Analysis and design of partial ground improvement in deep excavations

Ou, Chang‐Yu; Teng, Fuchen; Wang, I-Wen

doi:10.1016/j.compgeo.2007.09.005

Cited by 46 publications

(5 citation statements)

References 10 publications

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“…However, plane strain assumption is sometimes inconsistent with the measured excavation behavior and a number of 3D simulation studies have been conducted to describe the 3D effects in deep strutted excavations in a variety of soil conditions [31][32][33][34][35][36][37][38]. Ou et al [31,39] proposed a relationship for estimating 3D maximum wall deflection of an excavation based on 2D FE analysis results.…”

Section: Y M a Hashash H Song And A Osoulimentioning

confidence: 99%

Three‐dimensional inverse analyses of a deep excavation in Chicago clays

Hashash

Song

Osouli

2010

Num Anal Meth Geomechanics

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SUMMARYNumerical models are commonly used to estimate excavation-induced ground movements. Twodimensional (2D) plain strain assumption is typically used for the simulation of deep excavations which might not be suitable for excavations where three-dimensional (3D) effects dominate the ground response. This paper adapts an inverse analysis algorithm to learn soil behavior from field measurements using a 3D model representation of an excavation. The paper describes numerical issues related to this development including the generation of the 3D model mesh from laser scan images of the excavation. The inverse analysis to extract the soil behavior in 3D is presented. The model captures the measured wall deflections. Although settlements were not sufficiently measured, the predicted settlements around the excavation site reflected strong 3D effects and were consistent with empirical correlations.

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Section: Y M a Hashash H Song And A Osoulimentioning

confidence: 99%

Three‐dimensional inverse analyses of a deep excavation in Chicago clays

Hashash

Song

Osouli

2010

Num Anal Meth Geomechanics

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“…A few case studies and numerical analyses of the deep mixing column type of ground improvement in deep excavations have been published [33][34][35][36][37][38]. In the majority of the numerical analyses that have been made, 2D ''plane strain'' models or 3D analyses where the improved soil is modeled as a composite material have been used.…”

Section: Introductionmentioning

confidence: 99%

Two- and three-dimensional analyses of excavation support with rows of dry deep mixing columns

Ignat¹,

Baker

Larsson³

et al. 2015

Computers and Geotechnics

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“…Viewed from the construction perspective, the main construction area of an urban tunnel is concentrated in the soft layer under the dense building group in cities [2]. This will inevitably cause settlement due to the effects of two major factors: shallow depth and soft ground [3][4][5]. Once ground settlement occurs, building safety and other public utilities above the construction area are seriously threatened.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Random Forest-Based Models for Earth Pressure Balance Tunneling-Induced Ground Settlement Prediction

Yang

Yong

et al. 2023

Applied Sciences

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Construction-induced ground settlement is a serious hazard in underground tunnel construction. Accurate ground settlement prediction has great significance in ensuring the surface building’s stability and human safety. To that end, 148 sets of data were collected from the Singapore Circle Line rail traffic project containing seven defining parameters to create a database for predicting ground settlement. These parameters are the tunnel depth (H), the tunnel advance rate (AR), the EPB earth pressure (EP), the mean SPTN value from the soil crown to the surface (Sm), the mean water content of the soil layer (MC), the mean modulus of elasticity of the soil layer (E), and the grout pressure used for injecting grout into the tail void (GP). Three hybrid models consisting of random forest (RF) and three types of meta-heuristics, Ant Lion Optimizier (ALO), Multi-Verse Optimizer (MVO), and Grasshopper Optimization Algorithm (GOA), were developed to predict ground settlement. Furthermore, the mean absolute error (MAE), the mean absolute percentage error (MAPE), the coefficient of determination (R2) and the root mean square error (RMSE) were used to assess predictive performance of the constructed models for predicting ground settlement. The evaluation results demonstrated that the GOA-RF with a population size of 10 has achieved the most outstanding predictive capability with the indices of MAE (Training set: 2.8224; Test set: 2.3507), MAPE (Training set: 40.5629; Test set: 38.5637), R2 (Training set: 0.9487; Test set: 0.9282), and RMSE (Training set: 4.93; Test set: 3.1576). Finally, the sensitivity analysis results indicated that MC, AR, Sm, and GP have a significant impact on ground settlement prediction based on the GOA-RF model.

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Analysis and design of partial ground improvement in deep excavations

Cited by 46 publications

References 10 publications

Three‐dimensional inverse analyses of a deep excavation in Chicago clays

Three‐dimensional inverse analyses of a deep excavation in Chicago clays

Two- and three-dimensional analyses of excavation support with rows of dry deep mixing columns

Hybrid Random Forest-Based Models for Earth Pressure Balance Tunneling-Induced Ground Settlement Prediction

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