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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Bibliographic Details
Published in:Mathematical problems in engineering 2021, Vol.2021, p.1-10
Main Authors: Zhao, Huajing, Liu, Wei, Guan, Hao, Fu, Chunqing
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial neural networks
Back propagation networks
Computing time
Construction sites
Deep learning
Deflection
Diaphragm wall
Engineering
Excavation
Machine learning
Neural networks
Neurons
Predictions
Robustness (mathematics)
Simulation
Urban areas
Utilities
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2021/6664409