Neural network based model for seismic assessment of existing RC buildings

Çağlar, Naci; Garip, Zehra Şule

doi:10.12989/cac.2013.12.2.229

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…A very important part of the present study was the selection of parameters for the input vectors of the used MLP networks. Generally, in problems that concern the prediction of seismic damage to buildings, the input vectors of the MLP networks must contain seismic and structural parameters (see, e.g., [23,28,29,57,58]).…”

Section: Selection Of Parameters For the Input Vectorsmentioning

confidence: 99%

Rapid Prediction of Seismic Incident Angle’s Influence on the Damage Level of RC Buildings Using Artificial Neural Networks

Morfidis

Kostinakis

2022

Applied Sciences

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The angle of seismic excitation is a significant factor in the seismic response of RC buildings. The procedure required for the calculation of the angle for which the potential seismic damage is maximized (critical angle) contains multiple nonlinear time history analyses, each using different angles of incidence. Moreover, the seismic codes recommend the application of more than one accelerogram for the evaluation of seismic response; thus, the whole procedure becomes time consuming. Herein, a method to reduce the time required for the estimation of the critical angle based on multilayered feedforward perceptron neural networks is proposed. The basic idea is the detection of cases in which the critical angle increases the class of seismic damage compared to the class that arises from the application of the seismic motion along the buildings’ structural axes. To this end, the problem is expressed and solved as a pattern recognition problem. The ratios of seismic parameters’ values along the two horizontal seismic records’ components, as well as appropriately chosen structural parameters, were used as the inputs of the networks. The results of analyses show that the neural networks can reliably detect the cases in which the calculation of the critical angle is essential.

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Section: Selection Of Parameters For the Input Vectorsmentioning

confidence: 99%

Rapid Prediction of Seismic Incident Angle’s Influence on the Damage Level of RC Buildings Using Artificial Neural Networks

Morfidis

Kostinakis

2022

Applied Sciences

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“…Generally, in problems which concern the prediction of the seismic damage of buildings the input vectors of the MLP networks must contain seismic and structural parameters (see e.g. [23,28,29,50,51]).…”

Section: Selection Of Parameters For the Input Vectorsmentioning

confidence: 99%

Rapid Prediction of Seismic Incident Angle's Influence on the Damage Level of RC Buildings Using Artificial Neural Networks

Morfidis¹,

Kostinakis²

2021

Preprint

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The angle of seismic excitation is a significant factor of the seismic response of RC buildings. The procedure required for the calculation of the angle for which the potential seismic damage is maximized (critical angle) contains multiple nonlinear time history analyses using in each one of them different angles of incidence. Moreover, the seismic codes recommend the application of more than one accelerograms for the evaluation of seismic response. Thus, the whole procedure becomes time consuming. Herein, a method to reduce the time required for the estimation of the critical angle based on Multilayered Feedforward Perceptron Neural Networks is proposed. The basic idea is the detection of cases in which the critical angle increases the class of seismic damage compared to the class which arises from the application of the seismic motion along the buildings’ structural axes. To this end, the problem is expressed and solved as Pattern Recognition problem. As inputs of networks the ratios of seismic parameters’ values along the two horizontal seismic records' components, as well as appropriately chosen structural parameters, were used. The results of analyses show that the neural networks can reliably detect the cases in which the calculation of the critical angle is essential.

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“…Furthermore, they investigated the magnitude regarding the effect of seismic factors on the seismic damage by means of sensitivity examination. Caglar and Garip [35] trained a multi-layer perceptron (MLP) with a back-propagation (BP) algorithm by means of a database that was improved over a statistical process named P25 method. Their model was likewise examined over a verification set which establishes actual present RC constructions exposed to the 2003 Bingöl earthquake.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network

2020

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The latest earthquakes have proven that several existing buildings, particularly in developing countries, are not secured from damages of earthquake. A variety of statistical and machine-learning approaches have been proposed to identify vulnerable buildings for the prioritization of retrofitting. The present work aims to investigate earthquake susceptibility through the combination of six building performance variables that can be used to obtain an optimal prediction of the damage state of reinforced concrete buildings using artificial neural network (ANN). In this regard, a multi-layer perceptron network is trained and optimized using a database of 484 damaged buildings from the Düzce earthquake in Turkey. The results demonstrate the feasibility and effectiveness of the selected ANN approach to classify concrete structural damage that can be used as a preliminary assessment technique to identify vulnerable buildings in disaster risk-management programs.

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Neural network based model for seismic assessment of existing RC buildings

Cited by 10 publications

References 12 publications

Rapid Prediction of Seismic Incident Angle’s Influence on the Damage Level of RC Buildings Using Artificial Neural Networks

Rapid Prediction of Seismic Incident Angle’s Influence on the Damage Level of RC Buildings Using Artificial Neural Networks

Rapid Prediction of Seismic Incident Angle's Influence on the Damage Level of RC Buildings Using Artificial Neural Networks

Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network

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