Effects of Unidirectional In Situ Stress on Crack Propagation of a Jointed Rock Mass Subjected to Stress Wave

Fan, Zhanfeng; Cai, Jinhua

doi:10.1155/2021/5529540

Cited by 4 publications

(4 citation statements)

References 39 publications

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“…Our case study area is in Fuxin City, Liaoning Province, China where many buildings are between 20 and 30 years old, are located in urbanized areas or suburbs, and experience uneven foundation settlement. Natural changes in groundwater levels are exacerbated by anthropogenic activities which have caused and accelerated soil liquefaction, resulting in foundation settlement (Fan and Cai 2021). In recent years, several meter-long cracks have emerged in building foundations and at the lower level of walls.…”

Section: Foundation Settlement and Problem Formulation Foundation Set...mentioning

confidence: 99%

Interval Prediction of Building Foundation Settlement Using Kernel Extreme Learning Machine

et al. 2022

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Dynamic building foundation settlement subsidence threatens urban businesses and residential communities. In the temporal domain, building foundation settlement is often dynamic and requires real-time monitoring. Accurate quantification of the uncertainty of foundation settlement in the near future is essential to advanced risk management for buildings. Traditional models for predicting foundation settlement mostly utilize the point estimates approach, which provides a single value that can be close or distant from the actual one. However, such an estimation fails to quantify estimation uncertainties. The interval prediction, as an alternative, can provide a prediction interval for the ground settlement with high confidence bands. This study, proposes a lower upper bound estimation approach integrated with a kernel extreme learning machine to predict ground settlement levels with prediction intervals in the temporal domain. A revised objective function is proposed to further improve the interval prediction performance. In this study, the proposed method is compared to the artificial neural network and classical extreme learning machine. Building settlement data collected from Fuxing City, Liaoning Province in China was used to validate the proposed approach. The comparative results show that the proposed approach can construct superior prediction intervals for foundation settlement.

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Section: Foundation Settlement and Problem Formulation Foundation Set...mentioning

confidence: 99%

Interval Prediction of Building Foundation Settlement Using Kernel Extreme Learning Machine

et al. 2022

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“…Peng et al [27] found that high ground temperature and ground stress had a significant influence on the cracking mode of the surrounding rock of the hydraulic tunnel. Fan et al [28] used LS-DYNA to explore the crack growth of jointed rock mass under different ground stresses and verified that in-situ stress has an obvious guiding effect on crack growth.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Mechanical Characteristics of Horseshoe Tunnel Subjected to Blasting and Confining Pressure

Yao

Liu

et al. 2023

Applied Sciences

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The blast loading direction and in-situ stress field have an effect on the destruction process in the surrounding rock around the tunnel. Five blasting directions are considered in the experiment and simulation. The static stress distribution by confining pressure and the superposition stress waves process are discussed by simulation in LS-DYNA. Results indicated that the hoop stress accelerates the radial cracks growing, and the damage around the hole is not influenced by the blasting direction. The stress wave superposition and failure process along the tunnel are affected by the blasting direction. The distribution of static prestress is symmetrical under the uniform confining pressure (k = 1) and decelerates the crack extension by confining pressure. Under the non-uniform confining pressure loading (k ≠ 1), the tensile prestress is formed with a k increase and accelerates the horizontal crack propagation after blast loading. The concentrated stress is serious in the partial region along the tunnel, especially in the vault position under the static-dynamic coupling loading. Notably, the horizontal destruction area between the hole and the tunnel further expands when k increases.

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“…In southwest China, landslides are among the most devastating natural disasters, causing damage to life, property, and local infrastructure (Aggarwal et al, 2020;Fan & Cai 2021;Zhou et al, 2021). Over 7,000 landslides occurred in China in 2016 as reported in the annual report provided by the China Institute of Geo-Environment Monitoring (Tang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Sparse Gaussian Process Regression for Landslide Displacement Time-Series Forecasting

Yang

Feng²,

Wan³

et al. 2022

Front. Earth Sci.

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Landslide hazards are complex nonlinear systems with a highly dynamic nature. Accurate forecasting of landslide displacement and evolution is crucial for the prevention and mitigation of landslide hazards. In this study, a probabilistic landslide displacement forecasting model based on the quantification of epistemic uncertainty is proposed. In particular, the displacement forecasting problem is cast as a time-series regression problem with limited training samples and must be solved by statistical inference. The epistemic uncertainty of the landslide displacement series is depicted by the statistical properties of the function space constituted by the nonlinear mappings generated by the sparse Gaussian process regression. Data for our study was collected from the study area located in northwestern China. Other state-of-the-art probabilistic forecasting models have also been utilized for comparative analysis. The experimental results confirmed the superiority of the sparse Gaussian process in the modeling of landslide displacement series in terms of forecasting accuracy, uncertainty quantification, and robustness to overfitting.

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Effects of Unidirectional In Situ Stress on Crack Propagation of a Jointed Rock Mass Subjected to Stress Wave

Cited by 4 publications

References 39 publications

Interval Prediction of Building Foundation Settlement Using Kernel Extreme Learning Machine

Interval Prediction of Building Foundation Settlement Using Kernel Extreme Learning Machine

Dynamic Mechanical Characteristics of Horseshoe Tunnel Subjected to Blasting and Confining Pressure

Sparse Gaussian Process Regression for Landslide Displacement Time-Series Forecasting

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