Engineering Commons^™

Exploring Machine Learning In Deep Foundation And Soil Classification Application, Mohammad Moontakim Shoaib

LSU Master's Theses

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 …

A Machine Learning Approach To Robotic Additive Manufacturing Of Uv-Curable Polymers Using Direct Ink Writing, Luis A. Velazquez

LSU Master's Theses

This thesis presents the design and implementation of a robotic additive manufacturing system that uses ultraviolet (UV)-curable thermoset polymers. Its design considers future applications involving free-standing 3D printing by means of partial UV curing and the fabrication of samples that are reinforced with fillers or fibers to manufacture complex-shape objects.

The proposed setup integrates a custom-built extruder with a UR5e collaborative manipulator. The capabilities of the system were demonstrated using Anycubic resin formulations containing fumed silica (FS) at varying weight fractions from 2.8 to 8 wt%. To fully cure the specimens after fabrication, a UV chamber was used. Then, measurements …

Enhanced Grain Partitioning Of X-Ray Microtomography Segmented Images, Nicholas C. Skrivanos Ii

LSU Master's Theses

In the field of petroleum engineering, rock samples are often taken from wells during the drilling process. Grain partitioning of digital three-dimensional microtomography segmented images obtained from these samples provides valuable in-situ properties and statistics that allow for accurate particle and structure characterization. This information can be used directly in detailed production and reservoir analysis, and can also be used to generate realistic packing models for advanced simulation. Additionally, the partitioned image can be used as a building block for realistic hydraulic fracture modeling. This technology has applications in other fields as well, such as core analysis in soil sciences …

Quantitative Estimation Of Causality And Predictive Modeling For Precipitation Observation Sites And River Gage Sensors, Tri Vu Nguyen

LSU Master's Theses

This project seeks to investigate two questions: correlations from precipitation measurement sensors to river gage sensors, and predictive modeling of peak river gage heights during precipitation events. First, if correlations can be quantified, then a predictive model can be explored to predict peak water levels at river gage sensors, in response to precipitation inputs. Answering both research questions can provide early flood detection benefits and provide quantitative time assessments for flood risks. An extensive data-driven study was conducted across a geographical area of the U.S, spanning the time period 2008-2016 to identify river gage sensors that are closely correlated to …