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

Reni Suryanita, Harnedi Maizir and Hendra Jingga
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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

International Journal of Technology and Engineering Studies, 2017, vol. 3, issue 2, 74-83

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.

Keywords: Artificial Neural Networks; Ground Acceleration; Mean-Squared Error; Response Spectrum; Structural Response (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:apa:ijtess:2017:p:74-83

DOI: 10.20469/ijtes.3.40005-2

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