Mina Zamanian

ORCID Identifier(s)


Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Civil Engineering


Civil Engineering

First Advisor

Mohsen Shahandashti


ABSTRACT: A successful design and construction of infrastructure systems such as highways and bridges highly depend on accurate estimation of geotechnical properties and understanding their spatial distributions, especially in reliability-based designs such as the load and resistance factor design (LRFD) method. Insufficient and inaccurate subsurface information has a major contribution to cost overruns and delays in up to 50% of all infrastructure projects. Insufficient site investigation may also contribute to inadequate or conservative designs, leading to costly failures or increased project costs. Hence, geophysical methods, such as electrical resistivity imaging, that can potentially transform the existing subsurface investigations are used to develop tools for subsurface characterization based on data analytic approaches. The main objective of this study is to assess the validity of the developed linear regressions in the literature by empirically evaluating one of the critical assumptions of linear regressions – independence of regression residuals. This research argues that linear regression analysis must not be used for defining the relationships between electrical resistivity and geotechnical properties since it may lead to misleading information about the subsurface conditions. First, to achieve this objective, linear regression analysis was performed on an experimental dataset to identify the impacts of geotechnical properties on electrical resistivity variations. Second, a problem was articulated with the aim of investigating whether any spatial correlation exists between geotechnical properties and electrical resistivity values. A spatial regression model was then developed that best explains the spatial variability of electrical resistivity values with the variations of geotechnical properties. The second objective of this study was to provide practical recommendations for extracting useful information from complex and non-linear interactions between geotechnical properties and electrical resistivity values using machine learning techniques with deep structures such as deep learning. The proposed approach for characterizing the soil conditions using deep learning outperformed the existing methods used in the literature. This study identified a new research direction in the future for studying the relationships between geoelectrical and geotechnical properties through the investigation and quantification of the spatial relationships between these properties in clayey soils. The proposed approach helps create and use spatial regression models for a given site to determine the spatial distribution of geotechnical properties at each point (not necessarily those sampled using conventional site investigation methods) and conduct reliability analysis accordingly. The proposed analytical framework based on the deep learning technique also allows transportation agencies to have a better understanding of the effects of geotechnical properties on the variability of electrical resistivity values to obtain more reliable assessments of the subsurface characteristics.


Geotechnical properties, Advanced geophysical methods, Electrical resistivity imaging, Spatial associations, Complex relationships


Civil and Environmental Engineering | Civil Engineering | Engineering


Degree granted by The University of Texas at Arlington

Available for download on Thursday, August 01, 2024