This page has only limited features, please log in for full access.
Melda Yücel currently works at the Department of Industrial Engineering and Civil Engineering, Istanbul University-Cerrahpaşa.
Total Potential Optimization using Metaheuristic Algorithms (TPO/MA) is an alternative tool for the analysis of structures. It is shown that this emerging method is advantageous in solving nonlinear problems like trusses, tensegrity structures, cable networks, and plane stress systems. In the present study, TPO/MA, which does not need any specific implementation for nonlinearity, is demonstrated to be successfully applied to the analysis of plane strain structures. A numerical investigation is performed using nine different metaheuristic algorithms and an adaptive harmony search in linear analysis of a structural mechanics problem having 8 free nodes defined as design variables in the minimization problem of total potential energy. For nonlinear stress-strain relation cases, two structural mechanics problems, one being a thick-walled pipe and the other being a cantilever retaining wall, are analyzed by employing adaptive harmony search, which was found to be the best one in linear analyses. The nonlinear stress-strain relations considered in these analyses are hypothetical ones due to the lack of any such relationship in the literature. The results have shown that TPO/MA can solve nonlinear plane strain problems that can be encountered as engineering problems in structural mechanics.
Yusuf Toklu; Gebrail Bekdaş; Melda Yücel; Sinan Nigdeli; Aylin Kayabekir; Sanghun Kim; Zong Geem. Total Potential Optimization Using Metaheuristic Algorithms for Solving Nonlinear Plane Strain Systems. Applied Sciences 2021, 11, 3220 .
AMA StyleYusuf Toklu, Gebrail Bekdaş, Melda Yücel, Sinan Nigdeli, Aylin Kayabekir, Sanghun Kim, Zong Geem. Total Potential Optimization Using Metaheuristic Algorithms for Solving Nonlinear Plane Strain Systems. Applied Sciences. 2021; 11 (7):3220.
Chicago/Turabian StyleYusuf Toklu; Gebrail Bekdaş; Melda Yücel; Sinan Nigdeli; Aylin Kayabekir; Sanghun Kim; Zong Geem. 2021. "Total Potential Optimization Using Metaheuristic Algorithms for Solving Nonlinear Plane Strain Systems." Applied Sciences 11, no. 7: 3220.
The optimization methods for structural engineering problem are effective, but the optimization process must be repeated for each case of design variables. For that reason, artificial intelligence methods can be used to develop prediction models for optimum design variables of engineering problem. For this purpose, sets of optimum results are needed to be used in machine learning training. Metaheuristic methods are employed in optimization, and an artificial neural network (ANNs) model can be constructed. In this chapter, a prediction model used for optimum reinforced concrete (RC) members is presented. After the literature survey of machine learning and artificial intelligence methods used in structural optimization are summarized, ANNs are briefly explained. As examples, different RC problems are optimized, and ANNs models are developed for these examples. The results show that the prediction models may be a great source in the decision of design engineers in practical application.
Melda Yücel; Sinan Melih Nigdeli; Aylin Ece Kayabekir; Gebrail Bekdaş. Optimization and Artificial Neural Network Models for Reinforced Concrete Members. Springer Tracts in Nature-Inspired Computing 2021, 181 -199.
AMA StyleMelda Yücel, Sinan Melih Nigdeli, Aylin Ece Kayabekir, Gebrail Bekdaş. Optimization and Artificial Neural Network Models for Reinforced Concrete Members. Springer Tracts in Nature-Inspired Computing. 2021; ():181-199.
Chicago/Turabian StyleMelda Yücel; Sinan Melih Nigdeli; Aylin Ece Kayabekir; Gebrail Bekdaş. 2021. "Optimization and Artificial Neural Network Models for Reinforced Concrete Members." Springer Tracts in Nature-Inspired Computing , no. : 181-199.
