Doctor of Philosophy (PhD)


Civil and Environmental Engineering

Document Type



Piezocone Penetration Tests (CPTu) are widely used in engineering practice. Because of its wide application among engineering companies, and government agencies such as Louisiana Coastal Protection and Restoration Authority (CPRA) and United States Army Corps of Engineers (USACE) for geotechnical investigations encompassing all types of projects, there is an enormous database comprising CPTu derived and soil boring parameters. Both datasets are used in conjunction to derive geotechnical properties and are often co-located (within 150 m), which enables comparison between CPTu and soil boring parameters. Furthermore, with the advance of machine learning methods in geotechnical engineering, the capacity to understand the intricate behavior of organic soils and access to deriving trends and patterns is within reach. This dissertation aims to expand the application of CPTu as follows: (1) investigate and quantify the impact of sample disturbance and geological factors on embankment design and construction, particularly in coastal regions by the assessment of shear strength derived from CPTu parameters, (2) develop a customized model tailored to site-specific conditions utilizing fundamental CPTu parameters, data from soil borings, and machine learning algorithms to differentiate between organic and inorganic soils, (3) establish correlations between CPTu data and soil boring information using Artificial Neural Networks (ANNs) to predict unit weight values for both organic and inorganic soils accurately, and (4) integrate geotechnical, microbial, and ecological parameters to create time series trajectories for marsh restoration cells providing a comprehensive understanding of the evolution of these ecosystems over time. This dissertation provides examples of how applications of CPTu in organic soils in coastal Louisiana can be expanded.



Committee Chair

Jafari, Navid H.

Available for download on Sunday, October 25, 2026