Transmission and application of a P-wave across joints based on a modified g-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e790" altimg="si33.svg"><mml:mi>λ</mml:mi></mml:math> model

Fan, Zhanfeng; Zhang, Jichun; Xu, Hua; Wang, Xu

doi:10.1016/j.ijrmms.2021.104991

Cited by 7 publications

(4 citation statements)

References 41 publications

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“…In northern China, building foundation settlement is a common event and has resulted in millions of dollars of economic losses and several casualties. The major cause of foundation settlement can be attributed to soil liquefaction, which changes the shear stress in the foundation soil (Dong et al, 2020;Fan et al, 2022). This results in deviatoric deformation within the liquefiable soil beneath the building foundation and volumetric strains due to localized drainage during shaking (Lu et al, 2019).…”

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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“…The transmission of stress waves through joints are well studied, with analytical expressions derived for single joints [19] and multiple joints [1,3,4,20,21]. For linear elastic joints, Pyrak-Nolte et al [19] showed that energy is transmitted and reflected from a single joint, shown by Eqs.…”

Section: Introductionmentioning

confidence: 99%

Jointed Rock Masses as Metamaterials - Implications for Railway Tunnel Vibrations

Holmes¹,

Paraskevopoulou²,

Hildyard³

et al. 2023

Preprint

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“…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). The factors that trigger landslides vary, including heavy rainfall, reservoir water fluctuations, earthquakes, and human activities (Fan et al, 2022;Li et al, 2022). Reliable early warning systems that accurately quantify future landslide deformation assist experts in the evaluation of incoming landslide hazards (Tang et al, 2021).…”

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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Transmission and application of a P-wave across joints based on a modified g-λ model

Cited by 7 publications

References 41 publications

Interval Prediction of Building Foundation Settlement Using Kernel Extreme Learning Machine

Interval Prediction of Building Foundation Settlement Using Kernel Extreme Learning Machine

Jointed Rock Masses as Metamaterials - Implications for Railway Tunnel Vibrations

Sparse Gaussian Process Regression for Landslide Displacement Time-Series Forecasting

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