standard version of the TLBO algorithm and other literature methods shows that QTLBO increases the chance of finding a better solution besides improving the statistical criteria compared to the current TLBO.
 

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Design optimization of structures with discrete and continuous search spaces is a complex optimization problem with lots of local optima. Metaheuristic optimization algorithms, due to not requiring gradient information of the objective function, are efficient tools for solving these problems at a reasonable computational time. In this paper, the Doppler Effect-Mean Euclidian Distance Threshold (DE-MEDT) metaheuristic algorithm is applied to solve the discrete and continuous optimization problems of the truss structures subject to multiple loading conditions and design constraints. DE-MEDT algorithm is a recently proposed metaheuristic developed based on a physical phenomenon called Doppler Effect (DE) with some idealized rules and a mechanism called Mean Euclidian Distance Threshold (MEDT). The efficiency of the DE-MEDT algorithm is evaluated by optimizing five large-scale truss structures with continuous and discrete variables. Comparing the results found by the DE-MEDT algorithm with those of other existing metaheuristics reveals that the DE-MEDT optimizer is a suitable optimization technique for discrete and continuous design optimization of large-scale truss structures.
 

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Metaheuristic algorithms are preferred by the many researchers to reach the reliability based design optimization (RBDO) of truss structures. The cross-sectional area of the elements of a truss is considered as design variables for the size optimization under frequency constraints. The design of dome truss structures are optimized based on reliability by a popular metaheuristic optimization technique named Enhanced Vibrating Particle System (EVPS). Finite element analyses of structures and optimization process are coded in MATLAB. Large-scale dome truss of 600-bar, 1180-bar and 1410-bar are investigated in this paper and are compared with the previous studies. Also, a comparison is made between the reliability indexes of Deterministic Design Optimization (DDO) for large dome trusses and Reliability-Based Design Optimization (RBDO).
 

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The existence of uncertainties in engineering problems makes it essential to consider these effects at all times. Robust design optimization allows a design to be made less sensitive to uncertain input parameters. Actually, robust design optimization reduces the sensitivity of the objective function and the variations in design performance when uncertainty exists. In this study, two space trusses were optimized based on the modulus of elasticity, yield stress, and cross-sectional uncertainties in order to increase the response robustness and decrease the weight. The displacement of one node has been used as the criterion for Robust Design Optimization (RDO) of these two structures. Two trusses with 72 members and 582 members are considered, which are famous trusses in the field of structural optimization. Also, the EVPS meta-heuristic algorithm was employed which is an enhanced version of the VPS algorithm based on the single degrees of freedom of a system with viscous damping.
 

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In this paper, the improved shuffled-based Jaya algorithm (IS-Jaya) is applied to the size optimization of the braced dome with the frequency constraints. IS-Jaya is the enhanced version of the Jaya algorithm that the shuffling process and escaping from local optima are added for it. These two modifications increase the population diversity and ability the escape from the local optima of the Jaya. The robustness and performance of the IS-Jaya are evaluated by the three design examples. The results show that the IS-Jaya algorithm outperforms other state-of-the-art optimization techniques considered in the literature.
 

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This paper presents a computational framework for optimal design of non-prismatic reinforced concrete box girder bridges. The variables include the geometry of the cross section, tapered length, concrete strength and reinforcement of box girders and slabs. These are obtained by the enhanced colliding bodies optimization algorithm to optimizing the cost and again CO₂ emission. Loading and design is based on the AASHTO standard specification. The methodology is illustrated by a three-span continuous bridge. The trade-off between optimal cost and CO₂ emission in this type of bridge indicates that the difference of costs, as well as CO₂ emissions in the solution with both objectives is less than 1%. However, the optimal variables in the cost objective are different from the variables of CO₂ emission objective.
 

