Predicting soil settlement with quantified uncertainties by using ensemble Kalman filtering

Tao, Yuanqin; Sun, Honglei; Cai, Yuanqiang

doi:10.1016/j.enggeo.2020.105753

Cited by 25 publications

(15 citation statements)

References 34 publications

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“…Inverse analysis should be used to optimize the model parameters and, as a result, to update the predicted settlement (class C prediction). The Bayesian framework has been widely used in geotechnical engineering in recent years [15,16,18,24,27].…”

Section: Introductionmentioning

confidence: 99%

Reliability analysis of PVD-improved soft soil settlement prediction using Bayesian back analysis framework and simplified Hypothesis B method

Zhai,

Du,

Peng

et al. 2024

Preprint

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The time-dependent settlement of soft soils is one of the key problems in geotechnical engineering. Using Bayesian back analysis, this study examined the reliability of settlement of the Ballina embankment in Australia. As random variables, the primary compression index (Cc), swelling index (Ce), and secondary compression index (Cαe) were examined for their influence on the settlement probability distribution. To generate compression index samples, Monte Carlo Markov chain simulations （MCMCS） were used, and predicted settlement samples were derived from the compression index samples. Consequently, the predicted settlement samples can be used for the reliability analysis. Based on a comparison between the field settlement data and the predicted settlement, the results indicate that the 90% confidence interval of the predicted settlement is in reasonable agreement with the field settlement monitoring data. With the incorporation of more monitored settlement data into the Bayesian framework, the distribution of predicted settlement shifts from the Weibull distribution to the normal distribution. In addition, the degree of uncertainty in the prediction of settlement decreases with the amount of data incorporated into the model. Additionally, the small amount of data used in the Bayesian framework can also lead to underestimations of failure probability.

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Section: Introductionmentioning

confidence: 99%

Reliability analysis of PVD-improved soft soil settlement prediction using Bayesian back analysis framework and simplified Hypothesis B method

Zhai,

Du,

Peng

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…1 Among the various improvement methods, vacuum preloading combined with pre-fabricated vertical drainage can rapidly apply a vacuum load to drain the water out without considering the bearing capacity of the soil; thus, this approach has been utilized successfully to treat this kind of high-water content soft soil. [2][3][4][5] However, in the use of PVDs for drainage of soft soil with high fine content, a dense soil particle packed column (also called a "soil column") forms around the vertical drainage board, 6 as shown in Figure 1. That soil column significantly reduces the drainage velocity and restrains the further consolidation of the soil outside of this zone.…”

Section: Introductionmentioning

confidence: 99%

“…As the hydraulic soil dredged from the sea to construct land has an extremely high initial water content (approximately 200%) and is highly compressible, it must be dehydrated to improve its strength before subsequent foundation treatment 1 . Among the various improvement methods, vacuum preloading combined with pre‐fabricated vertical drainage can rapidly apply a vacuum load to drain the water out without considering the bearing capacity of the soil; thus, this approach has been utilized successfully to treat this kind of high‐water content soft soil 2–5 . However, in the use of PVDs for drainage of soft soil with high fine content, a dense soil particle packed column (also called a “soil column”) forms around the vertical drainage board, 6 as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Studying the soil column formation in soft soil improved by vacuum preloading via coupled scale‐up CFD‐DEM simulations

Zhu

Cao

et al. 2022

Num Anal Meth Geomechanics

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In many large-scale land reclamation projects, vacuum preloading combined with a prefabricated vertical drain (PVD) system has been utilized successfully to improve the slurry with extremely high water content. However, in the use of PVD systems for soft soil with high fine contents, a dense "soil column" forms around the drainage board, which dramatically impedes the drainage and compromises the improvement level. To understand the soil column formation mechanism, coupled computational fluid dynamics-discrete element method (CFD-DEM) and scale-up strategy were employed in this work to study microsized soil particle movements. The results showed that the soil particles driven by the fluid gradually clogged the PVD board filter and then piled up. Under vacuum preloading, seepage occurred in the soil column, causing vacuum pressure loss and reducing the drainage velocity. As the permeability of the micron-sized soil column is exponentially related to the particle size, the drainage velocity through the soil column with high fine content was low. With the development of the soil column, the drainage velocity decreased, slowing the subsequent soil column development, and changing the packing structure of the soil column. In addition, the vacuum pressure, opening size of the drainage board filter, and cohesion among soil particles influenced the soil column formation. The effects of these aspects are discussed in this work to provide ideas for improving the drainage efficiencies of PVD systems.

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“…Akita 9 utilized the displacement of a structure to estimate the spring constant. Tao 13 used the EnKF to predict the soil settlement of an airport road. Pandurangan 14 used the data from tiltmeters to estimate the fracture growth parameters.…”

Section: Introductionmentioning

confidence: 99%

Data assimilation for surface wave method by ensemble Kalman filter with random field modeling

Ren

Nishimura

Shibata

et al. 2022

Num Anal Meth Geomechanics

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The ensemble Kalman filter (EnKF) was used to estimate the spatial distribution of the Young's modulus of a model of an earth‐fill dam by assimilating the travel time to the first arrival of the surface waves. By the ensemble data assimilation, the measured data from a geophysical exploration was applied to simultaneously estimate the geotechnical properties and evaluate the uncertainties. Swedish weight sounding (SWS) test results were employed as the prior information to generate the initial ensemble through the sequential Gaussian simulation (sGs). In the experiments of assimilation, it was shown that the reproducibility of the parameter field is enhanced by this initial ensemble generation method, and that the uncertainties of the identified parameters can be reduced by the assimilation.

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Predicting soil settlement with quantified uncertainties by using ensemble Kalman filtering

Cited by 25 publications

References 34 publications

Reliability analysis of PVD-improved soft soil settlement prediction using Bayesian back analysis framework and simplified Hypothesis B method

Reliability analysis of PVD-improved soft soil settlement prediction using Bayesian back analysis framework and simplified Hypothesis B method

Studying the soil column formation in soft soil improved by vacuum preloading via coupled scale‐up CFD‐DEM simulations

Data assimilation for surface wave method by ensemble Kalman filter with random field modeling

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