Plasticity is the significant integrated property of clay-water relationship that can be initially associated with the consistency which is an outstanding term used especially in cohesive soils to describe the geotechnical behavior characteristics depending upon the change of water content. In the context of this study, the consistency characteristics of plastic clays are investigated based on the analyses conducted with both applications of regression and artificial intelligence methods. In order to acquire an actual input mesh, a domain-specific dataset has been created with the evaluation of 350 soil investigation reports containing a huge number of consistency tests, including the districts located on the European side of Istanbul, Turkey. The results of the conducted laboratory tests are recorded for very high and high plastic clays which are dominantly situated in the southwest regions of Istanbul. Regression analyses and artificial intelligence techniques have been performed with a frequently used software to query the attainment of the values of plastic limit and plasticity index directly from only liquid limit test results. The main aim to acquire a direct relationship between the liquid limit versus plasticity index value is to reduce the dependency of the consistency limit tests to the operators’ experience and also the physical condition of the experimental application environment. As a result of the analyses carried out for this purpose, equations with sufficient reliability, which are applied to obtain the direct plasticity index, were acquired. This condition enables to eliminate the application of the tests of the plastic limit. At the same time, the concurrency of the technique used in obtaining the related equations was questioned comparatively by using regression analysis and artificial intelligence applications. Consequently, discussions are made with well-known studies of literature to validate the applicability of the obtained relationships.
Zülal Akbay Arama; Melda Yucel; Muhammed Selahaddin Akin; Ilknur Dalyan. A comparative study on the application of artificial intelligence networks versus regression analysis for the prediction of clay plasticity. Arabian Journal of Geosciences 2021, 14, 1 -16.
AMA StyleZülal Akbay Arama, Melda Yucel, Muhammed Selahaddin Akin, Ilknur Dalyan. A comparative study on the application of artificial intelligence networks versus regression analysis for the prediction of clay plasticity. Arabian Journal of Geosciences. 2021; 14 (7):1-16.
Chicago/Turabian StyleZülal Akbay Arama; Melda Yucel; Muhammed Selahaddin Akin; Ilknur Dalyan. 2021. "A comparative study on the application of artificial intelligence networks versus regression analysis for the prediction of clay plasticity." Arabian Journal of Geosciences 14, no. 7: 1-16.
Tuned mass dampers (TMDs) are used to damp vibration of mechanical systems. TMDs are also used on structures to reduce the effects of strong forces such as winds and earthquakes. For the efficiency of TMD, optimization of TMD parameters is needed. Several classical formulations were proposed, but metaheuristic methods are generally used to find the best result. In addition, the metaheuristic based optimum results are used in machine learning of artificial intelligence-based models like artificial neural networks (ANN). These ANN models are also used in development of tuning equation via curve fitting. The classical and ANN-based formulations were found according to frequency domain responses. In the present study, the classical and ANN-based formulations were evaluated by comparing on time-history responses of seismic structure. In comparison, near-fault ground motion records including directivity pulses are used. The ANN based methods have advantages by providing smaller stroke requirement and damping for TMDs.
Melda Yucel; Sinan Melih Nigdeli; Gebrail Bekdaş. Evaluation of artificial neural network-based formulations for tuned mass dampers. Challenge Journal of Structural Mechanics 2021, 7, 17 .
AMA StyleMelda Yucel, Sinan Melih Nigdeli, Gebrail Bekdaş. Evaluation of artificial neural network-based formulations for tuned mass dampers. Challenge Journal of Structural Mechanics. 2021; 7 (1):17.
Chicago/Turabian StyleMelda Yucel; Sinan Melih Nigdeli; Gebrail Bekdaş. 2021. "Evaluation of artificial neural network-based formulations for tuned mass dampers." Challenge Journal of Structural Mechanics 7, no. 1: 17.
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.
Melda Yücel; Aylin Ece Kayabekir; Gebrail Bekdaş; Sinan Melih Nigdeli; Sanghun Kim; Zong Woo Geem. Adaptive-Hybrid Harmony Search Algorithm for Multi-Constrained Optimum Eco-Design of Reinforced Concrete Retaining Walls. Sustainability 2021, 13, 1639 .
AMA StyleMelda Yücel, Aylin Ece Kayabekir, Gebrail Bekdaş, Sinan Melih Nigdeli, Sanghun Kim, Zong Woo Geem. Adaptive-Hybrid Harmony Search Algorithm for Multi-Constrained Optimum Eco-Design of Reinforced Concrete Retaining Walls. Sustainability. 2021; 13 (4):1639.
Chicago/Turabian StyleMelda Yücel; Aylin Ece Kayabekir; Gebrail Bekdaş; Sinan Melih Nigdeli; Sanghun Kim; Zong Woo Geem. 2021. "Adaptive-Hybrid Harmony Search Algorithm for Multi-Constrained Optimum Eco-Design of Reinforced Concrete Retaining Walls." Sustainability 13, no. 4: 1639.
