Computers and Concrete

  • 제12권2호
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  • Pages.229-241
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  • 2013
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Neural network based model for seismic assessment of existing RC buildings

  • Caglar, Naci (Department of Civil Engineering, Sakarya University, Esentepe Campus) ;
  • Garip, Zehra Sule (Department of Civil Engineering, Karabuk University)
  • 투고 : 2012.10.02
  • 심사 : 2013.03.31
  • 발행 : 2013.08.01
⟨ 이전 논문 다음 논문 ⟩

초록

The objective of this study is to reveal the sufficiency of neural networks (NN) as a securer, quicker, more robust and reliable method to be used in seismic assessment of existing reinforced concrete buildings. The NN based approach is applied as an alternative method to determine the seismic performance of each existing RC buildings, in terms of damage level. In the application of the NN, a multilayer perceptron (MLP) with a back-propagation (BP) algorithm is employed using a scaled conjugate gradient. NN based model wasd eveloped, trained and tested through a based MATLAB program. The database of this model was developed by using a statistical procedure called P25 method. The NN based model was also proved by verification set constituting of real existing RC buildings exposed to 2003 Bingol earthquake. It is demonstrated that the NN based approach is highly successful and can be used as an alternative method to determine the seismic performance of each existing RC buildings.

키워드

참고문헌

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피인용 문헌

  1. Prediction of acceleration and impact force values of a reinforced concrete slab vol.14, pp.5, 2014, https://doi.org/10.12989/cac.2014.14.5.563
  2. A new approach to determine the moment-curvature relationship of circular reinforced concrete columns vol.15, pp.3, 2015, https://doi.org/10.12989/cac.2015.15.3.321
  3. Seismic parameters’ combinations for the optimum prediction of the damage state of R/C buildings using neural networks vol.106, 2017, https://doi.org/10.1016/j.advengsoft.2017.01.001
  4. Approaches to the rapid seismic damage prediction of r/c buildings using artificial neural networks vol.165, pp.None, 2013, https://doi.org/10.1016/j.engstruct.2018.03.028
  5. Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network vol.13, pp.8, 2013, https://doi.org/10.3390/en13082060
  6. Numerical analysis of the shear strength of circular reinforced concrete columns subjected to cyclic lateral loads using linear genetic programming vol.37, pp.7, 2020, https://doi.org/10.1108/ec-10-2018-0453
  7. Rapid Prediction of Seismic Incident Angle’s Influence on the Damage Level of RC Buildings Using Artificial Neural Networks vol.12, pp.3, 2013, https://doi.org/10.3390/app12031055