Evaluation of Performance of Walls in Deep Excavations Using Wall Deflection Paths

Hwang, Richard N.; Wong, L. W.

doi:10.1007/978-981-10-6632-0_16

Cited by 1 publication

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“…e empirical formula based on historic projects is relatively simple in model and easy to conduct [5][6][7][8], but the predicted results usually tend to be broad and the formula is unable to represent the dynamic evolution of wall deflection. e elaborate numerical simulation is theoretically more precise by considering the soil-structure interaction, but it is still difficult to take all the instinct and extinct factors into account, and a disparity of estimated results and field measurement frequently occurs [9,10].…”

Section: Introductionmentioning

confidence: 99%

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

Huajing

Liu

Guan

et al. 2021

Mathematical Problems in Engineering

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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.

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