Date of Defense
23-4-2025 5:00 PM
Location
F3-043
Document Type
Dissertation Defense
Degree Name
Doctor of Philosophy in Civil Engineering
College
COE
Department
Civil and Environmental Engineering
First Advisor
Prof. Mohamed Mostafa Mohamed
Keywords
Groundwater movement, fractured aquifers, fractured rock passive flux meter, G360MultiPort Sampler, water flux, contaminant flux, machine learning, deep learning, YOLOv8
Abstract
Groundwater and contaminant movement in fractured rock aquifers is highly variable. Its dependence on fracture apertures and orientation as well as fracture network interconnectivity is not well understood. This poses a challenge to the measurement of groundwater and contaminant fluxes, especially when using open-hole techniques, which significantly alter natural flow conditions by connecting different fractures along an open borehole or a well. In this work, the use of Fractured Rock Passive Flux Meter (FRPFM) with invisible tracer and visible dye component to measure groundwater fluxes and identify geometric fracture parameters is explored through laboratory experiments. The invisible tracer component results showed that water and contaminant fluxes were measured with relative errors of Β±25% and Β±14%, respectively. The results also showed that water flux was measured correctly by up to 50% of tracer loss, but beyond this point, the measurements became less accurate as tracer displacement rate declined. For the visible dye component, we used the deep learning model YOLOv8 to accurately identify the dye marks and measure their areas π΄ππ¦π and widths π₯π§ππ¦π from images of the dyed fabric. Results showed that groundwater fluxes were measured with relative errors of Β±23% and Β±16% based on π₯π§ππ¦π and π΄ππ¦π, respectively, with an overall relative error of Β±20%. The YOLOv8 model showed very good accuracy by achieving high precision π = 0.99 and recall π =0.75 for both object detection and mask predictions.
Included in
MEASUREMENT OF GROUNDWATER AND CONTAMINANT FLUXES IN FRACTURES USING A COMBINED SYSTEM OF PASSIVE FLUX METER AND MULTIPORT SAMPLER
F3-043
Groundwater and contaminant movement in fractured rock aquifers is highly variable. Its dependence on fracture apertures and orientation as well as fracture network interconnectivity is not well understood. This poses a challenge to the measurement of groundwater and contaminant fluxes, especially when using open-hole techniques, which significantly alter natural flow conditions by connecting different fractures along an open borehole or a well. In this work, the use of Fractured Rock Passive Flux Meter (FRPFM) with invisible tracer and visible dye component to measure groundwater fluxes and identify geometric fracture parameters is explored through laboratory experiments. The invisible tracer component results showed that water and contaminant fluxes were measured with relative errors of Β±25% and Β±14%, respectively. The results also showed that water flux was measured correctly by up to 50% of tracer loss, but beyond this point, the measurements became less accurate as tracer displacement rate declined. For the visible dye component, we used the deep learning model YOLOv8 to accurately identify the dye marks and measure their areas π΄ππ¦π and widths π₯π§ππ¦π from images of the dyed fabric. Results showed that groundwater fluxes were measured with relative errors of Β±23% and Β±16% based on π₯π§ππ¦π and π΄ππ¦π, respectively, with an overall relative error of Β±20%. The YOLOv8 model showed very good accuracy by achieving high precision π = 0.99 and recall π =0.75 for both object detection and mask predictions.