Truss structures are one of the major civil engineering members studied in the optimization research area. In this area, various optimization applications such as topology, size, cost, weight, material usage, etc., can be conducted for different truss structure types. In this scope with the present study, various optimization processes were carried out concerning two different large-scale space trusses to minimize the structural weight. According to this state, three structural models provided via two different truss structures, including 25 bar and 72 bar truss models, were handled for evaluation of six different metaheuristics together with the modification of Lèvy flight for three of the algorithms using swarm intelligence by considering both constant and variable populations, and different ranges for iterations, too. Additionally, the effects of the Lèvy flight function and whether it is successful or not in terms of the target of optimization were also investigated by comparing with some documented studies. In this regard, some statistical calculations were also realized to evaluate the optimization method performance and detection of optimum values for any data stably and successfully. According to the results, the Jaya algorithm can handle the optimization process successfully, including the case, without grouping truss members. The positive effect of Lèvy flight on swarm-based algorithms can be seen especially for the gray wolf algorithm.
Gebrail Bekdaş; Melda Yucel; Sinan Nigdeli. Evaluation of Metaheuristic-Based Methods for Optimization of Truss Structures via Various Algorithms and Lèvy Flight Modification. Buildings 2021, 11, 49 .
AMA StyleGebrail Bekdaş, Melda Yucel, Sinan Nigdeli. Evaluation of Metaheuristic-Based Methods for Optimization of Truss Structures via Various Algorithms and Lèvy Flight Modification. Buildings. 2021; 11 (2):49.
Chicago/Turabian StyleGebrail Bekdaş; Melda Yucel; Sinan Nigdeli. 2021. "Evaluation of Metaheuristic-Based Methods for Optimization of Truss Structures via Various Algorithms and Lèvy Flight Modification." Buildings 11, no. 2: 49.
Total Potential Optimization using Metaheuristic Algorithms (TPO/MA) is an alternative structural analysis method starting with the same principles of the Finite Element Method (FEM). In TPO/MA as in FEM, the structure at hand is divided into finite parts. In these parts, if FEM is to be used, the equilibrium equations are written in a matrix form in local coordinates, then they are combined to give a matrix equation valid for the totality of the structure. The final step is solving this matrix equation to find the displacements in the structure. On the other hand, in TPO/MA, potential energies of the elements are written, then they are summed for the totality of the structure, yielding a functional to be minimized to find the equilibrium position according to the minimum potential energy principle. This minimization gives the displacements of the structure. That is why this method can also be called Finite Element Method with Energy Minimization (FEMEM). FEM is very efficient for linear systems, but for nonlinear systems the matrix obtained depends on material properties, displacements, and loads, i.e. it is not constant and may be ill-conditioned. This makes solutions difficult and sometimes impossible. It has been shown in the literature that TPO/MA can overcome these difficulties much more easily, and can solve problems that cannot be solved by FEM. In this study, TPO/MA is applied to tunnel problems with plane stress properties. Minimization process is applied to several metaheuristic algorithms and hybrid ones, which are then compared with each other as to accuracy and precision.
Yusuf Cengiz Toklu; Gebrail Bekdaş; Aylin Ece Kayabekir; Sinan Melih Nigdeli; Melda Yucel. Total Potential Optimization Using Hybrid Metaheuristics: A Tunnel Problem Solved via Plane Stress Members. Developments in Advanced Control and Intelligent Automation for Complex Systems 2020, 221 -236.
AMA StyleYusuf Cengiz Toklu, Gebrail Bekdaş, Aylin Ece Kayabekir, Sinan Melih Nigdeli, Melda Yucel. Total Potential Optimization Using Hybrid Metaheuristics: A Tunnel Problem Solved via Plane Stress Members. Developments in Advanced Control and Intelligent Automation for Complex Systems. 2020; ():221-236.
Chicago/Turabian StyleYusuf Cengiz Toklu; Gebrail Bekdaş; Aylin Ece Kayabekir; Sinan Melih Nigdeli; Melda Yucel. 2020. "Total Potential Optimization Using Hybrid Metaheuristics: A Tunnel Problem Solved via Plane Stress Members." Developments in Advanced Control and Intelligent Automation for Complex Systems , no. : 221-236.
