Response prediction of multi-story building using backpropagation neural networks method

Response prediction of multi-story building using backpropagation neural networks method

The active ground motion in Indonesia might cause a catastrophic collapse of the building which leads to casualties and property damages. Therefore, it is imperative to design the structural response of building against seismic hazard correctly. Seismic-resistant building design process requires str...

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Main Authors: Suryanita Reni, Maizir Harnedi, Firzal Yohannes, Jingga Hendra, Yuniarto Enno
Format: Article
Language:English
Published: EDP Sciences 2019-01-01
Series:MATEC Web of Conferences
Online Access:https://www.matec-conferences.org/articles/matecconf/pdf/2019/25/matecconf_icancee2019_01011.pdf
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spelling doaj-1869bf37c48d4ef88cc78818e433a3542021-02-02T05:29:32ZengEDP SciencesMATEC Web of Conferences2261-236X2019-01-012760101110.1051/matecconf/201927601011matecconf_icancee2019_01011Response prediction of multi-story building using backpropagation neural networks methodSuryanita Reni0Maizir Harnedi1Firzal Yohannes2Jingga Hendra3Yuniarto Enno4Department of Civil Engineering, Universitas RiauDepartment of Civil Engineering, Sekolah Tinggi Teknologi PekanbaruDepartement of Architecture, Universitas RiauDepartment of Civil Engineering, Universitas RiauDepartment of Civil Engineering, Universitas RiauThe active ground motion in Indonesia might cause a catastrophic collapse of the building which leads to casualties and property damages. Therefore, it is imperative to design the structural response of building against seismic hazard correctly. Seismic-resistant building design process requires structural analysis to be performed to obtain the necessary building responses. However, the structural analysis could be difficult and time-consuming. This study aims to predict the structural response includes displacement, velocity, and acceleration of multi-story building with the fixed floor plan using Backpropagation Neural Network (BPNN) method. By varying the building height, soil condition, and seismic location in 47 cities in Indonesia, 6345 datasets were obtained and fed into the BPNN model for the learning process. The trained BPNN is capable of predicting the displacement, velocity, and acceleration responses with up to 96% of the expected rate.https://www.matec-conferences.org/articles/matecconf/pdf/2019/25/matecconf_icancee2019_01011.pdf
collection DOAJ
language English
format Article
sources DOAJ
author Suryanita Reni
Maizir Harnedi
Firzal Yohannes
Jingga Hendra
Yuniarto Enno
spellingShingle Suryanita Reni
Maizir Harnedi
Firzal Yohannes
Jingga Hendra
Yuniarto Enno
Response prediction of multi-story building using backpropagation neural networks method
MATEC Web of Conferences
author_facet Suryanita Reni
Maizir Harnedi
Firzal Yohannes
Jingga Hendra
Yuniarto Enno
author_sort Suryanita Reni
title Response prediction of multi-story building using backpropagation neural networks method
title_short Response prediction of multi-story building using backpropagation neural networks method
title_full Response prediction of multi-story building using backpropagation neural networks method
title_fullStr Response prediction of multi-story building using backpropagation neural networks method
title_full_unstemmed Response prediction of multi-story building using backpropagation neural networks method
title_sort response prediction of multi-story building using backpropagation neural networks method
publisher EDP Sciences
series MATEC Web of Conferences
issn 2261-236X
publishDate 2019-01-01
description The active ground motion in Indonesia might cause a catastrophic collapse of the building which leads to casualties and property damages. Therefore, it is imperative to design the structural response of building against seismic hazard correctly. Seismic-resistant building design process requires structural analysis to be performed to obtain the necessary building responses. However, the structural analysis could be difficult and time-consuming. This study aims to predict the structural response includes displacement, velocity, and acceleration of multi-story building with the fixed floor plan using Backpropagation Neural Network (BPNN) method. By varying the building height, soil condition, and seismic location in 47 cities in Indonesia, 6345 datasets were obtained and fed into the BPNN model for the learning process. The trained BPNN is capable of predicting the displacement, velocity, and acceleration responses with up to 96% of the expected rate.
url https://www.matec-conferences.org/articles/matecconf/pdf/2019/25/matecconf_icancee2019_01011.pdf
work_keys_str_mv AT suryanitareni responsepredictionofmultistorybuildingusingbackpropagationneuralnetworksmethod
AT maizirharnedi responsepredictionofmultistorybuildingusingbackpropagationneuralnetworksmethod
AT firzalyohannes responsepredictionofmultistorybuildingusingbackpropagationneuralnetworksmethod
AT jinggahendra responsepredictionofmultistorybuildingusingbackpropagationneuralnetworksmethod
AT yuniartoenno responsepredictionofmultistorybuildingusingbackpropagationneuralnetworksmethod
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