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 𝐴𝑑𝑦𝑒 and widths π›₯𝑧𝑑𝑦𝑒 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›₯𝑧𝑑𝑦𝑒 and 𝐴𝑑𝑦𝑒, respectively, with an overall relative error of Β±20%. The YOLOv8 model showed very good accuracy by achieving high precision 𝑃 = 0.99 and recall 𝑅=0.75 for both object detection and mask predictions.

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Apr 23rd, 5:00 PM

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 𝐴𝑑𝑦𝑒 and widths π›₯𝑧𝑑𝑦𝑒 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›₯𝑧𝑑𝑦𝑒 and 𝐴𝑑𝑦𝑒, respectively, with an overall relative error of Β±20%. The YOLOv8 model showed very good accuracy by achieving high precision 𝑃 = 0.99 and recall 𝑅=0.75 for both object detection and mask predictions.