In recent years, artificial intelligence (AI) and one of the sub-fields for it, which is machine learning, became significant in numerous study disciplines. During the determination of some results or finding of required parameters/information, these technologies provides many advantages and easiness for the usage of people such as researchers, designers, engineers, inventors and each kind of person dealt with them in terms of ease of use, saving of time, besides cost and efficiency for effort. In this chapter, a comprehensive research was presented intended for AI and machine learning technology together with their historical evaluation and applications. Also, a review of frequently-used machine learning techniques were imparted. As addition to these, many applications respect to some fields and also several studies of the structural engineering area were explained detailly. Furthermore, prediction studies carried out in structural engineering were demonstrated with all stages. In this regard, all of studies performed with the usage of different machine learning techniques were given in six sub-headings. As it can be understood from many developments, AI and machine learning technologies and application of them are pretty significant in terms of providing of beneficial situations and the usage of them will be more substantial and remarkable day by day.
Melda Yucel; Sinan Melih Nigdeli; Gebrail Bekdaş. Artificial Intelligence and Machine Learning with Reflection for Structural Engineering: A Review. Developments in Advanced Control and Intelligent Automation for Complex Systems 2020, 23 -72.
AMA StyleMelda Yucel, Sinan Melih Nigdeli, Gebrail Bekdaş. Artificial Intelligence and Machine Learning with Reflection for Structural Engineering: A Review. Developments in Advanced Control and Intelligent Automation for Complex Systems. 2020; ():23-72.
Chicago/Turabian StyleMelda Yucel; Sinan Melih Nigdeli; Gebrail Bekdaş. 2020. "Artificial Intelligence and Machine Learning with Reflection for Structural Engineering: A Review." Developments in Advanced Control and Intelligent Automation for Complex Systems , no. : 23-72.
Consistency limits are simple and precious soil parameters that are directly representing the mineralogical and physical properties and indirectly used to estimate the strength and rigidity characteristics of fine-grained soils. The consistency tests are assumed as the easiest and the basic tests of geotechnical engineering and the application details of the tests are depending on the experience of the operator and the environmental conditions. Therefore, the reliability and correctness of the tests are still in the concern of geotechnical studies. Besides, the development of information and computer technologies also present alternative solution perspectives for the attainment of geotechnical properties. In this connection, the regression analysis and artificial neural network applications become the most preferred techniques to estimate the consistency limits with the use of only a variable. In this study, it is aimed to obtain the consistency properties of high plastic clays that are procured from the site investigation reports conducted for Bakırköy district (Istanbul, Turkey) depending on only the evaluation of liquid limit test results. A database is arranged with the use of approximately 450 consistency limit tests. Linear and multi-linear regression analyses and artificial neural network applications were conducted with the use of different variants such as depth and fine content to determine the plastic limit and the plasticity index value. As a result, comparisons of the applied techniques are done to query the appropriateness of the methods and to find the approximation rate to the actual value. Consequently, the success of the applied techniques was tried to be shown with the developed equation set.
Zülal Akbay Arama; Melda Yücel; Muhammed Selahaddin Akın; Said Enes Nuray; Oğuzhan Alten. Prediction of Soil Plasticity Index with the Use of Regression Analysis and Artificial Neural Networks: A Specific Case for Bakırköy District. Advances in Intelligent Systems and Computing 2020, 281 -293.
AMA StyleZülal Akbay Arama, Melda Yücel, Muhammed Selahaddin Akın, Said Enes Nuray, Oğuzhan Alten. Prediction of Soil Plasticity Index with the Use of Regression Analysis and Artificial Neural Networks: A Specific Case for Bakırköy District. Advances in Intelligent Systems and Computing. 2020; ():281-293.
Chicago/Turabian StyleZülal Akbay Arama; Melda Yücel; Muhammed Selahaddin Akın; Said Enes Nuray; Oğuzhan Alten. 2020. "Prediction of Soil Plasticity Index with the Use of Regression Analysis and Artificial Neural Networks: A Specific Case for Bakırköy District." Advances in Intelligent Systems and Computing , no. : 281-293.
In this study, a structural engineering benchmark problem from classic literature, which is 3-bars truss model, was handled intended for determination of optimum areas of bars and the minimum volume of the structure, and harmony search (HS) metaheuristic algorithm was utilized to realize this. In here, the main purpose of the study is the prediction of these values directly by using an intelligent and learnt model. For this reason, after optimization process, training of intelligent machine learning algorithm, which is selected as ANN, was performed and following, the prediction operation was carried out with this developed model by considering optimization results. Finally, owing to the development of prediction model and to be successful about accurate and reliable predict, a test model also was generated to evaluate that main model is used replacing of the optimization process and to find the optimum results belonging any structure model. In this regard, the main prediction model has a capability that it can predict design variables as optimal, and minimum volume, directly.
