Numerical prediction and back-calculation of time-dependent behaviour of Ballina test embankment

Jostad, Hans Petter; Palmieri, Francesca; Andresen, Lars; Boylan, Noel

doi:10.1016/j.compgeo.2017.05.026

Cited by 22 publications

(6 citation statements)

References 6 publications

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“…Concerning Ballina clay and Class A prediction, the current study reasonably agreed with the works of Josted et al [30] and Liu et al [42]. Other predictors such as Lim et al [51], though the settlement with Class A was still under-predicted, the authors came up with an improved estimation.…”

Section: Class a Predictionssupporting

confidence: 90%

“…The work of matching techniques was reported by other authors too [7,27,28]. Kelly et al (2018) and a number of other engineers have reported on the numerical modelling development of a trial embankment built on PVD-improved estuarine soft clay of Ballina, Australia [10,[29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 91%

“…The work of a number of authors on Ballina clay supports this argument. The predictions of settlements were analyzed using PLAXIS 2D by employing SSC material model and incorporating LS mesh update option by Lie et al [42], Josted et al [30], Chan et al [32] and Gong [53]. These studies showed that predicted results were found less than or equal to the field observation values.…”

Section: Probable Limits Of Predicted Settlementsmentioning

confidence: 99%

“…In this prediction, the information obtained from laboratory tests is used for the numerical modelling [35], while Class C is used once the field measured data that represent the real condition are known. Class C prediction is performed using back-analysis techniques [24,30,36]. In the back-analysis technique, parameters are adjusted systematically until the predicted settlement agreed reasonably well with the field measurement data.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty Analysis in Prediction of Settlements for Spatial Prefabricated Vertical Drains Improved Soft Soil Sites

2020

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This paper presents the quantification of uncertainties in the prediction of settlements of embankments built on prefabricated vertical drains (PVDs) improved soft soil deposits based on data collected from two well-documented projects, located in Karakore, Ethiopia, and Ballina, Australia. For this purpose, settlement prediction biases and settlement distributions were statistically computed based on analyses conducted on two Class A and Class C numerical predictions made using PLAXIS 2D finite element modelling. From the results of prediction bias, Class C predictions agreed well with the field measured settlements at both sites. In Class C predictions, the computed settlements were biased to the measured values. For Class A predictions, the calculated settlement values were in the range of mean and mean minus 3SD (standard deviations) for Karakore clay, and they were within mean and mean minus 2SD limit for the Ballina soil. The contributing factors to the settlement uncertainties of the Karakore site may include variability within the soil profile of the alluvial deposit, particularly the presence of interbedded granular layer within the soft layers, and the high embankment fills, and the limited number of samples available for laboratory testing. At the Ballina test embankment site, the uncertainties may have been associated with the presence of transitional layers at the bottom of estuarine clay and sensitivity of soft soil to sample disturbances and limitations in representing all the site conditions.

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Section: Class a Predictionssupporting

confidence: 90%

Section: Introductionmentioning

confidence: 91%

Section: Probable Limits Of Predicted Settlementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Uncertainty Analysis in Prediction of Settlements for Spatial Prefabricated Vertical Drains Improved Soft Soil Sites

2020

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“…The simulation sequence of the FEM model included the construction stage of the embankment and subsequent consolidation stages, encompassing 1833 days in aggregate over 24 time steps. The details of FEM modeling have also been discussed by Jostad et al [14] and Tian et al [15].…”

Section: Dictionary Construction Using Fem Results Of Multiple Consol...mentioning

confidence: 99%

Physics-informed machine learning of multi-source monitoring data sparsely measured within a 2D vertical cross-section

Tian,

Wang

2024

IOP Conf. Ser.: Earth Environ. Sci.

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Conventional machine learning (ML) methods suffer from several limitations, e.g., overreliance on large training datasets and poor performance in extrapolating beyond observations. Geotechnical monitoring data at specific sites are multi-sourced (e.g., settlement and horizontal displacement data). Moreover, each source of monitoring data may vary temporally and be sparsely measured, e.g., within a two-dimensional (2D) geological cross-section. Thus, effectively learning multi-source and limited monitoring data is difficult using existing ML methods. To address this challenge, this study proposes a physics-informed ML method combining geotechnical numerical models (e.g., the finite element model (FEM)) with sparse dictionary learning (SDL) methods. It uses a range of probable numerical model results corresponding to multiple, temporally varying responses in a 2D spatial range to construct a “dictionary” in SDL and employs multi-source and limited monitoring data to identify a few “atoms” from the constructed dictionary to improve model predictions. An engineering project for embankment construction is used to illustrate the proposed approach. Our results indicate that the proposed approach effectively integrates FEM results with multi-source and sparsely measured monitoring data and improves the predictions of multiple spatio-temporally varying geotechnical responses significantly, particularly those at subsequent time steps and unmonitored spatial locations within a 2D vertical cross-section.

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Soil Improvement of Alluvial Deposits Under High-Speed Railway Embankment: Field Case Study

Mridakh

Lahlou

Ejjaaouani³

et al. 2019

Geotech Geol Eng

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Numerical prediction and back-calculation of time-dependent behaviour of Ballina test embankment

Cited by 22 publications

References 6 publications

Uncertainty Analysis in Prediction of Settlements for Spatial Prefabricated Vertical Drains Improved Soft Soil Sites

Uncertainty Analysis in Prediction of Settlements for Spatial Prefabricated Vertical Drains Improved Soft Soil Sites

Physics-informed machine learning of multi-source monitoring data sparsely measured within a 2D vertical cross-section

Soil Improvement of Alluvial Deposits Under High-Speed Railway Embankment: Field Case Study

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