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Optimization methods are essential in today's world. Several types of optimization methods exist, and deterministic methods cannot solve some problems, so approximate optimization methods are used. The use of approximate optimization methods is therefore widespread. One of the metaheuristic algorithms for optimization, the EVPS algorithm has been successfully applied to engineering problems, particularly structural engineering problems. As this algorithm requires experimental parameters, this research presents a method for determining these parameters for each problem and a self-adaptive algorithm called the SA-EVPS algorithm. In this study, the SA-EVPS algorithm is compared with the EVPS algorithm using the 72-bar spatial truss structure and three classical benchmarked functions
 

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Despite the advantages of the plastic limit analysis of structures, this robust method suffers from some drawbacks such as intense computational cost. Through two recent decades, metaheuristic algorithms have improved the performance of plastic limit analysis, especially in structural problems. Additionally, graph theoretical algorithms have decreased the computational time of the process impressively. However, the iterative procedure and its relative computational memory and time have remained a challenge, up to now. In this paper, a metaheuristic-based artificial neural network (ANN), which is categorized as a supervised machine learning technique, has been employed to determine the collapse load factors of two-dimensional frames in an absolutely fast manner. The numerical examples indicate that the proposed method's performance and accuracy are satisfactory.
 

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Vibrating particles system (VPS) is a swarm intelligence-based optimizer inspired by free vibration with a single degree of freedom systems. VPS is one of the well-known algorithms in structural optimization problems. However, its performance can be improved to find a better solution. This study introduces an improved version of the VPS using the statistical regeneration mechanism for the optimal design of the structures with discrete variables. The improved version is named VPS-SRM, and its efficiency is tested in the three real-size optimization problems. The optimization results reveal the capability and robustness of the VPS-SRM for the optimal design of the structures with discrete sizing variables.
 

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In this paper, three recently improved metaheuristic algorithms are utilized for the optimum design of the frame structures using the force method. These algorithms include enhanced colliding bodies optimization (ECBO), improved shuffled Jaya algorithm (IS-Jaya), and Vibrating particles system - statistical regeneration mechanism algorithm (VPS-SRM). The structures considered in this study have a lower degree of statical indeterminacy (DSI) than their degree of kinematical indeterminacy (DKI). Therefore, the force method is the most suitable analysis method for these structures. The robustness and performance of these methods are evaluated by the three design examples named 1-bay 10-story steel frame, 3-bay 15-story steel frame, and 3-bay 24-story steel frame.
 

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Cement, water, fine aggregates, and coarse aggregates are combined to produce concrete, which is the most common substance after water and has a distinctly compressive strength, the most important quality indicator. Hardened concrete's compressive strength is one of its most important properties. The compressive strength of concrete allows us to determine a wide range of concrete properties based on this characteristic, including tensile strength, shear strength, specific weight, durability, erosion resistance, sulfate resistance, and others. Increasing concrete's compressive strength solely by modifying aggregate characteristics and without affecting water and cement content is a challenge in the direction of concrete production. Artificial neural networks (ANNs) can be used to reduce laboratory work and predict concrete's compressive strength. Metaheuristic algorithms can be applied to ANN in an efficient and targeted manner, since they are intelligent systems capable of solving a wide range of problems. This study proposes new samples using the Taguchi method and tests them in the laboratory. Following the training of an ANN with the obtained results, the highest compressive strength is calculated using the EVPS and SA-EVPS algorithms.
 

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This paper deals with the optimum design of the mixed structures that consists of two parts, a lower part made of concrete and an upper part made of steel. Current codes and available commercial software packages do not provide analytical solutions for such structural systems, especially if a decoupled analysis is performed where the lower part is excited by ground motion and its response of total accelerations is used for the upper part. Due to irregular damping ratios, mass and stiffness, dynamic response of each part of a mixed structure differs significantly. The present paper aims at comparing of the optimum design of these structures under the coupled and decoupled models. Toward that goal, the coupled and decoupled time history analyses are performed and the optimum design of the two methods are compared. The results of the two approach show that the cost of the decoupled analysis is higher than the cost of the coupled analysis and the design of the decoupled method may be uneconomical, because the interaction between the two upper and lower parts is neglected.
 