Melda Yücel; Gebrail Bekdaş; Sinan Melih Nigdeli. Prediction of Optimum 3-Bar Truss Model Parameters with an ANN Model. Advances in Intelligent Systems and Computing 2020, 317 -324.
AMA StyleMelda Yücel, Gebrail Bekdaş, Sinan Melih Nigdeli. Prediction of Optimum 3-Bar Truss Model Parameters with an ANN Model. Advances in Intelligent Systems and Computing. 2020; ():317-324.
Chicago/Turabian StyleMelda Yücel; Gebrail Bekdaş; Sinan Melih Nigdeli. 2020. "Prediction of Optimum 3-Bar Truss Model Parameters with an ANN Model." Advances in Intelligent Systems and Computing , no. : 317-324.
In all kinds of site investigation reports prepared to acquire the current situation of the project site, it is a common fact to perform the consistency tests which are specialized as Atterberg limit tests. Consistency can be defined as an important term, especially for fine-grained soils, to appoint the current state of the water content of soil formation in the field. Based on the ease and cost-effectiveness of the Atterberg tests, it has become a traditional solution to determine the fundamental design properties such as the rigidity and strength of the soil formation with the use of empirical approaches that are developed according to them. In this context, “compaction” can be an interesting term to investigate the appropriateness of determination of special characteristics of the phenomenon such as the optimum water content and the maximum dry unit weight with the development of a new perspective based on a simplest experimental process formed with only the evaluation of water content. Because it is a complicated and time-consuming process to apply the compaction test beginning of the sample preparation step to the ultimate evaluation step. Hence, in this paper, an integrated study is performed for highly plastic clays to acquire the consistency and the compaction properties together with a direct relationship. A huge database was prepared according to the data’s given in the well-accepted literature sources by the transmission of liquid limit and plastic limit test results conducted for only the high plastic clays. Besides, simple equations are tried to be obtained to calculate the plasticity index and approximations are proposed to find the maximum dry unit weight and the optimum water content of the soil, respectively. As a result, the applicability of both the regression analysis and the artificial neural network studies to the attainment process of both consistency characteristics and compaction problem were compared with each other to procure a reliable determination process.
Zülal Akbay Arama; Hazal Berrak Gençdal; Said Enes Nuray; Melda Yücel. The Applicability of Regression Analysis and Artificial Neural Networks to the Prediction Process of Consistency and Compaction Properties of High Plastic Clays. Advances in Intelligent Systems and Computing 2020, 295 -305.
AMA StyleZülal Akbay Arama, Hazal Berrak Gençdal, Said Enes Nuray, Melda Yücel. The Applicability of Regression Analysis and Artificial Neural Networks to the Prediction Process of Consistency and Compaction Properties of High Plastic Clays. Advances in Intelligent Systems and Computing. 2020; ():295-305.
Chicago/Turabian StyleZülal Akbay Arama; Hazal Berrak Gençdal; Said Enes Nuray; Melda Yücel. 2020. "The Applicability of Regression Analysis and Artificial Neural Networks to the Prediction Process of Consistency and Compaction Properties of High Plastic Clays." Advances in Intelligent Systems and Computing , no. : 295-305.
In addition to classical methods in structural analysis, the exact solution of the deformed shape of the structure is by using a metaheuristic method. According to the theory of total potential energy minimization, the static condition of the deformed shape of structures can be found directly by assigning the coordinates of the deformed shape of the structure and finding the case with the minimum potential energy. In that case, the analysis process is an optimization process, and metaheuristics are effective in this process. The total potential optimization using metaheuristic algorithms (TPO/MA) is an effective approach for several including plane-stress members. A cantilever beam is presented in the study, and it is solved via plane-stress members using TPO/MA. The problem is presented via two meshing options. For the frequent meshing, a hybrid algorithm of Jaya Algorithm (JA) using student phase of Teaching–Learning-Based Optimization (TLBO) is presented. TPO/MA is effective to find similar results with the finite element method.
Yusuf Cengiz Toklu; Gebrail Bekdaş; Aylin Ece Kayabekir; Sinan Melih Nigdeli; Melda Yücel. Total Potential Optimization Using Metaheuristics: Analysis of Cantilever Beam via Plane-Stress Members. Advances in Intelligent Systems and Computing 2020, 127 -138.
