Using Machine Learning to Predict Consolidation Parameters
Publication: Geo-Congress 2024
ABSTRACT
While one-dimensional consolidation tests remain the benchmark for performing site-specific evaluations of consolidation characteristics, the time required to perform these tests has resulted in significant research interest in correlating consolidation and recompression indices to more readily available properties such as natural moisture content or liquid limit. Recent advances in machine learning provide the ability to analyze and draw inferences from a combined range of soil properties to better estimate the consolidation and recompression indices. This study utilizes a dataset of consolidation tests (n = 1,261) performed primarily on Holocene age clay and organic materials within the New Orleans area to evaluate the applicability and use of various machine learning algorithms, including multivariate ordinary least squares and random forest regressors. The results of these models are compared to traditional univariate linear regressions and provide commentary on the use of machine learning to statistically treat the variability inherent in geotechnical testing.
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Information & Authors
Information
Published In
Geo-Congress 2024
Pages: 445 - 453
History
Published online: Feb 22, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Benchmark
- Business management
- Computer programming
- Computing in civil engineering
- Consolidated soils
- Engineering fundamentals
- Geomechanics
- Geotechnical engineering
- Hydrologic engineering
- Hydrologic properties
- Hydrology
- Management methods
- Mathematics
- Parameters (statistics)
- Practice and Profession
- Soil analysis
- Soil mechanics
- Soil properties
- Soil tests
- Soils (by type)
- Statistics
- Tests (by type)
- Water and water resources
- Water content
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Metrics & Citations
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