Technical Papers
Feb 24, 2022

A Probabilistic Predictive Model for Foundation Settlement on Liquefiable Soils Improved with Ground Densification

Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 148, Issue 5

Abstract

In this paper, we present a probabilistic predictive procedure for a foundation’s permanent average settlement on liquefiable soils improved with ground densification. The proposed procedure is based on 770 three-dimensional (3D), fully coupled, effective-stress, finite-element analyses designed through quasi-Monte Carlo sampling of key input parameters. The numerical models are themselves calibrated and validated with centrifuge model studies, and they consider realistic, nonlinear, 3D structures on shallow foundations, seismic soil–structure interaction, interlayering and layer cross interactions, ground densification properties and geometry, and ground motion characteristics. We use nonlinear regression with lasso-type regularization to estimate model coefficients. The primary predictors of a foundation’s settlement are identified as the cumulative absolute velocity of the outcropping rock motion; total thickness of the soil deposit above bedrock and cumulative thickness of the critical liquefiable layer(s); the foundation’s bearing pressure, size, and embedment depth; the structure’s total height; the achieved density and size of ground improvement; and the thickness of the remaining undensified susceptible soils within the foundation’s influence zone. In the end, the predictive model is shown to capture the trends in a limited number of centrifuge and field case histories collected from the literature. The insight from the numerical database and the first-of-its-kind predictive model aims to guide the design of liquefaction mitigation strategies that improve the performance of the soil–foundation–structure system holistically and reliably.

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Data Availability Statement

All data, models, and code that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

The authors acknowledge support from the US National Science Foundation (NSF) under Grant 1454431. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF. This work utilized the Summit supercomputer, which is supported by the NSF (Awards ACI-1532235 and ACI-1532236), the University of Colorado Boulder, and Colorado State University.

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Go to Journal of Geotechnical and Geoenvironmental Engineering
Journal of Geotechnical and Geoenvironmental Engineering
Volume 148Issue 5May 2022

History

Received: Mar 31, 2021
Accepted: Dec 15, 2021
Published online: Feb 24, 2022
Published in print: May 1, 2022
Discussion open until: Jul 24, 2022

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Postdoctoral Research Fellow, Dept. of Civil, Architectural, and Environmental Engineering, Univ. of Texas, Austin, TX 78712. ORCID: https://orcid.org/0000-0002-2319-5336. Email: [email protected]
Zach Bullock, A.M.ASCE [email protected]
Assistant Professor, Dept. of Civil Engineering, Univ. of British Columbia, 6150 Applied Science Lane, Vancouver, BC, Canada V6T 1Z4. Email: [email protected]
Associate Professor, Dept. of Civil, Environmental, and Architectural Engineering, Univ. of Colorado Boulder, 1111 Engineering Dr., Campus Box 428, Boulder, CO 80309 (corresponding author). ORCID: https://orcid.org/0000-0002-7188-4208. Email: [email protected]
Abbie Liel, M.ASCE [email protected]
Professor, Dept. of Civil, Environmental, and Architectural Engineering, Univ. of Colorado Boulder, Boulder, CO 80309. Email: [email protected]

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Cited by

  • Influence of Stratigraphic Variability and Layering on Liquefiable Soils Near and Away from Structures, Geo-Congress 2024, 10.1061/9780784485316.040, (383-393), (2024).
  • Influence of Dense Granular Columns and Liquefiable Soil Stratigraphic Variations on the Performance of Overlying Structures, Geo-Congress 2024, 10.1061/9780784485316.008, (62-72), (2024).
  • A Machine Learning-Based Approach for Predicting Structural Settlement on Layered Liquefiable Soils Improved with Densification, Geo-Congress 2023, 10.1061/9780784484654.031, (297-307), (2023).
  • Performance-Based Assessment and Design of Structures on Liquefiable Soils: From Triggering to Consequence and Mitigation, Proceedings of the 4th International Conference on Performance Based Design in Earthquake Geotechnical Engineering (Beijing 2022), 10.1007/978-3-031-11898-2_21, (376-396), (2022).

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