AMA StyleYusuf Cengiz Toklu, Gebrail Bekdaş, Aylin Ece Kayabekir, Sinan Melih Nigdeli, Melda Yücel. Total Potential Optimization Using Metaheuristics: Analysis of Cantilever Beam via Plane-Stress Members. Advances in Intelligent Systems and Computing. 2020; ():127-138.
Chicago/Turabian StyleYusuf Cengiz Toklu; Gebrail Bekdaş; Aylin Ece Kayabekir; Sinan Melih Nigdeli; Melda Yücel. 2020. "Total Potential Optimization Using Metaheuristics: Analysis of Cantilever Beam via Plane-Stress Members." Advances in Intelligent Systems and Computing , no. : 127-138.
Tuned mass dampers (TMDs) were used to damp vibration of mechanical systems. TMDs were also used on structures to reduce the effects of strong winds and earthquakes. For the efficiency of TMD, the optimization of TMD parameters is needed. Several classical formulations were proposed, but metaheuristic methods are generally used to find the best result. Also, the metaheuristic-based optimum results are used in machine learning of artificial intelligence models like artificial neural networks (ANN). These ANN models are also used in the development of tuning equation via curve fitting. The classical and ANN-based formulations were found according to frequency-domain responses. In the present study, the classical and ANN-based formulations were compared on time-history responses of seismic structures. In comparison, near-fault ground motion records including directivity pulses are used. The ANN-based methods have advantages by providing smaller stroke requirement and damping for TMDs.
Melda Yücel; Sinan Melih Nigdeli; Gebrail Bekdaş. The Comparison of Classical and Artificial Neural Network-Based Formulations for Tuned Mass Damper Optimization. Advances in Intelligent Systems and Computing 2020, 93 -109.
AMA StyleMelda Yücel, Sinan Melih Nigdeli, Gebrail Bekdaş. The Comparison of Classical and Artificial Neural Network-Based Formulations for Tuned Mass Damper Optimization. Advances in Intelligent Systems and Computing. 2020; ():93-109.
Chicago/Turabian StyleMelda Yücel; Sinan Melih Nigdeli; Gebrail Bekdaş. 2020. "The Comparison of Classical and Artificial Neural Network-Based Formulations for Tuned Mass Damper Optimization." Advances in Intelligent Systems and Computing , no. : 93-109.
As an alternative structural analysis method, total potential optimization using metaheuristic algorithms (TPO/MAs) has been demonstrated to be very effective, especially in nonlinear cases, for trusses and trusslike systems such as tensegric structures and cable networks. A recent study showed that TPO/MAs also can be applied successfully to plane-stress problems with linear constitutive equations. The present study enlarged the application area of TPO/MAs to the analysis of plates for plane-stress cases with nonlinear stress–strain equations. A relevant formulation was developed and used to solve five problems. Because TPO/MAs can be applied by using different metaheuristic algorithms, a number of them, some hybrid, were used, and they were compared among themselves and with finite-element solutions. The final example was a tunnel problem with 150 nodes, i.e., with 265 unknowns. This problem also was solved via hybrid algorithms. Several proposals for future research are listed.
Yusuf Cengiz Toklu; Aylin Ece Kayabekir; Gebrail Bekdaş; Sinan Melih Nigdeli; Melda Yücel. Analysis of Plane-Stress Systems via Total Potential Optimization Method Considering Nonlinear Behavior. Journal of Structural Engineering 2020, 146, 04020249 .
AMA StyleYusuf Cengiz Toklu, Aylin Ece Kayabekir, Gebrail Bekdaş, Sinan Melih Nigdeli, Melda Yücel. Analysis of Plane-Stress Systems via Total Potential Optimization Method Considering Nonlinear Behavior. Journal of Structural Engineering. 2020; 146 (11):04020249.
Chicago/Turabian StyleYusuf Cengiz Toklu; Aylin Ece Kayabekir; Gebrail Bekdaş; Sinan Melih Nigdeli; Melda Yücel. 2020. "Analysis of Plane-Stress Systems via Total Potential Optimization Method Considering Nonlinear Behavior." Journal of Structural Engineering 146, no. 11: 04020249.
Design engineers may find various options of metaheuristic method in optimization of their problems. Because of the randomization nature of metaheuristic methods, solutions may trap to non-optimum solutions which are just optimums in a limited part of the selected range of the design variables. Generally, metaheuristics use several options to prevent this situation, but the same optimization process may solve different performances in every run of the process. Due to that, a comparative study by using ten different algorithms was done in this study. The optimization problem is the cost minimization of an L-shaped reinforced concrete (RC) retaining wall. The evaluation is done by conducting 30 multiple cycles of optimization, and comparing minimum cost, average cost and standard deviation values.
