A new application area of ANN and ANFIS: determination of earthquake load reduction factor of prefabricated industrial buildings

Ceylan, Murat; Arslan, Musa Hakan; Ceylan, Rahime; Kaltakcı, M. Yaşar; Özbay, Yüksel

doi:10.1080/10286600802506726

Cited by 22 publications

(10 citation statements)

References 19 publications

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“…Mollaioli and Bruno [32] developed constant ductility spectra for SDOF and MDOF systems. Ceylan et al [33] estimated the strength reduction factor for prefabricated industrial structures having a single story, one and two bays. Ganjavi and Hao [34] studied the seismic response of linear and nonlinear MDOF systems subjected to a group of earthquakes recorded on alluvium and soft soils, considering different shear strength and stiffness distribution patterns.…”

Section: Literature Reviewmentioning

confidence: 99%

Strength or force reduction factors for steel buildings: MDOF vs SDOF systems

Reyes‐Salazar¹,

Llanes-Tizoc²,

Bojórquez³

et al. 2017

J. vibroeng.

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The nonlinear seismic responses of steel buildings with perimeter moment resisting frames (MRF), modeled as complex 3D MDOF systems, are calculated and the ductility demands, ductility reduction factors and the force reduction factors (), are studied. Equivalent 3D models with spatial MRF, two-dimensional models, and equivalent SDOF systems (SD), are also considered. Results indicate that the global and local force reduction factors significantly vary from one structural representation to another and that they are much larger for the SD models. One of the reasons for this is that, although there is equivalence between the SD and MDOF models, the dissipated energy and the number of incursions in the inelastic range are significantly larger for the SD models. In addition, while for the SD models total plasticization occurs, for the SAC and EQ models, even for significant yielding, plastic hinges are developed only in a relatively small number of structural members; therefore, using the factors of the SD models may be conservative. According to the results obtained in this research for the more realistic representation of the steel building (3D), the value of 8 suggested in many codes for the factor for ductile MRF cannot be justified. It is only justified for the SD systems. The implication of this is that non-conservative designs may be obtained if so large value is used. More transparence is needed in the codes regarding the magnitude and the components involved in the factor.

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Section: Literature Reviewmentioning

confidence: 99%

Strength or force reduction factors for steel buildings: MDOF vs SDOF systems

Reyes‐Salazar¹,

Llanes-Tizoc²,

Bojórquez³

et al. 2017

J. vibroeng.

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“…An ANN can examine examples of events, make generalizations about the related events, collect information and make decisions about new examples that it has never seen using the information it has learned. In general, ANN is successful in operations such as model selection and classification, function prediction, finding the best value and data classification [10,18]. ANN has been used to solve many complex problems, particularly in the field of engineering, but has found a limited number of applications in architectural science [13].…”

Section: Artificial Neural Network (Ann)mentioning

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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“…It was observed that the neural network could detect the amount of imposed damage with high accuracy. Two different neural networks, the adaptive neuro-fuzzy inference system (ANFIS) and the three-layered artificial neural network (TL-ANN) model, were used to estimate the earthquake load reduction factor for industrial structures by Ceylan et al [6]. They showed that the ANFIS model was more successfully than the TL-ANN model.…”

Section: Introductionmentioning

confidence: 99%

Classification of Earthquake-Induced Damage forR/CSlab Column Frames Using Multiclass SVM and Its Combination with MLP Neural Network

Kia

Şensoy

2014

Mathematical Problems in Engineering

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Nonlinear time history analysis (NTHA) is an important engineering method in order to evaluate the seismic vulnerability of buildings under earthquake loads. However, it is time consuming and requires complex calculations and a high memory machine. In this study, two networks were used for damage classification: multiclass support vector machine (M-SVM) and combination of multilayer perceptron neural network with M-SVM (MM-SVM). In order to collect data, three frames ofR/Cslab column frame buildings with wide beams in slab were considered. For NTHA, twenty different ground motion records were selected and scaled to ten different levels of peak ground acceleration (PGA). Thus, 600 obtained data from the numerical simulations were applied to M-SVM and MM-SVM in order to predict the global damage classification of samples based on park and Ang damage index. Amongst the four different kernel tricks, the Gaussian function was determined as an efficient kernel trick using the maximum total accuracy method of test data. By comparing the obtained results from M-SVM and MM-SVM, the total classification accuracy of MM-SVM is more than M-SVM and it is accurate and reliable for global damage classification ofR/Cslab column frames. Furthermore, the proposed combined model is able to classify the classes with low members.

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A new application area of ANN and ANFIS: determination of earthquake load reduction factor of prefabricated industrial buildings

Cited by 22 publications

References 19 publications

Strength or force reduction factors for steel buildings: MDOF vs SDOF systems

Strength or force reduction factors for steel buildings: MDOF vs SDOF systems

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

Classification of Earthquake-Induced Damage forR/CSlab Column Frames Using Multiclass SVM and Its Combination with MLP Neural Network

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