Modeling loess collapsibility using oedometer tests for foundation and pile settlement prediction

To enhance the construction foundation safety in the loess regions of Northwest China, this study systematically investigated the collapsibility characteristics of loess from a construction site in Lanzhou through laboratory collapse tests under constant pressure. Regression modeling was employed to quantitatively evaluate the influence of key geotechnical parameters-including natural density, dry density, moisture content, void ratio, and compression modulus-on the collapsibility coefficient. Significant relationships were established between the collapsibility coefficient and void ratio, natural moisture content, and compression modulus. Furthermore, the standard method was used to determine the variation of potential collapse around piles with depth, revealing an exponential distribution trend. It is worth emphasizing that this study successfully developed an exponential function model based on easily obtainable parameters (natural density, dry density, moisture content, and compression modulus) for quantitatively predicting the collapsibility coefficient. In contrast to existing models, which often rely on single indicators and lack a multi-parameter collaborative evaluation system-resulting in limited engineering applicability-this study integrated five parameters to construct a comprehensive prediction model, filling the gap in multi-index evaluation systems. This model provides a practical tool for more accurately predicting loess collapse settlement and can be directly applied to risk assessment and mitigation design for building foundations and pile-soil interactions in similar loess environments in Northwest China.

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Publication Details

Journal
Scientific Reports
Published
2025-11-26
DOI
https://doi.org/10.1038/s41598-025-26250-3
Citations
2
Primary Topic
Soil and Unsaturated Flow
Type
article
Field-Weighted Citation Impact
1.91
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article

Modeling loess collapsibility using oedometer tests for foundation and pile settlement prediction

J B Mu, Laping He, Yanpeng Zhu, Xiaohui Li et al.
2 citations
Scientific Reports
Soil and Unsaturated Flow
1.91
article

Modeling loess collapsibility using oedometer tests for foundation and pile settlement prediction

J B Mu, Laping He, Yanpeng Zhu, Xiaohui Li, Hao Yuan, Long Zhao, Shuaihua Ye
article en
2 citations

Abstract

To enhance the construction foundation safety in the loess regions of Northwest China, this study systematically investigated the collapsibility characteristics of loess from a construction site in Lanzhou through laboratory collapse tests under constant pressure. Regression modeling was employed to quantitatively evaluate the influence of key geotechnical parameters-including natural density, dry density, moisture content, void ratio, and compression modulus-on the collapsibility coefficient. Significant relationships were established between the collapsibility coefficient and void ratio, natural moisture content, and compression modulus. Furthermore, the standard method was used to determine the variation of potential collapse around piles with depth, revealing an exponential distribution trend. It is worth emphasizing that this study successfully developed an exponential function model based on easily obtainable parameters (natural density, dry density, moisture content, and compression modulus) for quantitatively predicting the collapsibility coefficient. In contrast to existing models, which often rely on single indicators and lack a multi-parameter collaborative evaluation system-resulting in limited engineering applicability-this study integrated five parameters to construct a comprehensive prediction model, filling the gap in multi-index evaluation systems. This model provides a practical tool for more accurately predicting loess collapse settlement and can be directly applied to risk assessment and mitigation design for building foundations and pile-soil interactions in similar loess environments in Northwest China.

Scientific ReportsVol. 15(1)
Lanzhou University of Technology (CN), Gansu Institute of Political Science and Law (CN)
Openalex Percentile: Top 12%
Soil and Unsaturated Flow
1.91
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