Analysis of Diaphragm Wall Deflection Induced by Excavation Based on Machine Learning
For the concrete diaphragm wall (CDW) supported excavation, excessive wall deflection may pose a potential risk to adjacent structures and utilities in urban areas. Therefore, it is of significance to predict the CDW deformation with high accuracy and efficiency. This paper investigates three machin...
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2021-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2021/6664409 |
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doaj-0b34ba3cfdef45568ed9b63a0d09666f2021-02-22T00:00:33ZengHindawi LimitedMathematical Problems in Engineering1563-51472021-01-01202110.1155/2021/6664409Analysis of Diaphragm Wall Deflection Induced by Excavation Based on Machine LearningHuajing Zhao0Wei Liu1Hao Guan2Chunqing Fu3School of Urban Rail TransportationSchool of Urban Rail TransportationSchool of Urban Rail TransportationBeijing Uni.-Construction Group Co., Ltd.For the concrete diaphragm wall (CDW) supported excavation, excessive wall deflection may pose a potential risk to adjacent structures and utilities in urban areas. Therefore, it is of significance to predict the CDW deformation with high accuracy and efficiency. This paper investigates three machine learning algorithms, namely, back-propagation neural network (BPNN), long short-term memory (LSTM), and gated recurrent unit (GRU), to predict the excavation-induced CDW deflection. A database of field measurement collected from an excavation project in Suzhou, China, is used to verify the proposed models. The results show that GRU exhibits lower prediction errors and better robustness in 10-fold cross validation than BPNN and executes less computational time than LSTM. Therefore, GRU is the most suitable algorithm for CDW deflection prediction considering both effectiveness and efficiency, and the predicted results can provide reasonable assistance for safety monitoring and early warning strategies conducted on the construction site.http://dx.doi.org/10.1155/2021/6664409 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Huajing Zhao Wei Liu Hao Guan Chunqing Fu |
spellingShingle |
Huajing Zhao Wei Liu Hao Guan Chunqing Fu Analysis of Diaphragm Wall Deflection Induced by Excavation Based on Machine Learning Mathematical Problems in Engineering |
author_facet |
Huajing Zhao Wei Liu Hao Guan Chunqing Fu |
author_sort |
Huajing Zhao |
title |
Analysis of Diaphragm Wall Deflection Induced by Excavation Based on Machine Learning |
title_short |
Analysis of Diaphragm Wall Deflection Induced by Excavation Based on Machine Learning |
title_full |
Analysis of Diaphragm Wall Deflection Induced by Excavation Based on Machine Learning |
title_fullStr |
Analysis of Diaphragm Wall Deflection Induced by Excavation Based on Machine Learning |
title_full_unstemmed |
Analysis of Diaphragm Wall Deflection Induced by Excavation Based on Machine Learning |
title_sort |
analysis of diaphragm wall deflection induced by excavation based on machine learning |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1563-5147 |
publishDate |
2021-01-01 |
description |
For the concrete diaphragm wall (CDW) supported excavation, excessive wall deflection may pose a potential risk to adjacent structures and utilities in urban areas. Therefore, it is of significance to predict the CDW deformation with high accuracy and efficiency. This paper investigates three machine learning algorithms, namely, back-propagation neural network (BPNN), long short-term memory (LSTM), and gated recurrent unit (GRU), to predict the excavation-induced CDW deflection. A database of field measurement collected from an excavation project in Suzhou, China, is used to verify the proposed models. The results show that GRU exhibits lower prediction errors and better robustness in 10-fold cross validation than BPNN and executes less computational time than LSTM. Therefore, GRU is the most suitable algorithm for CDW deflection prediction considering both effectiveness and efficiency, and the predicted results can provide reasonable assistance for safety monitoring and early warning strategies conducted on the construction site. |
url |
http://dx.doi.org/10.1155/2021/6664409 |
work_keys_str_mv |
AT huajingzhao analysisofdiaphragmwalldeflectioninducedbyexcavationbasedonmachinelearning AT weiliu analysisofdiaphragmwalldeflectioninducedbyexcavationbasedonmachinelearning AT haoguan analysisofdiaphragmwalldeflectioninducedbyexcavationbasedonmachinelearning AT chunqingfu analysisofdiaphragmwalldeflectioninducedbyexcavationbasedonmachinelearning |
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