Optimum design of gravity and reinforced retaining walls using enhanced charged system search algorithm

2014 ◽  
Vol 18 (5) ◽  
pp. 1464-1469 ◽  
Author(s):  
S. Talatahari ◽  
R. Sheikholeslami
Keyword(s):  
Optimum Design ◽  
Search Algorithm ◽  
Retaining Walls ◽  
Charged System Search ◽  
Charged System

scholarly journals Optimum Design of Gravity Retaining Walls Using Charged System Search Algorithm

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
S. Talatahari ◽  
R. Sheikholeslami ◽  
M. Shadfaran ◽  
M. Pourbaba
Keyword(s):  
Optimum Design ◽  
Search Algorithm ◽  
Retaining Wall ◽  
Earth Pressure ◽  
Retaining Walls ◽  
Stone Masonry ◽  
Charged System Search ◽  
Charged System ◽  
Major Factors ◽  
The Cost

This study focuses on the optimum design retaining walls, as one of the familiar types of the retaining walls which may be constructed of stone masonry, unreinforced concrete, or reinforced concrete. The material cost is one of the major factors in the construction of gravity retaining walls therefore, minimizing the weight or volume of these systems can reduce the cost. To obtain an optimal seismic design of such structures, this paper proposes a method based on a novel meta-heuristic algorithm. The algorithm is inspired by the Coulomb's and Gauss’s laws of electrostatics in physics, and it is called charged system search (CSS). In order to evaluate the efficiency of this algorithm, an example is utilized. Comparing the results of the retaining wall designs obtained by the other methods illustrates a good performance of the CSS. In this paper, we used the Mononobe-Okabe method which is one of the pseudostatic approaches to determine the dynamic earth pressure.

Optimum Design of Reinforced Concrete Retaining Walls

2018 ◽  
pp. 360-378
Author(s):  
Rasim Temür ◽  
Gebrail Bekdaş
Keyword(s):  
Reinforced Concrete ◽  
Optimum Design ◽  
Search Algorithm ◽  
Harmony Search ◽  
Minimum Cost ◽  
Retaining Walls ◽  
Slab Thickness ◽  
Construction Costs ◽  
Design Variables ◽  
Cantilever Retaining Walls

Methodologies based on metaheuristic algorithms such as particle swarm optimization, harmony search algorithm, and teaching-learning-based optimization are proposed for optimum design of reinforced concrete cantilever retaining walls. The objective function of optimization is to find a design providing minimum cost, including material and construction costs. For this purpose, the best combination of 11 design variables (heel and toe projections, stem thickness at the top and bottom of a wall, slab thickness and rebar diameters, and spacing between the bars) that satisfy 29 design constraints including stability (overturning, sliding, and bearing) and reinforced concrete design of the wall are searched during the optimization process. The rules of ACI 318 14 (building code requirements for structural concrete) are used for the reinforced concrete design. In order to determine the strengths and weaknesses of algorithms, several different cases are investigated. As conclusions, some suggestions have been obtained that will lead to future work in this field.

Novel Adaptive Charged System Search algorithm for optimal tuning of fuzzy controllers

2014 ◽  
Vol 41 (4) ◽  
pp. 1168-1175 ◽  
Author(s):  
Radu-Emil Precup ◽  
Radu-Codruţ David ◽  
Emil M. Petriu ◽  
Stefan Preitl ◽  
Mircea-Bogdan Rădac
Keyword(s):  
Search Algorithm ◽  
Fuzzy Controllers ◽  
Optimal Tuning ◽  
Charged System Search ◽  
Charged System

scholarly journals REDUCTION OF ACTIVE POWER LOSS BY ADAPTIVE CHARGED SYSTEM SEARCH ALGORITHM

2017 ◽  
Vol 5 (10) ◽  
pp. 34-45
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Search Algorithm ◽  
Active Power ◽  
Newtonian Mechanics ◽  
Charged System Search ◽  
Optimal Power ◽  
Multi Agent ◽  
Charged System ◽  
Power Problem

This paper presents a new optimization algorithm called Adaptive Charged System Search Algorithm (ACA) for solving optimal power problem. Coulomb law from electrostatics and the Newtonian laws of mechanics are forming the basics of the proposed algorithm. Adaptive Charged System Search Algorithm (ACA) is a multi-agent approach in which each agent is a Charged Particle (CP) & they affect each other based on their fitness values, separation of distances. The quantity of the resultant force is determined by using the electrostatics laws and the quality of the movement is determined using Newtonian mechanics laws. Proposed Adaptive Charged System Search Algorithm (ACA) has been tested in Standard IEEE 57,118 bus systems & real power loss has been comparatively reduced with voltage profiles are within the limits.

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