6 results on '"Onur Pekcan"'
Search Results
2. Flower Pollination Algorithm for Slope Stability Analysis
- Author
-
A. Öcal, Y. E. Sarıçiçek, and Onur Pekcan
- Subjects
Range (mathematics) ,Factor of safety ,Safety factor ,Slope stability ,Limit (mathematics) ,Slip (materials science) ,Slope stability analysis ,Algorithm ,Metaheuristic ,Mathematics - Abstract
Slope stability analysis is considered to be one of the most critical problems in geotechnical engineering. Typically, the analysis consists of two parts: (i) determination of the slip surface and (ii) calculation of the factor of safety. The primary aim is to find the most critical slip surface with the lowest factor of safety, which generally governs the behavior of landslide. Due to the nature of soils, the problem is very complex and requires solving several equilibrium equations simultaneously; therefore, obtaining accurate results can be a complicated task. In this study, a novel metaheuristic method, namely flower pollination algorithm (FPA), is proposed to locate the slip surface for various slope stability problems. The developed method utilizes Spencer and Janbu’s simplified limit equilibrium equations to calculate the safety factor. To investigate the performance of the proposed method, a set of slope stability problems having different soil properties with various geometries are solved, and results are compared with the solutions previously obtained in the literature. Findings indicate that FPA can effectively solve slope stability problems and reaches the critical slip surfaces producing the lowest safety factors when compared to those obtained using traditional optimization methods and some of the metaheuristic algorithms. Consequently, it is achieved that FPA presents competitive performance against well-known algorithms. Further improvements are on the way for FPA, which can be implemented for solving a broader range of slope stability problems.
- Published
- 2021
- Full Text
- View/download PDF
3. Optimum Sizing of Truss Structures Using a Hybrid Flower Pollinations
- Author
-
Onur Pekcan and Murat Altun
- Subjects
Mathematical optimization ,Optimization problem ,Computer science ,Search algorithm ,business.industry ,Differential evolution ,Crossover ,Truss ,Local search (optimization) ,business ,Hybrid algorithm ,Metaheuristic - Abstract
The truss sizing optimization problem, where the cross-sectional areas of truss members are optimized, is a nonlinear and complex problem. The problem becomes quite challenging especially when a large number of variables exist. Metaheuristics proved to be working efficiently for this problem type. Flower pollination algorithm (FPA) is a recently developed metaheuristic search algorithm, and it has been successfully applied to structural optimization problems in the literature. In this paper, the formulation of FPA is enhanced to improve its local and global searching capabilities for better performance. For this purpose, a hybrid algorithm is developed by imitating the algorithmic structure of FPA while the mutation and crossover operators of differential evolution (DE) with elitism strategy are replaced with Levy flights of FPA to explore search space more efficiently and exploitation ability of FPA is improved with the addition of mutation factor and crossover operator of DE working in the local search formula of FPA. The performance of the proposed algorithm is tested with benchmark truss sizing optimization problems and the results are compared with other metaheuristics. The results show that hybrid FPA improves the capabilities of the original one, produces more robust solutions than both FPA and DE, generates quite competitive results for the test problems compared to the previous studies with different algorithms. Therefore, the hybrid FPA proves to be a promising optimization algorithm alternative for engineering practice.
