scholarly journals Metaheuristic Optimization of Reinforced Concrete Footings

2020 ◽  
pp. 116-140
Keyword(s):  
Reinforced Concrete ◽  
Harmony Search ◽  
Flower Pollination Algorithm ◽  
Limit States ◽  
Flower Pollination ◽  
Design Variables ◽  
Reinforced Concrete Member ◽  
Teaching Learning Based Optimization ◽  
Complex Engineering ◽  
Teaching Learning

Footings are one of the structural members, which is one of the complex engineering problems to optimize. Differently from the other reinforced concrete member designs such as beams and columns, geotechnical limit states are also needed to consider in addition to structural state limits. In this chapter, the optimum design of RC footing is presented according to ACI 318: Building code requirements for structural concrete. The optimum results of methodologies employing different algorithms including Harmony Search (HS), Teaching-Learning-Based Optimization algorithm (TLBO), and Flower Pollination Algorithm (FPA). In the methodologies, the design variables are values about dimensions of the footing, the orientation of the supported columns and reinforcements. Also, a simplified methodology is also presented with a design code employing HS.

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.

Airfoil Topology Optimization using Teaching-Learning based Optimization

2015 ◽  
Vol 6 (1) ◽  
pp. 23-34
Author(s):  
Dushhyanth Rajaram ◽  
Himanshu Akhria ◽  
S. N. Omkar
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Spline Curves ◽  
Conceptual Design Phase ◽  
Design Variables ◽  
Heuristic Technique ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

This paper primarily deals with the optimization of airfoil topology using teaching-learning based optimization, a recently proposed heuristic technique, investigating performance in comparison to Genetic Algorithm and Particle Swarm Optimization. Airfoil parametrization and co-ordinate manipulations are accomplished using piecewise b-spline curves using thickness and camber for constraining the design space. The aimed objective of the exercise was easy computation, and incorporation of the scheme into the conceptual design phase of a low-reynolds number UAV for the SAE Aerodesign Competition. The 2D aerodynamic analyses and optimization routine are accomplished using the Xfoil code and MATLAB respectively. The effects of changing the number of design variables is presented. Also, the investigation shows better performance in the case of Teaching-Learning based optimization and Particle swarm optimization in comparison to Genetic Algorithm.

scholarly journals Eco-Friendly Design of Reinforced Concrete Retaining Walls: Multi-objective Optimization with Harmony Search Applications

2020 ◽  
Vol 12 (15) ◽  
pp. 6087 ◽  
Author(s):  
Aylin Ece Kayabekir ◽  
Zülal Akbay Arama ◽  
Gebrail Bekdaş ◽  
Sinan Melih Nigdeli ◽  
Zong Woo Geem
Keyword(s):  
Reinforced Concrete ◽  
Co2 Emission ◽  
Search Algorithm ◽  
Harmony Search ◽  
Retaining Walls ◽  
Unit Weight ◽  
Flower Pollination Algorithm ◽  
Objective Functions ◽  
Multi Objective ◽  
The Cost

In this study, considering the eco-friendly design necessities of reinforced concrete structures, the acquirement of minimizing both the cost and the CO2 emission of the reinforced concrete retaining walls in conjunction with ensuring stability conditions has been investigated using harmony search algorithm. Optimization analyses were conducted with the use of two different objective functions to discover the contribution rate of variants to the cost and CO2 emission individually. Besides this, the integrated relationship of cost and CO2 emission was also identified by multi-objective analysis in order to identify an eco-friendly and cost-effective design. The height of the stem and the width of the foundation were treated as design variables. Several optimization cases were fictionalized in relation with the change of the depth of excavation, the amount of the surcharge applied at the top of the wall system at the backfill side, the unit weight of the backfill soil, the costs, and CO2 emission amounts of both the concrete and the reinforcement bars. Consequently, the results of the optimization analyses were arranged to discover the possibility of supplying an eco-friendly design of retaining walls with the minimization of both cost and gas emission depending upon the comparison of outcomes of the identified objective functions. The proposed approach is effective to find both economic and ecological results according to hand calculations and flower pollination algorithm.

scholarly journals An Optimal Design of CMOS Two Stage Comparator Circuit using Swarm Intelligence Technique

2018 ◽  
Vol 7 (3) ◽  
pp. 125
Author(s):  
Sasikumar Sasikumar ◽  
Muthaiah Muthaiah
Keyword(s):  
Swarm Intelligence ◽  
Optimization Technique ◽  
Harmony Search ◽  
Flower Pollination Algorithm ◽  
Two Stage ◽  
Mos Transistor ◽  
Flower Pollination ◽  
Input Control ◽  
Simulation Based ◽  
Intelligence Technique

A swarm intelligent based optimization technique named as Flower pollination algorithm (FPA) is applied for the design of the CMOS two stage comparator circuit. The basic idea of FPA mimics the flower pollination process of flowering plants. The input control parameters of FPA improve the exploration and exploitation capabilities of optimization problem. This paper presents the design of a CMOS two-stage comparator circuit using simulation based model called swarm intelligence technique. Simulation results shows that the proposed method is capable to determine the transistor sizes and bias current values of the CMOS comparator. The results obtained from the FPA improved the design performance of comparator in terms of power consumption, MOS transistor area and gain. To investigate the efficiency of proposed approach, comparisons have been carried out with differential evolution (DE) and harmony search (HS) algorithm based circuit design. The performances of FPA based comparator design are better than the previously reported works

scholarly journals An Optimal Design of CMOS Two Stage Comparator Circuit using Swarm Intelligence Technique

