Multi-objective optimization of foundation using global-local gravitational search algorithm

Khajehzadeh, Mohammad,Taha, Mohd Raihan,Eslami, Mahdiyeh Techno-Press 2014 Structural Engineering and Mechanics, An Int'l Jou Vol.50 No.3

This paper introduces a novel optimization technique based on gravitational search algorithm (GSA) for numerical optimization and multi-objective optimization of foundation. In the proposed method, a chaotic time varying system is applied into the position updating equation to increase the global exploration ability and accurate local exploitation of the original algorithm. The new algorithm called global-local GSA (GLGSA) is applied for optimization of some well-known mathematical benchmark functions as well as two design examples of spread foundation. In the foundation optimization, two objective functions include total cost and $CO_2$ emissions of the foundation subjected to geotechnical and structural requirements are considered. From environmental point of view, minimization of embedded $CO_2$ emissions that quantifies the total amount of carbon dioxide emissions resulting from the use of materials seems necessary to include in the design criteria. The experimental results demonstrate that, the proposed GLGSA remarkably improves the accuracy, stability and efficiency of the original algorithm.

Efficient gravitational search algorithm for optimum design of retaining walls

Khajehzadeh, Mohammad,Taha, Mohd Raihan,Eslami, Mahdiyeh Techno-Press 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.45 No.1

In this paper, a new version of gravitational search algorithm based on opposition-based learning (OBGSA) is introduced and applied for optimum design of reinforced concrete retaining walls. The new algorithm employs the opposition-based learning concept to generate initial population and updating agents' position during the optimization process. This algorithm is applied to minimize three objective functions include weight, cost and $CO_2$ emissions of retaining structure subjected to geotechnical and structural requirements. The optimization problem involves five geometric variables and three variables for reinforcement setups. The performance comparison of the new OBGSA and classical GSA algorithms on a suite of five well-known benchmark functions illustrate a faster convergence speed and better search ability of OBGSA for numerical optimization. In addition, the reliability and efficiency of the proposed algorithm for optimization of retaining structures are investigated by considering two design examples of retaining walls. The numerical experiments demonstrate that the new algorithm has high viability, accuracy and stability and significantly outperforms the original algorithm and some other methods in the literature.

Efficient gravitational search algorithm for optimum design of retaining walls

Mohammad Khajehzadeh,Mohd Raihan Taha,Mahdiyeh Eslami 국제구조공학회 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.45 No.1

In this paper, a new version of gravitational search algorithm based on opposition-based learning (OBGSA) is introduced and applied for optimum design of reinforced concrete retaining walls. The new algorithm employs the opposition-based learning concept to generate initial population and updating agents’ position during the optimization process. This algorithm is applied to minimize three objective functions include weight, cost and CO2 emissions of retaining structure subjected to geotechnical and structural requirements. The optimization problem involves five geometric variables and three variables for reinforcement setups. The performance comparison of the new OBGSA and classical GSA algorithms on a suite of five well-known benchmark functions illustrate a faster convergence speed and better search ability of OBGSA for numerical optimization. In addition, the reliability and efficiency of the proposed algorithm for optimization of retaining structures are investigated by considering two design examples of retaining walls. The numerical experiments demonstrate that the new algorithm has high viability, accuracy and stability and significantly outperforms the original algorithm and some other methods in the literature.

Multi-objective optimization of foundation using global-local gravitational search algorithm

Mohammad Khajehzadeh,Mohd Raihan Taha,Mahdiyeh Eslami 국제구조공학회 2014 Structural Engineering and Mechanics, An Int'l Jou Vol.50 No.3

This paper introduces a novel optimization technique based on gravitational search algorithm (GSA) for numerical optimization and multi-objective optimization of foundation. In the proposed method, achaotic time varying system is applied into the position updating equation to increase the global exploration ability and accurate local exploitation of the original algorithm. The new algorithm called global-local GSA (GLGSA) is applied for optimization of some well-known mathematical benchmark functions as well as two design examples of spread foundation. In the foundation optimization, two objective functions include total cost and CO2 emissions of the foundation subjected to geotechnical and structural requirements are considered. From environmental point of view, minimization of embedded CO2 emissions that quantifies the total amount of carbon dioxide emissions resulting from the use of materials seems necessary to include in the design criteria. The experimental results demonstrate that, the proposed GLGSA remarkably improves the accuracy, stability and efficiency of the original algorithm.

