Prediction of force reduction factor (R) of prefabricated industrial buildings using neural networks

Arslan, Musa Hakan; Ceylan, Murat; Kaltakci, Yaspr M.; Özbay, Yüksel; Gülay, F. Gülten

doi:10.12989/sem.2007.27.2.117

Cited by 27 publications

(7 citation statements)

References 7 publications

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“…Here S 1 = IO, S 2 = LS, S 3 = CP and S 4 = C. In the study, the total of 19 different structural parameters described above are used. Arslan [16,20] and Arslan et al [17] studied different structural parameters for frame RC and prefabricated industrial buildings. In the mentioned studies, basic parameters were considered as the main reason for damages according to the researchers.…”

Section: Fig 2 Two Of the Rc Buildings Analyzed In The Studymentioning

confidence: 99%

“…The ANN is a type of artificial intelligence application that has been implemented by engineers to carry out specialized design tasks so far. ANNs have been widely used for the prediction of various structural quantities [12][13][14][15][16][17] structural damage diagnosis and detections [18,19], evaluation of RC buildings performance [20], active response control of offshore structures [21,22] and static model identification of an FRP deck [23] etc. ANNs thus have been a powerful tool in solution of various structural engineering problems.…”

Section: Introductionmentioning

confidence: 99%

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An Ann Approaches on Estimating Earthquake Performances of Existing Rc Buildings

Arslan¹,

Ceylan²,

Koyuncu³

2012

NNW

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Genetic algorithms (GAs) are stochastic methods that are widely used in search and optimization. The breeding process is the main driving mechanism for GAs that leads the way to find the global optimum. And the initial phase of the breeding process starts with parent selection. The selection utilized in a GA is effective on the convergence speed of the algorithm. A GA can use different selection mechanisms for choosing parents from the population and in many applications the process generally depends on the fitness values of the individuals. Artificial neural networks (ANNs) are used to decide the appropriate parents by the new hybrid algorithm proposed in this study. And the use of neural networks aims to produce better offspring during the GA search. The neural network utilized in this algorithm tries to learn the structural patterns and correlations that enable two parents to produce high-fit offspring. In the breeding process, the first parent is selected based on the fitness value as usual. Then it is the neural network that decides the appropriate mate for the first parent chosen. Hence, the selection mechanism is not solely dependent on the fitness values in this study. The algorithm is tested with seven benchmark functions. It is observed from results of these tests that the new selection method leads genetic algorithm to converge faster.

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Section: Fig 2 Two Of the Rc Buildings Analyzed In The Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Ann Approaches on Estimating Earthquake Performances of Existing Rc Buildings

Arslan¹,

Ceylan²,

Koyuncu³

2012

NNW

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show abstract

“…The relevant literature includes a number of studies on the use of ANN and IPT in civil engineering problems. For instance, Arslan et al [2] estimated reduction factors for prefabricated single bay-single story reinforced concrete (RC) buildings using ANN with 81% accuracy. Arslan [4] determined the torsional strength of RC beams using various ANN algorithms and showed that the ANNs were better able to predict the torsional strength of the beams than conventional and code approaches.…”

Section: Introductionmentioning

confidence: 99%

Statistical Feature Extraction Based on an Ann Approach for Estimating the Compressive Strength of Concrete

Doğan¹,

Arslan²,

Ceylan³

2015

NNW

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Applications of artificial intelligence in engineering disciplines have become widespread and have provided alternative solutions to engineering problems. Image processing technology (IPT) and artificial neural networks (ANNs) are types of artificial intelligence methods. However, IPT and ANN have been used together in extremely few studies. In this study, these two methods were used to determine the compressive strength of concrete, a complex material whose mechanical features are difficult to predict. Sixty cube-shaped specimens were manufactured, and images of specific features of the specimens were taken before they were tested to determine their compressive strengths. An ANN model was constituted as a result of the process of digitizing the images. In this way, the two different artificial intelligence methods were used together to carry out the analysis. The compressive strength values of the concrete obtained via analytical modeling were compared with the test results. The results of the comparison (R 2 = 0.9837−0.9961) indicate that the combination of these two artificial intelligence methods is highly capable of predicting the compressive strengths of the specimens. The model's predictive capability was also evaluated in terms of several statistical parameters using a set of statistical methods during the digitization of the images constituting the artificial neural network.

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“…ANN have been successfully applied to a number of areas of structural engineering, an important branch of civil engineering. In recent literature, structural analysis and design, structural dynamics and control, structural damage assessment, and the structural behavior and properties of concrete materials, such as strength and constitutive modeling, are good examples for the application of ANN [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Shear strength predicting of FRP-strengthened RC beams by using artificial neural networks

Yavuz

Arslan

Baykan

2014

Science and Engineering of Composite Materials

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In this study, the efficiency of artificial neural networks (ANN) in predicting the shear strength of reinforced concrete (RC) beams, strengthened by means of externally bonded fiber-reinforced polymers (FRP), is explored. Experimental data of 96 rectangular RC beams from an existing database in the literature were used to develop the ANN model. Eight different input parameters affecting the shear strength were selected for creating the ANN structure. Each parameter was arranged in an input vector and a corresponding output vector that includes the shear strength of the RC beam. For all outputs, the ANN model was trained and tested using a three-layered back-propagation method. The initial performance of back-propagation was evaluated and discussed. In addition, the accuracy of well-known building codes in predicting the shear strength of FRP-strengthened RC beams was also examined, in a comparable way, using same test data. The study shows that the ANN model gives reasonable predictions of the ultimate shear strength of RC beams (R 2 ≈0.93). Moreover, the study concludes that the ANN model predicts the shear strength of FRP-strengthened RC beams better than existing building code approaches.

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Prediction of force reduction factor (R) of prefabricated industrial buildings using neural networks

Cited by 27 publications

References 7 publications

An Ann Approaches on Estimating Earthquake Performances of Existing Rc Buildings

An Ann Approaches on Estimating Earthquake Performances of Existing Rc Buildings

Statistical Feature Extraction Based on an Ann Approach for Estimating the Compressive Strength of Concrete

Shear strength predicting of FRP-strengthened RC beams by using artificial neural networks

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