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Prediction of Structural Response Based on Ground Acceleration Using Artificial Neural Networks

Author

Listed:
  • RENI SURYANITA

    (Faculty of Engineering, University of Riau, Pekanbaru, Indonesia)

  • HARNEDI MAIZIR

    (Civil Engineering Department, Sekolah Tinggi Teknologi Pekanbaru, Pekanbaru, Indonesia)

  • HENDRA JINGGA

    (Faculty of Engineering, University of Riau, Pekanbaru, Indonesia)

Abstract

This study utilizes Artificial Neural Network (ANN) to predict structural responses of multi-storey reinforced concrete building based on ground acceleration. The strong ground acceleration might cause catasthropic collapse of multi-storey building which leads to casualties and property damages. Therefore, it is imperative to properly design the multi-storey building against seismic hazard. Seismic-resistant building design process requires structural analysis to be performed to obtain the necessary building responses. Modal response spectrum analysis is performed to simulate ground acceleration and produce structural response data for further use in the ANN. The ANN architecture comprises of 3 layers: an input layer, a hidden layer, and an output layer. Ground acceleration parameters from 34 provinces in Indonesia, soil condition, and building geometry are selected as input parameters, whereas structural responses consisting of acceleration, velocity and displacement (story drift) are selected as output parameters for the ANN. As many as 6345 data sets are used to train the ANN. From the overall data sets, 4590 data sets (72%) are used for training process, 877 data sets (14%) for the validation process, and 878 data sets (14%) for testing. The trained ANN is capable for predicting structural responses based on ground acceleration at (96%) rate of prediction and the calculated Mean-Squared Errors (MSE) as low as 1.2.10−4. The high accuracy of structural response prediction can greatly assist the engineer to identify the building condition rapidly and plan the building maintenance routinely.

Suggested Citation

  • Reni Suryanita & Harnedi Maizir & Hendra Jingga, 2017. "Prediction of Structural Response Based on Ground Acceleration Using Artificial Neural Networks," International Journal of Technology and Engineering Studies, PROF.IR.DR.Mohid Jailani Mohd Nor, vol. 3(2), pages 74-83.
    • Handle: RePEc:apa:ijtess:2017:p:74-83
    DOI: 10.20469/ijtes.3.40005-2
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    References listed on IDEAS

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    1. N. A. Zainuddin & Norhuda, I. & Adeib, I. S & Alibek Kuljabekov & S. H. Sarijo, 2015. "An Artificial Neural Network modelling of ginger rhizome extracted using Rapid Expansion Supercritical Solution (RESS) method," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 1(1), pages 01-14.
    2. Harnedi Maizir & Reni Suryanita & Hendra Jingga, 2016. "Estimation of Pile Bearing Capacity of Single Driven Pile in Sandy Soil Using Finite Element and Artificial Neural Network Methods," International Journal of Applied and Physical Sciences, Dr K.Vivehananthan, vol. 2(2), pages 45-50.
    3. Nabeel Zuhair Tawfeeq Abdulnabi & Oguz Altun, 2016. "Batch size for training convolutional neural networks for sentence classification," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 2(5), pages 156-163.
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    Cited by:

    1. Cherl Nino M. Locsin & Rosana J. Ferolin, 2018. "Neural networks application for water distribution demand-driven decision support system," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 4(4), pages 160-175.
    2. Yerkezhan Seitbekova & Bakhytzhan Assilbekov & Iskander Beisembetov & Alibek Kuljabekov, 2020. "The bus arrival time prediction using LSTM neural network and location analysis," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 6(2), pages 46-57.
    3. Arthur Stepchenko & Jurij Chizhov & Ludmila Aleksejeva, 2018. "Transfer of the data preprocessing parameters and fore- casting models," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 4(6), pages 214-221.
    4. Francesco Smarra & Giovanni Domenico Di Girolamo & Vincenzo Gattulli & Fabio Graziosi & Alessandro D’Innocenzo, 2020. "Learning Models for Seismic-Induced Vibrations Optimal Control in Structures via Random Forests," Journal of Optimization Theory and Applications, Springer, vol. 187(3), pages 855-874, December.
    5. Noraida Haji Ali & Fadilah Harun, 2019. "Video forgery detection based-on passive (blind) approach," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 5(5), pages 199-206.
    6. Jian-Da Wu & Yi-Cheng Luo & Hsien-Yu Lin, 2017. "Vehicle types classification using deep neural network techniques," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 3(6), pages 235-243.

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