- Published
- 2021
- Full Text
- View/download PDF
4. Optimizing Reinforced Cantilever Retaining Walls Under Dynamic Loading Using Improved Flower Pollination Algorithm
- Author
-
Onur Pekcan, M. Türkezer, E. B. Tutuş, and Murat Altun
- Subjects
Cantilever ,Optimization problem ,Dynamic loading ,Computer science ,Range (statistics) ,Soil parameters ,Retaining wall ,Algorithm ,Metaheuristic ,Cost savings - Abstract
Retaining walls need to be designed optimally since huge cost savings are probable considering their dimensions and materials used in the design and construction. Even better savings are possible when they are constructed in earthquake-prone regions. In this study, an improved flower pollination algorithm (IFPA) is used to optimize the design of the reinforced concrete cantilever retaining wall subjected to dynamic loadings. The mathematical model contains three constraints including geotechnical, structural, and geometrical considerations. As the previous FPA applications revealed the efficiency of this method for retaining wall problems, some modifications have been made on the existing method when dynamic loadings are included. To reveal the performance of IFPA, sensitivity analyses are carried out using a variety of soil parameters. Also, tuning of IFPA parameters is illustrated with two different retaining wall case studies reported in the literature. The results indicate that IFPA is a viable alternative to the well-known metaheuristics. This study also reveals that there is space for further improvements to cover wider range of geotechnical engineering-related optimization problems.
- Published
- 2021
- Full Text
- View/download PDF
5. A Hybrid Cuckoo Search Algorithm for Cost Optimization of Mechanically Stabilized Earth Walls
- Author
-
Murat Altun, Yagizer Yalcin, and Onur Pekcan
- Subjects
Range (mathematics) ,Computer science ,Differential evolution ,Genetic algorithm ,Particle swarm optimization ,Cuckoo search ,Implementation ,Algorithm ,Metaheuristic ,Engineering optimization - Abstract
Having a wide range of applications in civil engineering practice, Mechanically Stabilized Earth Walls (MSEWs) are regarded as efficient and reliable alternatives to the conventional retaining structure types. As is often the case in engineering, the performance and cost-effectiveness of these structures rely on robust design strategies, which must be proficient to yield optimal solutions in multimodal spaces. While the inherent characteristics of engineering problems often render the design a challenging task, metaheuristic algorithms are suitable options provided that problem-specific considerations and modifications are implemented. In this regard, Cuckoo Search (CS) and its variants are successful in many engineering applications. In the present study, CS is adopted to optimize the reinforcement type, length, and layout of MSEWs and a hybrid CS (HCSDE) variant based on Differential Evolution formulation is developed to further enhance the search capability of the algorithm. The proposed algorithm is applied to various MSEW design benchmarks and comparatively evaluated with respect to well-established methods such as Genetic Algorithm and Particle Swarm Optimization. The results of the study indicate that CS is competent for the problem and the capability of the algorithm can be further enhanced through the proposed adaptations in HCSDE. The improved solutions of HCSDE compared to the other optimization methods highlight the proposed formulation as a promising algorithm for practical implementations.
- Published
- 2020
- Full Text
- View/download PDF
6. Cuckoo Search Based Backcalculation Algorithm for Estimating Layer Properties of Full-Depth Flexible Pavements
- Author
-
Onur Pekcan and Arda Öcal
- Subjects
Software ,Artificial neural network ,Computer science ,business.industry ,Deflection (engineering) ,Computer Science::Neural and Evolutionary Computation ,Particle swarm optimization ,Cuckoo search ,business ,Material properties ,Metaheuristic ,Algorithm ,Finite element method - Abstract
This study introduces a backcalculation algorithm to estimate the material properties of the full-depth asphalt pavements. The proposed algorithm, namely CS-ANN, uses an Artificial Neural Network (ANN) based forward response engine, which is developed from the solutions of nonlinear finite element analysis to calculate the deflections mathematically. In the backward phase of the method, Cuckoo Search (CS), is utilized to search for the layer moduli values. The performance of the proposed method is investigated by analyzing the synthetically calculated deflections by a finite element based software and deflection data obtained from the field. In addition, to evaluate the searching capability of CS, optimization algorithms widely used in pavement backcalculation; Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Gravitational Search Algorithm (GSA), are employed for comparison purposes. Obtained results indicate that the proposed backcalculation approach is able to determine stiffness-related layer properties in an accurate and rapid manner. In addition, CS presents a promising performance in reaching the optimum solutions that are better than GA, PSO, and GSA.
- Published
- 2020
- Full Text
- View/download PDF
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.