Chapter
Feb 22, 2024
Predicting Standard Penetration Test (SPT) N-Values Using Drilling Response Measurements Collected during the Installation of Rigid Inclusions
Authors: Venkata Prashanth Muppana, P.E. [email protected], Venkata Phani Satti Raju Balusu [email protected], John H. Myers, P.E. [email protected], and Sonia Sorabella Swift, P.E. [email protected]Author Affiliations
Publication: Geo-Congress 2024
ABSTRACT
This paper evaluates the performance of supervised machine learning (ML) algorithms in predicting standard penetration test (SPT) N-values. The study utilized drilling response measurements (DRM) collected from the installation of rigid inclusions at a highly heterogeneous municipal solid waste (MSW) site. A range of ML algorithms were evaluated in this paper, including linear regression, decision trees, random forests, support vector machines, gradient boosting, and artificial neural networks. The ML algorithms were trained and evaluated on DRM data along with SPT boring data available for the project. The results of this study showed that ML algorithms can be used to predict SPT N-values to give rudimentary information about the relative density of the soil from DRM data.
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REFERENCES
Bunieski Menard, S. (2021). Practical methodologies to apply machine learning algorithms to ground improvement data Méthodes pratiques pour appliquer des algorithmes de machine learning sur des données d’amélioration de sol.
García, E. M., Alberti, M. G., and Arcos Álvarez, A. A. (2022). Measurement-While-Drilling Based Estimation of Dynamic Penetrometer Values Using Decision Trees and Random Forests. Applied Sciences (Switzerland), 12(9). https://doi.org/10.3390/app12094565.
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Published In
Geo-Congress 2024
Pages: 145 - 154
History
Published online: Feb 22, 2024
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ASCE Technical Topics:
- Algorithms
- Artificial intelligence and machine learning
- Chemical properties
- Chemistry
- Coasts, oceans, ports, and waterways engineering
- Computer programming
- Computing in civil engineering
- Engineering fundamentals
- Environmental engineering
- Geotechnical engineering
- Geotechnical investigation
- Heterogeneity
- Mathematics
- Municipal wastes
- Ocean engineering
- Offshore platforms
- Offshore structures
- Penetration tests
- Pollutants
- Waste management
- Waste sites
- Wastes
Authors
Affiliations
1Menard USA, Tampa, FL. Email: [email protected]
2Standard Chartered Bank GBS, Chennai, Tamil Nadu, India. Email: [email protected]
3Menard USA, Richmond, VA. Email: [email protected]
4Menard USA, Woburn, MA. Email: [email protected]
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.