2018 ◽  
Vol 7 (3) ◽  
pp. 131
Author(s):  
Sasikumar Sasikumar ◽  
Muthaiah Muthaiah
Keyword(s):  
Swarm Intelligence ◽  
Optimization Technique ◽  
Harmony Search ◽  
Flower Pollination Algorithm ◽  
Two Stage ◽  
Mos Transistor ◽  
Flower Pollination ◽  
Input Control ◽  
Simulation Based ◽  
Intelligence Technique

A swarm intelligent based optimization technique named as Flower pollination algorithm (FPA) is applied for the design of the CMOS two stage comparator circuit. The basic idea of FPA mimics the flower pollination process of flowering plants. The input control parameters of FPA improve the exploration and exploitation capabilities of optimization problem. This paper presents the design of a CMOS two-stage comparator circuit using simulation based model called swarm intelligence technique. Simulation results shows that the proposed method is capable to determine the transistor sizes and bias current values of the CMOS comparator. The results obtained from the FPA improved the design performance of comparator in terms of power consumption, MOS transistor area and gain. To investigate the efficiency of proposed approach, comparisons have been carried out with differential evolution (DE) and harmony search (HS) algorithm based circuit design. The performances of FPA based comparator design are better than the previously reported works

scholarly journals Design optimization of a three-stage transmission using advanced optimization techniques

2019 ◽  
Vol 10 ◽  
pp. A8 ◽  
Author(s):  
Edmund S. Maputi ◽  
Rajesh Arora
Keyword(s):  
Geometric Model ◽  
Optimization Techniques ◽  
Stress Variation ◽  
Design Variables ◽  
Teaching Learning Based Optimization ◽  
Machine Elements ◽  
Mechanical Devices ◽  
Compact Design ◽  
Teaching Learning ◽  
Intended Use

Gear transmission systems are very important machine elements and their failure can lead to losses or damage of other mechanical components that comprise a machine or device. Since gears are applied in numerous mechanical devices, there is need to design and subsequently optimize them for intended use. In the present work, two objectives, viz., volume and center distance, are minimized for a rotary tiller to achieve a compact design. Two methods were applied: (1) analytical method, (2) a concatenation of the bounded objective function method and teaching–learning-based optimization techniques, thereby improving the result by 44% for the former and 55% for the latter. Using a geometric model and previous literature, the optimal results obtained were validated with 0.01 variation. The influence of design variables on the objective functions was also evaluated using variation studies reflecting on a ranking according to objective. Bending stress variation of 12.4% was less than contact stress at 51% for a defined stress range.

scholarly journals Adaptive-Hybrid Harmony Search Algorithm for Multi-Constrained Optimum Eco-Design of Reinforced Concrete Retaining Walls

2021 ◽  
Vol 13 (4) ◽  
pp. 1639
Author(s):  
Melda Yücel ◽  
Aylin Ece Kayabekir ◽  
Gebrail Bekdaş ◽  
Sinan Melih Nigdeli ◽  
Sanghun Kim ◽  
...  
Keyword(s):  
Reinforced Concrete ◽  
Structural Engineering ◽  
Optimization Problem ◽  
Search Algorithm ◽  
Harmony Search ◽  
Retaining Walls ◽  
Comparative Investigation ◽  
Flower Pollination Algorithm ◽  
Local Optimum ◽  
Population Number

In the optimum design of reinforced concrete (RC) structural members, the robustness of the employed method is important as well as solving the optimization problem. In some cases where the algorithm parameters are defined as non-effective values, local-optimum solutions may prevail over the existing global optimum results. Any metaheuristic algorithm can be effective to solve the optimization problem but must give the same results for several runs. Due to the randomization nature of these algorithms, the performance may vary with respect to time. The essential and novel work done in this study is the comparative investigation of 10 different metaheuristic algorithms and two modifications of harmony search (HS) algorithm on the optimum cost design of RC retaining walls constrained with geotechnical and structural state limits. The employed algorithms include classical ones (genetic algorithm (GA), differential evaluation (DE), and particle swarm optimization (PSO)), proved ones on structural engineering applications (harmony search, artificial bee colony, firefly algorithm), and recent algorithms (teaching–learning-based optimization (TLBO), flower pollination algorithm (FPA), grey wolf optimization, Jaya algorithm (JA)). The modifications of HS include adaptive HS (AHS) concerning the automatic change of algorithm parameters and hybridization of AHS with JA that is developed for the investigated problem. According to the numerical investigations, recent algorithms such as TLBO, FPA, and JA are generally the best at finding the optimum values with less deviation than the others. The adaptive-hybrid HS proposed in this study is also competitive with these algorithms, while it can reach the best solution by using a lower population number which can lead to timesaving in the optimization process. By the minimization of material used in construction via best optimization, sustainable structures that support multiple types of constraints are provided.

Sign in / Sign up

Export Citation Format

Share Document