Aylin Ece Kayabekir; Melda Yücel; Gebrail Bekdaş; Sinan Melih Nigdeli. Comparative study of optimum cost design of reinforced concrete retaining wall via metaheuristics. Challenge Journal of Concrete Research Letters 2020, 11, 75 .
AMA StyleAylin Ece Kayabekir, Melda Yücel, Gebrail Bekdaş, Sinan Melih Nigdeli. Comparative study of optimum cost design of reinforced concrete retaining wall via metaheuristics. Challenge Journal of Concrete Research Letters. 2020; 11 (3):75.
Chicago/Turabian StyleAylin Ece Kayabekir; Melda Yücel; Gebrail Bekdaş; Sinan Melih Nigdeli. 2020. "Comparative study of optimum cost design of reinforced concrete retaining wall via metaheuristics." Challenge Journal of Concrete Research Letters 11, no. 3: 75.
Since a long time, metaheuristic algorithms are benefited to detect the best results for any optimization problem. Furthermore, these methods are used to prevent of time, effort and cost losses, while they are performing the optimization process. Hence, in this study, a cantilever beam model, which is one of the structural optimization problem from civil engineering area, was handled with the aim of minimization of the total weight by find the optimum section values consisting of hollow section depths and widths. For this reason, three different methods including the algorithms that artificial bee colony (ABC), bat (BA), and a modified bat (MBA) combining of BA with Lévy flight, were operated. Additionally, several applications previously carried out for this model, were presented in order to compare of optimization results (minimum objective function with optimum design variable values), and success of proposed algorithm was showed with statistical results and optimization parameter values.
Melda Yucel; Gebrai̇l Bekdaş; Si̇nan Meli̇h Ni̇gdeli̇. Minimizing the Weight of Cantilever Beam via Metaheuristic Methods by Using Different Population-Iteration Combinations. WSEAS TRANSACTIONS ON COMPUTERS 2020, 19, 69 -77.
AMA StyleMelda Yucel, Gebrai̇l Bekdaş, Si̇nan Meli̇h Ni̇gdeli̇. Minimizing the Weight of Cantilever Beam via Metaheuristic Methods by Using Different Population-Iteration Combinations. WSEAS TRANSACTIONS ON COMPUTERS. 2020; 19 ():69-77.
Chicago/Turabian StyleMelda Yucel; Gebrai̇l Bekdaş; Si̇nan Meli̇h Ni̇gdeli̇. 2020. "Minimizing the Weight of Cantilever Beam via Metaheuristic Methods by Using Different Population-Iteration Combinations." WSEAS TRANSACTIONS ON COMPUTERS 19, no. : 69-77.
By finding the minimum total potential energy of a structural system with a defined degree of freedoms assigned as design variables, it is possible to find the equilibrium condition of the deformed system. This method, called total potential optimization using metaheuristic algorithms (TPO/MA), has been verified on truss and truss-like structures, such as cable systems and tensegric structures. Harmony Search (HS) algorithm methods perfectly found the analysis results of the previous structure types. In this study, TPO/MA is presented for analysis of plates for plane stress members to solve general types of problems. Due to the complex nature of the system, a novel hybrid Harmony Search (HHS) approach was proposed. HHS is the hybridization of local search phases of HS and the global search phase of the Flower Pollination Algorithm (FPA). The results found via HHS were verified with the finite element method (FEM). When compared with classical HS, HHS provides smaller total potential energy values, and needs less iterations than other new generation metaheuristic algorithms.
Aylin Ece Kayabekir; Yusuf Cengiz Toklu; Gebrail Bekdaş; Sinan Melih Nigdeli; Melda Yücel; Zong Woo Geem. A Novel Hybrid Harmony Search Approach for the Analysis of Plane Stress Systems via Total Potential Optimization. Applied Sciences 2020, 10, 2301 .
AMA StyleAylin Ece Kayabekir, Yusuf Cengiz Toklu, Gebrail Bekdaş, Sinan Melih Nigdeli, Melda Yücel, Zong Woo Geem. A Novel Hybrid Harmony Search Approach for the Analysis of Plane Stress Systems via Total Potential Optimization. Applied Sciences. 2020; 10 (7):2301.