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In this study, experimental and computational approaches are used in order to develop and optimize self-compacting concrete mixes (Artificial neural network, EVPS metaheuristic algorithm, Taguchi method). Initially, ten basic mix designs were tested, and an artificial neural network was trained to predict the properties of these mixes. The network was then used to generate ten optimized mixes using the EVPS algorithm. Three mixes with the highest compressive strength were selected, and additional tests were conducted using the Taguchi approach. Inputting these results, along with the initial mix designs, into a second trained neural network, 10 new mix designs were tested using the network. Two of these mixes did not meet the requirements for self-compacting concrete, specifically in the U-box test. However, the predicted compressive strength results showed excellent agreement with low error percentages compared to the laboratory results, which indicates the effectiveness of the artificial neural network in predicting concrete properties, thus indicating that self-compacting concrete properties can be predicted with reasonable accuracy. The paper emphasizes the reliability and cost-effectiveness of artificial neural networks in predicting concrete properties. The study highlights the importance of providing diverse and abundant training data to improve the accuracy of predictions. The results demonstrate that neural networks can serve as valuable tools for predicting concrete characteristics, saving time and resources in the process. Overall, the research provides insights into the development of self-compacting concrete mixes and highlights the effectiveness of computational approaches in optimizing concrete performance.
 

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This paper presents the chaotic variants of the particle swarm optimization-statistical regeneration mechanism (PSO-SRM). The nine chaotic maps named Chebyshev, Circle, Iterative, Logistic, Piecewise, Sine, Singer, Sinusoidal, and Tent are used to increase the performance of the PSO-SRM. These maps are utilized instead of the random number, which defines the solution generation method. The robustness and performance of these methods are tested in the three steel frame design problems, including the 1-bay 10-story steel frame, 3-bay 15-story steel frame, and 3-bay 24-story steel frame. The optimization results reveal that the applied chaotic maps improve the performance of the PSO-SRM.
 

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The global population growth and the subsequent surge in housing demand have inevitably led to an increase in the demand for concrete, and consequently, cement. This has posed environmental challenges, as cement factories are significant contributors to carbon dioxide emissions. One promising solution is to incorporate pozzolanic materials into concrete production. This study investigates the effects of using travertine sludge as a partial substitute for cement. Seven different mix designs, along with a control mix, were created and compared. The primary variable was the ratio of travertine sludge to cement weight, considered in intervals of 10%, 15%, 20%, 25%, 30%, 35%, and 40% of the cement's weight. Various tests were conducted, including compressive strength and flexural strength at ages of 7, 28, and 90 days, as well as a permeability test at 28 days. The findings revealed interesting patterns. At the 7-day mark, as the percentage of travertine sludge increased, there was a decrease in compressive strength. However, by the 28-day mark, the concrete displayed a varied behavior: using up to 30% travertine sludge by weight reduced the strength, but exceeding 30% resulted in increased strength. At the 90-day mark, an overall increase in strength was observed with the rise in travertine sludge percentage. Such pozzolanic effects on compressive strength were somewhat predictable. Additionally, based on the flexural strength tests, travertine sludge can be deemed a viable substitute for a certain percentage of cement by weight. This research underscores the potential of sustainable alternatives in the construction industry, promoting both professional development and personal branding for those engaged in eco-friendly practices.
 

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In order to evaluate the damage state, value, and position of structural members more accurately, a multi-objective optimization (MO) method is utilized that is based on changes in natural frequency. The multi-objective optimization dynamic-based damage detection method is first introduced. Two objective functions for optimization are then introduced in terms of changing the natural frequencies and mode shapes. The multi-objective optimization problem (MOP) is formulated by using the two objective functions. Three considered MO algorithms consist of Colliding Bodies Optimization (MOCBO), Particle Swarm Optimization (MOPSO), and non-dominated sorting genetic algorithm (NSGA-II) to achieve the best structural damage detection. The proposed methods are then applied to three planar steel frame structures. Compared to the traditional optimization methods utilizing the single-objective optimization (SO) algorithms, the presented methods provide superior results.

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This study aimed to develop and optimize artificial stone mix designs incorporating microsilica using artificial neural networks (ANNs) and metaheuristic optimization algorithms. Initially, 10 base mix designs were prepared and tested based on previous experience and literature. The test results were used to train an ANN model. The trained ANN was then optimized using SA-EVPS and EVPS algorithms to maximize 28-day compressive strength, with aggregate gradation as the optimization variable. The optimized mixes were produced and tested experimentally, revealing some discrepancies with the ANN predictions. The ANN was retrained using the original and new experimental data, and the optimization process was repeated iteratively until an acceptable agreement was achieved between predicted and measured strengths. This approach demonstrates the potential of combining ANNs and metaheuristic algorithms to efficiently optimize artificial stone mix designs, reducing the need for extensive physical testing.