The Artificial Bee Colony (ABC) Algorithm is a sophisticated optimization technique that is inspired by the intelligent behaviors of honey bee swarms. These behaviors, such as foraging and communication within complex social structures, serve as the foundation for the algorithm's effectiveness. In this paper, the ABC algorithm is utilized to optimize the design of reinforced cantilever concrete retaining walls, with the goal of minimizing both cost and weight. The results are compared to existing literature, demonstrating the success of the ABC algorithm in achieving the objectives. Furthermore, a comparison is conducted between the optimized design and a conventional manual design, revealing a significant reduction in cost and weight through optimization. Additionally, two types of reinforced concrete cantilever retaining walls-a T-shape wall with variable stem thickness and a standard T-shape wall-are presented and compared, considering their differing variables and constraints. These comparisons are made for two objective functions: the cost and weight of the wall. To further investigate the impact of initial parameters, such as the unit weight of soil and stem height, a sensitivity analysis is conducted. The robustness of the ABC algorithm in optimizing the cost and weight of reinforced concrete cantilever retaining walls is demonstrated by the results.

Power consumption is one of the most challenging issues in design of electronic circuits. Reversible logic is a solution for power optimization. In this paper, we propose three fault-tolerant serial multiplier designs based on the reversible logic with error detection capability. The first proposed serial multiplier which is based on the Booth’s algorithm utilizes a new arrangement of reversible gates. The second proposed serial multiplier for signed numbers is based on a newer algorithm called the K algorithm. This algorithm requires less cost compared to the Booth's algorithm.The third proposed fault-tolerant serial multiplier which is optimized for unsigned number multiplication is based on the conventional Add & Shift method. The comparative results show that the proposed multipliers are much better than the existing designs considering the main criterions used in reversible logic circuits which include quantum cost, number of gates, number of garbage outputs, delay, and computational complexity.

Researchers' attention has recently been focused to the measurement and tracking of prestressing force in the tendons of prestressed concrete (PC) constructions. Older structures need non-destructive testing techniques to evaluate these forces, even if modern structures are fitted with sensors to monitor prestress losses. This work presents a new approach that uses static displacement data under experimental loads to determine the real prestress force in the tendons of a prestressed concrete beam. This approach offers a more economical alternative by doing away with the requirement for destructive tests or pre-installed sensors. A genetic algorithm (GA) is created to precisely calculate the prestress force of tendons. Laboratory testing shows that the proposed method can detect prestress losses with excellent accuracy, even in the presence of intentional measurement mistakes of up to 10%.Researchers' attention has recently been focused to the measurement and tracking of prestressing force in the tendons of prestressed concrete (PC) constructions. Older structures need non-destructive testing techniques to evaluate these forces, even if modern structures are fitted with sensors to monitor prestress losses. This work presents a new approach that uses static displacement data under experimental loads to determine the real prestress force in the tendons of a prestressed concrete beam. This approach offers a more economical alternative by doing away with the requirement for destructive tests or pre-installed sensors. A genetic algorithm (GA) is created to precisely calculate the prestress force of tendons. Laboratory testing shows that the proposed method can detect prestress losses with excellent accuracy, even in the presence of intentional measurement mistakes of up to 10%.

This study focuses on the investigation of intelligent form-finding and vibration analysis of a triangular polyhedral tensegrity that is enclosed within a sphere and subjected to external loads. The nonlinear dynamic equations of the system are derived using the Lagrangian approach and the finite element method. The proposed form-finding approach, which is based on a basic genetic algorithm, can determine regular or irregular tensegrity shapes without dimensional constraints. Stable tensegrity structures are generated from random configurations and based on defined constraints (nodes located on the sphere, parallelism, and area of upper and lower surfaces), and shape finding is performed using the fitness function of the genetic algorithm and multi-objective optimization goals. The genetic algorithm's efficacy in determining the shape of structures with unpredictable configurations is evaluated in two distinct scenarios: one involving a known connection matrix and the other involving fixed or random member positions (struts and cables). The shapes obtained from the algorithm suggested in this study are validated using the force density approach, and their vibration characteristics are examined. The findings of the comparative study demonstrate the efficacy of the proposed methodology in determining the vibrational behavior of tensegrity structures through the utilization of intelligent shape seeking techniques.

Increasing population and food demand, disproportionate cultivation and annual production of various agricultural products with market needs and low productivity of the agricultural sector and the loss of water and soil resources have made it necessary to determine and implement the country's optimal cropping pattern. In this study, due to the limitations and problems of classical methods in order to reduce processing time and improve the quality of solutions, the Multi-Objective Chaotic Particle Swarm Optimization was used to determine the optimal cultivation pattern of Sistan plain in optimal conditions and deficit irrigation. The results of the Multi-Objective Chaotic Particle Swarm Optimization for the dominant cultures in the region showed that the current cropping pattern of the region is not optimal and with the implementation of the proposed model, the profit per unit area under cultivation will increase. The results of application of deficit irrigation during different growing periods of wheat, barley, alfalfa, sorghum, watermelon and grapes showed that applying deficit irrigation in this plain is not a good strategy and therefore only a full irrigation strategy is recommended. The results of sensitivity analysis of the model showed that at low prices, farmers reaction is less and at higher prices more reaction to price changes and with increasing prices, the program efficiency is lower.

One of the basic topics in hydrological and river engineering studies is flood routing.Flood flooding is common in multi-tributary rivers and rivers without intermediate basin statistics. Therefore, to achieve the determination of slopes and cross-sections in all sections of the river, the Muskingum hydrological model is a useful method that helps to save information on the depth and flow of the flood at any time by saving time and money. To specify. In this study, the nonlinear parameters of the new Muskingum model are optimized based on the fly algorithm (MA). In this non-linear model of Muskingum, which has eight parameters, the recovery coefficient γ is used, which has more or less values ​​than the number of peaks discharged in the output hydrograph.To evaluate the performance of Muskingum's new nonlinear model with the new MA algorithm, the Wilson and Weisman-Lewis case study has been used by many previous researchers for validation.The results of the MA algorithm for Wilson and Weissman-Lewis rivers show the minimization of the residual squares (SSQ) as the objective function, which is 3.21 for the Wilson River and 68722 for the Weissman River. The results of this study showed that the proposed model has high accuracy in estimating the output discharge values.

This article investigates the problem of simultaneous attitude and vibration control of a flexible spacecraft to perform high precision attitude maneuvers and reduce vibrations caused by the flexible panel excitations in the presence of external disturbances, system uncertainties, and actuator faults. Adaptive integral sliding mode control is used in conjunction with an attitude actuator fault iterative learning observer (based on sliding mode) to develop an active fault tolerant algorithm considering rigid-flexible body dynamic interactions. The discontinuous structure of fault-tolerant control led to discontinuous commands in the control signal, resulting in chattering. This issue was resolved by introducing an adaptive rule for the sliding surface. Furthermore, the utilization of the sign function in the iterative learning observer for estimating actuator faults has not only enhanced its robustness to external disturbances through a straightforward design, but has also led to a decrease in computing workload. The strain rate feedback control algorithm has been employed with the use of piezoelectric sensor/actuator patches to minimize residual vibrations caused by rigid-flexible body dynamic interactions and the effect of attitude actuator faults. Lyapunov's law ensures finite-time overall system stability even with fully coupled rigid-flexible nonlinear dynamics. Numerical simulations demonstrate the performance and advantages of the proposed system compared to other conventional approaches.