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Optimization has become increasingly significant and applicable in resolving numerous engineering challenges, particularly in the structural engineering field. As computer science has advanced, various population-based optimization algorithms have been developed to address these challenges. These methods are favored by most researchers because of the difficulty of calculations in classical optimization methods and achieving ideal solutions in a shorter time in metaheuristic technique methods. Recently, there has been a growing interest in optimizing truss structures. This interest stems from the widespread utilization of truss structures, which are known for their uncomplicated design and quick analysis process. In this paper, the weight of the truss, the cross-sectional area of the members as discrete variables, and the coordinates of the truss nodes as continuous variables are optimized using the HGPG algorithm, which is a combination of three metaheuristic algorithms, including the Gravity Search Algorithm (GSA), Particle Swarm Optimization (PSO), and Gray Wolf Optimization (GWO). The presented formulation aims to improve the weaknesses of these methods while preserving their strengths. In this research, 15, 18, 25, and 47-member trusses were utilized to show the efficiency of the HGPG method, so the weight of these examples was optimized while their constraints such as stress limitations, displacement constraints, and Euler buckling were considered. The proposed HGPG algorithm operates in discrete and continuous modes to optimize the size and geometric configuration of truss structures, allowing for comprehensive structural optimization. The numerical results show the suitable performance of this process.

In recent years, there has been a lot of interest in the development and deployment of control methods that use different components of the building to mitigate the seismic response of the structure. Meanwhile, the building facade, as a non-structural component, can be a suitable alternative in affecting the structure's behavior because of its role as an envelope of the building with a significant weight. Among the modular cladding systems, the Double Skin Facade (DSF) can be considered a passive system due to the distance of the exterior layer from the main structure and sufficient continuity and rigidity.  In this study, DSF systems are used as Peripheral Mass Dampers (PMDs) that control structural movements by dissipating energy during strong motions. The PMD system provides a building with several inherent dampers without the need for extra mass. To show the reliability and efficiency of the proposed approach, the PMD model is investigated and compared with results available in uncontrolled and Tuned Mass Damper (TMD) models. The PMD model is examined in three structural frames with 10, 20, and 30 stories with the extreme Mass Ratios (MRs) of 5% to 20%. The Particle Swarm Optimization (PSO) is performed on damper parameters of PMD and TMD systems to minimize structural responses. The results demonstrate that an optimal PMD system with multiple inherent mass dampers outperforms a single TMD system.

This study presents a new method of damage detection using a combination of the second-order gradient Levenberg-Marquardt algorithm (SOGLMA) and fuzzy logic (FL) to solve the nonlinear damage detection equation for space frame structures. For damage detection in structures with a large number of degrees of freedom using the second-order gradient Levenberg-Marquardt algorithm, it is necessary to perform an iterative process of analysis and solving a set of simultaneous nonlinear equations that requires a lot of time. Therefore, the computation time and the number of iterations are reduced by using the proposed method "Combination of the second-order gradient Levenberg-Marquardt algorithm and fuzzy logic (SOGLMA-FL)". Acceleration response in sensor nodes obtained at different time steps from dynamic analysis are considered as input values for fuzzification. The output values of the proposed method after defuzzification are the damage extent of structural elements. The results show that the proposed damage detection method (SOGLMA-FL) has faster convergence, lower numbers of iteration and reduced computation time than the damage detection method (SOGLMA) for space frame structures.

Presently, the introduction of intelligent models to optimize structural problems has become an important issue in civil engineering and almost all other fields of engineering. Optimization models in artificial intelligence have enabled us to provide powerful and practical solutions to structural optimization problems. In this study, a novel method for optimizing structures as well as solving structure-related problems is presented. The main purpose of this paper is to present an algorithm that addresses the major drawbacks of commonly-used algorithms including the Grey Wolf Optimization Algorithm (GWO), the Gravitational Search Algorithm (GSA), and the Particle Swarm Optimization Algorithm (PSO), and at the same time benefits from a high convergence rate. Also, another advantage of the proposed CGPGC algorithm is its considerable flexibility to solve a variety of optimization problems. To this end, we were inspired by the GSA law of gravity, the GWO's top three search factors, the PSO algorithm in calculating speed, and the cellular machine theory in the realm of population segmentation. The use of cellular neighborhood reduces the likelihood of getting caught in the local optimal trap and increases the rate of convergence to the global optimal point. Achieving reasonable results in mathematical functions (CEC 2005) and spatial structures (with a large number of variables) in comparison with those from GWO, GSA, PSO, and some other common heuristic algorithms shows an enhancement in the performance of the introduced method compared to the other ones.

In this work, a new hybrid Symbiotic Organisms Search (SOS) algorithm introduced to design and optimize spatial and planar structures under structural constraints. The SOS algorithm is inspired by the interactive behavior between organisms to propagate in nature. But one of the disadvantages of the SOS algorithm is that due to its vast search space and a large number of organisms, it may trap in a local optimum. To fix this problem Harmony search (HS) algorithm, which has a high exploration and high exploitation, is applied as a complement to the SOS algorithm. The weight of the structureschr('39') elements is the objective function which minimized under displacement and stress constraints using finite element analysis. To prove the high capabilities of the new algorithm several spatial and planar benchmark truss structures, designed and optimized and the results have been compared with those of other researchers. The results show that the new algorithm has performed better in both exploitation and exploration than other meta-heuristic and mathematics methods.

The purpose of the present study is the damage detection in the thin plates in terms of the wide application of such structures in various branches of engineering such as structural, mechanical, aerospace, shipbuilding, etc. using gradient-based second-order numerical optimization techniques. The technique used for optimization in this study is the second-order Levenberg-Marquardt algorithm (SOLMA). Using the acceleration response in a number of structural nodes under dynamic excitation, identification of the location and extent of damage in the plate elements are obtained by the proposed algorithm over an iterative cycle and by updating the sensitivity matrix. The damage has been assumed in the form of decreased modulus of elasticity in linear mode. A numerical problem has been solved and presented in order to verify and compare the proposed damage detection method with other methods. Also several numerical problems have been solved and its results have been presented in order to evaluate different scenarios such as one or more damages, small or large damage extent, absence or presence of noise with different levels, number of measured responses (number of sensors), position of measured points and the dynamic analysis time of the damage detection problem with the proposed method. The results show the appropriate accuracy, efficiency and performance of the proposed damage detection method.

We optimized the geometries of the graphene and graphene with hydrogen using PW91VWN, PWCIPL,MPWLYP, G96LYP, G96141.0-210.6-310, 6-31G*Ievels of theory and compared our results with each other.We present the most important structural parameters determined for the addition of a hydrogen atom tographene and the outward movement of the carbon atom that is bonded to hydrogen is 0.48 A Also wecalculateed vibrational frequencies at the same levels. All thenned)mamic parameters of including AG, H. ASwere calculated