Analysis of Diaphragm Wall Deflection Induced by Excavation Based on Machine Learning

Huajing Zhao, Wei Liu, Hao Guan and Chunqing Fu

Mathematical Problems in Engineering, 2021, vol. 2021, 1-10

Abstract:

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.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2021/6664409.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2021/6664409.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:6664409

DOI: 10.1155/2021/6664409

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().