Abstract

In Karst areas, sinkholes are a major geohazard that creates damage and economic loss to building and civil infrastructure. The main goal of the proposed study is to develop a probabilistic spatio-magnitude sinkhole susceptibility model that predicts the potential of sinkhole occurrence on a regional scale. The dissertation presents statistical techniques such as frequency ratio (FR) and logistic regression (LR), and a machine learning technique such as artificial neural network (ANN) to develop the sinkhole hazard models. The study area was limited to the East Central Floria (ECF) region. Seven hydrogeological factors were identified as key contributing factors to sinkhole occurrence and they were used as input variables to the sinkhole hazard model. The seven factors include hydraulic head difference between surficial and upper Florida aquifers, groundwater recharge rate to the upper Floridan aquifer, soil permeability, overburden thickness, surficial aquifer system (SAS) thickness, intermediate aquifer system (IAS) thickness, and proximity to karst features. Both FR and LR were used to construct the spatial prediction models for sinkhole susceptibility mapping. In addition, ANN was employed to develop a probabilistic spatio-magnitude sinkhole hazard model. The prediction accuracy of all models was validated by area under the receiver operating characteristic (ROC) curve (AUC) analysis. Subsequently, the probabilities of location and size of the sinkhole are computed and mapped on a regional scale by Geographical Information System (GIS) techniques.

Notes

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Graduation Date

2019

Semester

Fall

Advisor

Nam, Boo Hyun

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Civil, Environmental, and Construction Engineering

Degree Program

Civil Engineering

Format

application/pdf

Identifier

CFE0008283

Language

English

Release Date

June 2023

Length of Campus-only Access

3 years

Access Status

Doctoral Dissertation (Campus-only Access)

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