Chicago/Turabian StyleAylin Ece Kayabekir; Yusuf Cengiz Toklu; Gebrail Bekdaş; Sinan Melih Nigdeli; Melda Yücel; Zong Woo Geem. 2020. "A Novel Hybrid Harmony Search Approach for the Analysis of Plane Stress Systems via Total Potential Optimization." Applied Sciences 10, no. 7: 2301.
This chapter reveals the advantages of artificial neural networks (ANNs) by means of prediction success and effects on solutions for various problems. With this aim, initially, multilayer ANNs and their structural properties are explained. Then, feed-forward ANNs and a type of training algorithm called back-propagation, which was benefited for these type networks, are presented. Different structural design problems from civil engineering are optimized, and handled intended for obtaining prediction results thanks to usage of ANNs.
Melda Yucel; Sinan Melih Nigdeli; Gebrail Bekdaş. Artificial Neural Networks (ANNs) and Solution of Civil Engineering Problems. Advances in Computational Intelligence and Robotics 2020, 13 -38.
AMA StyleMelda Yucel, Sinan Melih Nigdeli, Gebrail Bekdaş. Artificial Neural Networks (ANNs) and Solution of Civil Engineering Problems. Advances in Computational Intelligence and Robotics. 2020; ():13-38.
Chicago/Turabian StyleMelda Yucel; Sinan Melih Nigdeli; Gebrail Bekdaş. 2020. "Artificial Neural Networks (ANNs) and Solution of Civil Engineering Problems." Advances in Computational Intelligence and Robotics , no. : 13-38.
In this chapter, an application for demonstrating prediction success and error performance of ensemble methods combined via various machine learning and artificial intelligence algorithms and techniques was performed. For this reason, two single method was selected and combination models with Bagging ensemble was constructed and operated in the direction optimum design of concrete beams covering with carbon fiber reinforced polymers (CFRP) by ensuring the determination of design variables. The first part was optimization problem and method composing from an advanced bio-inspired metaheuristic called Jaya algorithm. Machine learning prediction methods and their operation logics were detailed. Performance evaluations and error indicators were represented for prediction models. In the last part, performed prediction applications and created models were introduced. Also, obtained prediction success for main model generated with optimization results was utilized to determine the optimum predictions about test models.
Melda Yucel; Aylin Ece Kayabekir; Sinan Melih Nigdeli; Gebrail Bekdaş. Optimum Design of Carbon Fiber-Reinforced Polymer (CFRP) Beams for Shear Capacity via Machine Learning Methods. Genetic Algorithms and Applications for Stock Trading Optimization 2020, 84 -102.
AMA StyleMelda Yucel, Aylin Ece Kayabekir, Sinan Melih Nigdeli, Gebrail Bekdaş. Optimum Design of Carbon Fiber-Reinforced Polymer (CFRP) Beams for Shear Capacity via Machine Learning Methods. Genetic Algorithms and Applications for Stock Trading Optimization. 2020; ():84-102.
Chicago/Turabian StyleMelda Yucel; Aylin Ece Kayabekir; Sinan Melih Nigdeli; Gebrail Bekdaş. 2020. "Optimum Design of Carbon Fiber-Reinforced Polymer (CFRP) Beams for Shear Capacity via Machine Learning Methods." Genetic Algorithms and Applications for Stock Trading Optimization , no. : 84-102.
This chapter presents a summary review of development of Artificial Intelligence (AI). Definitions of AI are given with basic features. The development process of AI and machine learning is presented. The developments of applications from the past to today are mentioned and use of AI in different categories is given. Prediction applications using artificial neural network are given for engineering applications. Usage of AI methods to predict optimum results is the current trend and it will be more important in the future.
Melda Yucel; Gebrail Bekdaş; Sinan Melih Nigdeli. Review and Applications of Machine Learning and Artificial Intelligence in Engineering. Advances in Computational Intelligence and Robotics 2020, 1 -12.
AMA StyleMelda Yucel, Gebrail Bekdaş, Sinan Melih Nigdeli. Review and Applications of Machine Learning and Artificial Intelligence in Engineering. Advances in Computational Intelligence and Robotics. 2020; ():1-12.
Chicago/Turabian StyleMelda Yucel; Gebrail Bekdaş; Sinan Melih Nigdeli. 2020. "Review and Applications of Machine Learning and Artificial Intelligence in Engineering." Advances in Computational Intelligence and Robotics , no. : 1-12.