A theoretical model for estimating the water-tightness of jet-grouted cut-off walls with geometric imperfections

Wang, Kai; Li, Zhaoping; Zheng, Hao; Xu, Xuezhao; He, Huafei

doi:10.1016/j.compgeo.2021.104316

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…Additionally, the predictions generated by the physics-inspired NNs were compared with the deterministic prediction derived from the equivalent wall thickness method. The daily seepage flow rate reported by Wang et al [40] using the equivalent wall thickness method was 1.76 m 3 , which closely aligned with the 50% fractile (i.e., median) of the seepage flow rate predicted by TDA at 1.64 m 3 /day. The 95% fractile of seepage flow rate given by TDA was 4.78 m 3 /day, which indicates that significant construction errors in the cut-off walls may lead to substantial seepage flow rates.…”

Section: Model Capacity Of Physics-inspired Nnssupporting

confidence: 74%

A physics-inspired machine learning approach for water-tightness estimation of defective cut-off walls with random construction errors

Tao,

Pan,

Liu

et al. 2023

Acta Geotech.

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Underground cut-off walls are widely used in various geotechnical applications to hinder groundwater flow, contaminant transportation and possibly heat conduction. Cut-off walls were usually found defective due to construction errors during the installation phase, leading to significant leakages in subsequent operation phase. Existing physics-based leakage evaluation approaches, such as the finite element analysis and three-dimensional discretized algorithm, are computationally expensive and may not satisfy the need for instant on-site leakage risk assessment. In this regard, a more efficient mapping between construction errors and performance of cut-off walls is highly demanded. A natural option for such mapping is the artificial intelligence approach. Several novel physics-inspired neural network models are proposed based on the well-designed physical layers with varying complexity, to strike a balance between benefits of machine learning and physical approaches. The result shows that introducing physical layers with clearer physical meaning helps mitigating overfitting problems, improving prediction accuracy, result interpretability and model capacity, at the price of increasing the calculation efficiency during training. An optimized degree of physical meaning clarity can be achieved to strike a balance between fitting effect and training computation cost.

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Section: Model Capacity Of Physics-inspired Nnssupporting

confidence: 74%

A physics-inspired machine learning approach for water-tightness estimation of defective cut-off walls with random construction errors

Tao,

Pan,

Liu

et al. 2023

Acta Geotech.

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“…outlined the coefficient of variation for column diameters that considers soil heterogeneity. Wang et al 37 . concluded that the measured column diameters on a construction site follow a Gaussian distribution.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Modoni et al 25 outlined the coefficient of variation for column diameters that considers soil heterogeneity. Wang et al 37 concluded that the measured column diameters on a construction site follow a Gaussian distribution. However, these studies only introduce statistical indices to exhibit the probability distribution of column diameter.…”

Section: Introductionmentioning

confidence: 99%

Modeling diameter of jet grouting columns using Bayesian framework

Zhao,

Chen,

Wang

2023

Num Anal Meth Geomechanics

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This study proposes a new model in explicit form to predict the diameter of the jet grouting column of three popular jet grouting systems (i.e., single, double, and triple). The proposed model can quantify the uncertainty associated with the prediction. Bayesian model selection was used to determine the optimal models and, the bootstrap sampling method was adopted to avoid bias in the collected database. The predictions agree well with the measured data, and the 95% credible intervals can cover almost all the measured data on the testing dataset, which indicates the robustness of the proposed model. A simple formula combined with a table was established to facilitate the engineers in applying the proposed model. The results reveal that soil type has a significant impact on diameter prediction. Uncertainty quantification can reveal possible fluctuations in column diameter and is vital to get a cost‐efficient design.

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“…In addition, the finite element numerical calculation method is often used to explore and evaluate the relationship between shotcrete materials, shotcrete parameters and wall indicators [37][38][39]. Wang et al [40] analyzed the watertightness of jet-grouted cut-off walls with geometric imperfections and deduced a simplified theoretical model. Flanagan et al [41] calculated the in-situ hydraulic conductivities of both the surrounding glacial soils and the jet grout walls by matching observed inflows and piezometric levels.…”

Section: Introductionmentioning

confidence: 99%

Study on the Construction Method and Effects of Ipsilateral, Multi-Nozzle, High-Pressure Jet Grouting Cut-Off Wall

Xie

Gao

et al. 2022

Sustainability

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High-pressure jet grouting is widely used in the construction of cut-off wall in hydraulic engineering. Compared to the high-pressure jet grouting used for foundation improvement, the high-pressure jet grouting cut-off wall of hydraulic engineering has lower requirements for the advance consolidation and provision of bearing capacity, and more focus on providing an anti-seepage effect and ensuring the continuous integrity of the wall. In the past, the research on high-pressure jet grouting focused more on the application to foundation treatments, and the research on the construction efficiency of cut-off wall is relatively insufficient. Therefore, in this study, a self-developed ipsilateral multi-nozzle jet grouting machine with a large drill diameter and construction method are proposed according to the construction characteristics of the embankment cut-off wall in hydraulic engineering. Based on the dyke protection projects, Jinggangshan navigation hydropower and Xingan navigation hydropower, the large-diameter, multi-nozzle, high-pressure jet cut-off wall test is carried out, and the wall forming effect of the cut-off wall is verified through the combination of indoor and outdoor tests. The results show that the proposed construction method can adapt to the formation conditions in which it is difficult to implement conventional high-pressure jet grouting, and obtains good construction efficiency. A favorable wall quality can be obtained by using more efficient large-diameter and ipsilateral multi-nozzle jet grouting in the construction of high-pressure jet cut-off wall.

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A theoretical model for estimating the water-tightness of jet-grouted cut-off walls with geometric imperfections

Cited by 7 publications

References 17 publications

A physics-inspired machine learning approach for water-tightness estimation of defective cut-off walls with random construction errors

A physics-inspired machine learning approach for water-tightness estimation of defective cut-off walls with random construction errors

Modeling diameter of jet grouting columns using Bayesian framework

Study on the Construction Method and Effects of Ipsilateral, Multi-Nozzle, High-Pressure Jet Grouting Cut-Off Wall

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