Semester of Graduation
Summer 2023
Degree
Master of Science in Civil Engineering (MSCE)
Department
Department of Civil and Environmental Engineering
Document Type
Thesis
Abstract
The applicability of several Machine Learning (ML) models was explored in this research to predict the ultimate capacity and load-settlement behavior of axially loaded single-driven piles from Cone Penetration Test (CPT) data. Additionally, a common CPT-based soil behavior type (SBT) classification system was reproduced using those ML models. Eighty static pile load tests and corresponding CPT data close to those pile locations were collected from 34 sites in Louisiana for the deep foundation application. On the other hand, 70 CPT soundings were taken in 14 different parishes across Louisiana for the soil classification application. Specifically, tree-based ML models such as Decision Tree (DT), Random Forest (RF) and Gradient Boosted Tree (GBT) were developed and compared in predicting ultimate pile capacity. It was found that the GBT model performed best among the tree-based models. This GBT model was further compared with four conventional direct pile-CPT methods based on several statistical criteria, and in this comparison, the GBT model outranked the conventional methods. On the contrary, in addition to RF and GBT, an Artificial Neural Network (ANN) model was developed to predict load-settlement behavior. A comparison was made between these ML models based on several statistical criteria. Furthermore, these ML models were graphically compared with two common load-transfer methods in predicting actual static pile load test curves. All the ML models performed satisfactorily in predicting the load-settlement behavior. Finally, a common CPT-based SBT classification system was replicated using RF and GBT models. Six different input settings were explored, and a total of 12 models were developed. The model types included basic CPT parameters such as corrected cone tip resistance, sleeve friction, pore water pressure parameters and effective overburden pressure, as well as normalized CPT parameters. A comparison between all types of models was conducted based on several performance criteria, and it was found that GBT models with input settings comprising normalized parameters performed best among all the developed models. Hence, these findings support the use of ML models in predicting ultimate pile capacity and load-settlement behavior and replicating a CPT-based SBT classification system.
Date
5-18-2023
Recommended Citation
Shoaib, Mohammad Moontakim, "Exploring Machine Learning in Deep Foundation and Soil Classification Application" (2023). LSU Master's Theses. 5785.
https://repository.lsu.edu/gradschool_theses/5785
Committee Chair
Abu-Farsakh, Murad
DOI
10.31390/gradschool_theses.5785