Prediction of building damage induced by tunnelling through an optimized artificial neural network

Ground surface movement due to tunnelling in urban areas imposes strains to the adjacent buildings through distortion and rotation, and may consequently cause structural damage. The methods of building damage estimation are generally based on a two-stage procedure in which ground movement in the gre...

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Main Authors: Moosazadeh, S., Namazi, E., Aghababaei, H., Marto, A., Mohamad, H., Hajihassani, M.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.20917 /
Published: Springer London 2018
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047161551&doi=10.1007%2fs00366-018-0615-5&partnerID=40&md5=4e9c58609aa02661f6811a269626fd58
http://eprints.utp.edu.my/20917/
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spelling utp-eprints.209172019-02-26T02:44:09Z Prediction of building damage induced by tunnelling through an optimized artificial neural network Moosazadeh, S. Namazi, E. Aghababaei, H. Marto, A. Mohamad, H. Hajihassani, M. Ground surface movement due to tunnelling in urban areas imposes strains to the adjacent buildings through distortion and rotation, and may consequently cause structural damage. The methods of building damage estimation are generally based on a two-stage procedure in which ground movement in the greenfield condition is estimated empirically, and then, a separate method based on structural mechanic principles is used to assess the damage. This paper predicts the building damage based on a model obtained from artificial neural network and a particle swarm optimization algorithm. To develop the model, the input and output parameters were collected from Line No. 2 of the Karaj Urban Railway Project in Iran. Accordingly, two case studies of damaged buildings were used to assess the ability of this model to predict the damage. Comparison with the measured data indicated that the model achieved the satisfactory results. © 2018 Springer-Verlag London Ltd., part of Springer Nature Springer London 2018 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047161551&doi=10.1007%2fs00366-018-0615-5&partnerID=40&md5=4e9c58609aa02661f6811a269626fd58 Moosazadeh, S. and Namazi, E. and Aghababaei, H. and Marto, A. and Mohamad, H. and Hajihassani, M. (2018) Prediction of building damage induced by tunnelling through an optimized artificial neural network. Engineering with Computers . pp. 1-13. http://eprints.utp.edu.my/20917/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Ground surface movement due to tunnelling in urban areas imposes strains to the adjacent buildings through distortion and rotation, and may consequently cause structural damage. The methods of building damage estimation are generally based on a two-stage procedure in which ground movement in the greenfield condition is estimated empirically, and then, a separate method based on structural mechanic principles is used to assess the damage. This paper predicts the building damage based on a model obtained from artificial neural network and a particle swarm optimization algorithm. To develop the model, the input and output parameters were collected from Line No. 2 of the Karaj Urban Railway Project in Iran. Accordingly, two case studies of damaged buildings were used to assess the ability of this model to predict the damage. Comparison with the measured data indicated that the model achieved the satisfactory results. © 2018 Springer-Verlag London Ltd., part of Springer Nature
format Article
author Moosazadeh, S.
Namazi, E.
Aghababaei, H.
Marto, A.
Mohamad, H.
Hajihassani, M.
spellingShingle Moosazadeh, S.
Namazi, E.
Aghababaei, H.
Marto, A.
Mohamad, H.
Hajihassani, M.
Prediction of building damage induced by tunnelling through an optimized artificial neural network
author_sort Moosazadeh, S.
title Prediction of building damage induced by tunnelling through an optimized artificial neural network
title_short Prediction of building damage induced by tunnelling through an optimized artificial neural network
title_full Prediction of building damage induced by tunnelling through an optimized artificial neural network
title_fullStr Prediction of building damage induced by tunnelling through an optimized artificial neural network
title_full_unstemmed Prediction of building damage induced by tunnelling through an optimized artificial neural network
title_sort prediction of building damage induced by tunnelling through an optimized artificial neural network
publisher Springer London
publishDate 2018
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047161551&doi=10.1007%2fs00366-018-0615-5&partnerID=40&md5=4e9c58609aa02661f6811a269626fd58
http://eprints.utp.edu.my/20917/
_version_ 1741196416015925248
score 11.62408

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