Optimum design of retaining structures under seismic loading using adaptive sperm swarm optimization

Mohammad Khajehzadeh,Amir Kalhor,Mehran Soltani Tehrani,Mohammadreza Jebeli 국제구조공학회 2022 Structural Engineering and Mechanics, An Int'l Jou Vol.81 No.1

The optimum design of reinforced concrete cantilever retaining walls subjected to seismic loads is an extremely important challenge in structural and geotechnical engineering, especially in seismic zones. This study proposes an adaptive sperm swarm optimization algorithm (ASSO) for economic design of retaining structure under static and seismic loading. The proposed ASSO algorithm utilizes a time-varying velocity damping factor to provide a fine balance between the explorative and exploitative behavior of the original method. In addition, the new method considers a reasonable velocity limitation to avoid the divergence of the sperm movement. The proposed algorithm is benchmarked with a set of test functions and the results are compared with the standard sperm swarm optimization (SSO) and some other robust metaheuristic from the literature. For seismic optimization of retaining structures, Mononobe-Okabe method is employed for dynamic loading conditions and total construction cost of the structure is considered as the single objective function. The optimization constraints include both geotechnical and structural restrictions and the design variables are the geometrical dimensions of the wall and the amount of steel reinforcement. Finally, optimization of two benchmark retaining structures under static and seismic loads using the ASSO algorithm is presented. According to the numerical results, the ASSO may provide better optimal solutions, and the designs obtained by ASSO have a lower cost by up to 20% compared with some other methods from the literature.

Optimized ANNs for predicting compressive strength of high-performance concrete

Hossein Moayedi,Amirali Eghtesad,Mohammad Khajehzadeh,Suraparb Keawsawasvong,Mohammed M. Al-Amidi,Bao Le Van 국제구조공학회 2022 Steel and Composite Structures, An International J Vol.44 No.6

Predicting the compressive strength of concrete (CSoC) is of high significance in civil engineering. The CSoC is a highly dependent and non-linear parameter that requires powerful models for its simulation. In this work, two novel optimization techniques, namely evaporation rate-based water cycle algorithm (ER-WCA) and equilibrium optimizer (EO) are employed for optimally finding the parameters of a multi-layer perceptron (MLP) neural processor. The efficiency of these techniques is examined by comparing the results of the ensembles to a conventionally trained MLP. It was observed that the ER-WCA and EO optimizers can enhance the training accuracy of the MLP by 11.18 and 3.12% (in terms of reducing the root mean square error), respectively. Also, the correlation of the testing results climbed from 78.80% to 82.59 and 80.71%. From there, it can be deduced that both ER-WCA-MLP and EO-MLP can be promising alternatives to the traditional approaches. Moreover, although the ER-WCA enjoys a larger accuracy, the EO was more efficient in terms of complexity, and consequently, time-effectiveness.

Three-dimensional vibration analysis of 3D graphene foam curved panels on elastic foundations

Li-Cai Zhao,Shi-Shuenn Chen,Mohammad Khajehzadeh,Mariwan Araz Yousif,Vahid Tahouneh 국제구조공학회 2022 Steel and Composite Structures, An International J Vol.43 No.1

This paper has focused on presenting a three dimensional theory of elasticity for free vibration of 3D-graphene foam reinforced polymer matrix composites (GrF-PMC) cylindrical panels resting on two-parameter elastic foundations. The elastic foundation is considered as a Pasternak model with adding a Shear layer to the Winkler model. The porous graphene foams possessing 3D scaffold structures have been introduced into polymers for enhancing the overall stiffness of the composite structure. Also, 3D graphene foams can distribute uniformly or non-uniformly in the shell thickness direction. The effective Young’s modulus, mass density and Poisson’s ratio are predicted by the rule of mixture. Three complicated equations of motion for the panel under consideration are semi-analytically solved by using 2-D differential quadrature method. The fast rate of convergence and accuracy of the method are investigated through the different solved examples. Because of using twodimensional generalized differential quadrature method, the present approach makes possible vibration analysis of cylindrical panels with two opposite axial edges simply supported and arbitrary boundary at the curved edges. It is explicated that 3D-GrF skeleton type and weight fracti