Can geotechnical liquefaction indices serve as predictors of foundation settlement?

Bullock, Zach; Dashti, Shideh; Liel, Abbie B.

doi:10.1177/8755293021994844

Cited by 9 publications

(2 citation statements)

References 40 publications

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“…This result is consistent with the findings of the literature. 15,43 However, it is worth pointing out that when determining the optimal ground motion intensity measure suitable for evaluating liquefaction-induced settlement, the intensity measure derived from nearby strong ground motion stations should be used instead of the estimated values to avoid erroneous conclusions.…”

Section: Discussionmentioning

confidence: 99%

A continuous Bayesian network regression model for estimating seismic liquefaction‐induced settlement of the free‐field ground

Xiong

Zhang

et al. 2022

Earthq Engng Struct Dyn

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The Bayesian network (BN) method has been successfully applied to evaluate earthquake liquefaction‐induced settlement of free‐field ground in recent years due to several specific advantages. However, the existing BN models need to discretize all continuous variables, and therefore, they can only predict the range value of the settlement (i.e., classification). Thus, information loss is inevitable in the process of discretization, which will largely reduce the prediction accuracy of the model. To realize the application of the BN method in the regression prediction of seismic liquefaction‐induced settlement, this study proposes a hybrid modelling method combining a hill‐climbing algorithm and domain knowledge to construct the structure of a continuous BN regression model based on historical liquefaction‐induced settlement data, and then the conditional linear Gaussian approach is used to learn the conditional probability distributions of parameters. A five‐fold cross‐validation test is used to demonstrate better generalization performance and advantages (such as considering model uncertainty and prior knowledge) of the continuous BN regression model compared with a discrete BN classification model, simplified methods such as the Tokimatsu & Seed and Ishihara & Yoshimine methods, and an artificial neural network model. Their advantages and disadvantages are discussed. In addition, the other two continuous BN models using the Arias intensity and cumulative absolute velocity instead of the peak ground acceleration perform slightly worse than the proposed continuous BN model, and the reason for this difference is discussed.

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Section: Discussionmentioning

confidence: 99%

A continuous Bayesian network regression model for estimating seismic liquefaction‐induced settlement of the free‐field ground

Xiong

Zhang

et al. 2022

Earthq Engng Struct Dyn

View full text Add to dashboard Cite

show abstract

“…Among them, the theoretical calculation method can be subdivided into empirical method and numerical analysis method. The former is simple and practical, and is generally determined based on laboratory test results combined with relevant experience, and the calculated results generally have a large deviation from the measured values [2][3]. The latter is the product of modern mechanics research and develops gradually with the progress of computer technology.…”

Section: Introductionmentioning

confidence: 99%

Research on Settlement Prediction of Building Foundation in Smart City Based on BP Network

Wei¹

2023

IJACSA

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In the construction process of high-rise buildings, it is necessary to predict the settlement and deformation of the foundation, and the current prediction methods are mainly based on empirical theoretical calculations and methods and more accurate numerical analysis methods. In the face of the interference of complex and ever-changing terrain and parameter values on prediction methods, in order to accurately determine the settlement of building foundations, this study designed a smart city building foundation settlement prediction method based on BP neural network. Firstly, a real-time dynamic monitoring unit for building foundation settlement was constructed using Wireless Sensor Network (WSN) technology. Then, the monitoring data was used to calculate the relevant parameters of building foundation settlement through layer sum method. Finally, input the monitoring data into the BP network results, adjust the weights of the output layer and hidden layer using settlement related parameters, and output the settlement prediction results of the smart city building foundation through training. The study selected average error and prediction time as evaluation criteria to test the feasibility of the method proposed in this article. This method can effectively predict foundation settlement, with an average prediction error always less than 4% and a prediction process time always less than 49ms.

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Identifying the optimal intensity measure and key factors of earthquake liquefaction-induced uplift of underground structures

Pang

2023

Bull Eng Geol Environ

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Can geotechnical liquefaction indices serve as predictors of foundation settlement?

Cited by 9 publications

References 40 publications

A continuous Bayesian network regression model for estimating seismic liquefaction‐induced settlement of the free‐field ground

A continuous Bayesian network regression model for estimating seismic liquefaction‐induced settlement of the free‐field ground

Research on Settlement Prediction of Building Foundation in Smart City Based on BP Network

Identifying the optimal intensity measure and key factors of earthquake liquefaction-induced uplift of underground structures

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