{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/140037"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/140037","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Probabilistic Characterization of Sediments Using a Combined Geotechnical and Geophysical Approach","abstract":"Reliable characterization of seabed surface sediments is critical for offshore engineering, naval applications, and understanding coastal sediment dynamics. Traditional geotechnical methods provide accurate point measurements but lack spatial continuity, while geophysical surveys offer broad coverage but yield indirect properties that are often difficult to link quantitatively to engineering parameters such as sediment strength or erodibility. This dissertation addresses these limitations by developing and integrating novel probabilistic frameworks utilizing data from Portable Free Fall Penetrometers (PFFP) and Chirp sub-bottom profilers for enhanced shallow-water sediment assessment. First, a probabilistic machine learning model, combining Random Forest and a Bayesian 1D Convolutional Neural Network, is developed to classify sediments based on full PFFP deceleration profiles. This approach moves beyond deterministic methods by providing robust classifications across four behavior types with quantified uncertainty bounds. Second, acknowledging the complementary strengths of geotechnical and geophysical data, a novel data fusion framework based on Gaussian Process Regression and Bayesian methods is introduced. This framework quantitatively integrates the probabilistic classifications derived from sparse, high-accuracy PFFP measurements with continuous, lower-certainty classifications obtained from Chirp sonar data via geophysical inversion. The result is a unified, spatially continuous sediment profile along survey transects with significantly reduced and quantified uncertainty compared to using either data source alone, demonstrated through field case studies. Third, the research establishes a direct, data-driven link between rapid in-situ PFFP measurements and sediment erodibility, specifically the critical shear stress ($tau_c$). A two-step probabilistic model is developed, first correlating PFFP deceleration with sediment grain composition (fines-sand ratio) and subsequently linking this composition to $tau_c$ values derived from laboratory erosion tests. This framework provides the first probabilistic estimates of erodibility directly from PFFP data, highlighting the controlling influence of the fines-sand ratio and revealing significantly higher variability in the erodibility of mixed sediments. Collectively, this dissertation delivers an integrated suite of probabilistic tools that leverage PFFP and Chirp sonar data for rapid, spatially comprehensive, and uncertainty-aware characterization of surficial seabed sediments. These advancements enhance the interpretation of PFFP data, enable robust fusion with geophysical surveys, and provide novel means to assess sediment erodibility, contributing significantly to improved site investigations and predictive capabilities in dynamic coastal and estuarine environments.","abstract_html":"Reliable characterization of seabed surface sediments is critical for offshore engineering, naval applications, and understanding coastal sediment dynamics. Traditional geotechnical methods provide accurate point measurements but lack spatial continuity, while geophysical surveys offer broad coverage but yield indirect properties that are often difficult to link quantitatively to engineering parameters such as sediment strength or erodibility. This dissertation addresses these limitations by developing and integrating novel probabilistic frameworks utilizing data from Portable Free Fall Penetrometers (PFFP) and Chirp sub-bottom profilers for enhanced shallow-water sediment assessment. First, a probabilistic machine learning model, combining Random Forest and a Bayesian 1D Convolutional Neural Network, is developed to classify sediments based on full PFFP deceleration profiles. This approach moves beyond deterministic methods by providing robust classifications across four behavior types with quantified uncertainty bounds. Second, acknowledging the complementary strengths of geotechnical and geophysical data, a novel data fusion framework based on Gaussian Process Regression and Bayesian methods is introduced. This framework quantitatively integrates the probabilistic classifications derived from sparse, high-accuracy PFFP measurements with continuous, lower-certainty classifications obtained from Chirp sonar data via geophysical inversion. The result is a unified, spatially continuous sediment profile along survey transects with significantly reduced and quantified uncertainty compared to using either data source alone, demonstrated through field case studies. Third, the research establishes a direct, data-driven link between rapid in-situ PFFP measurements and sediment erodibility, specifically the critical shear stress (<span class=\"etd-inline-math\">tau<sub>c</sub></span>). A two-step probabilistic model is developed, first correlating PFFP deceleration with sediment grain composition (fines-sand ratio) and subsequently linking this composition to <span class=\"etd-inline-math\">tau<sub>c</sub></span> values derived from laboratory erosion tests. This framework provides the first probabilistic estimates of erodibility directly from PFFP data, highlighting the controlling influence of the fines-sand ratio and revealing significantly higher variability in the erodibility of mixed sediments. Collectively, this dissertation delivers an integrated suite of probabilistic tools that leverage PFFP and Chirp sonar data for rapid, spatially comprehensive, and uncertainty-aware characterization of surficial seabed sediments. These advancements enhance the interpretation of PFFP data, enable robust fusion with geophysical surveys, and provide novel means to assess sediment erodibility, contributing significantly to improved site investigations and predictive capabilities in dynamic coastal and estuarine environments.","abstract_has_math":true,"creators":["Rahman, Md Rejwanur"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Civil Engineering","degree_department":"Civil and Environmental Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Rodriguez-Marek, Adrian","Stark, Nina"],"committee_members":["Castellanos, Bernardo Antonio","Beemer, Ryan D.","Dorgan, Kelly"],"year":2025,"date_issued":"2025-12-18","date_published":"2025-12-18","updated_at":"2026-07-22T22:20:18Z","subjects":["Sediment Characterization","Marine Geotechnical Engineering"],"languages":["en"],"rights":["Creative Commons Attribution-NonCommercial 4.0 International"],"rights_urls":["http://creativecommons.org/licenses/by-nc/4.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45095"],"render_values":[{"text":"vt_gsexam:45095","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/140037","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Rodriguez-Marek, Adrian","Stark, Nina"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Castellanos, Bernardo Antonio","Beemer, Ryan D.","Dorgan, Kelly"]},{"key":"dc:contributor.department","label":"Department","values":["Civil and Environmental Engineering"]},{"key":"dc:creator","label":"Author","values":["Rahman, Md Rejwanur"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-12-19T09:00:36Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-12-19T09:00:36Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-12-18"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Sediment Characterization","Marine Geotechnical Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Creative Commons Attribution-NonCommercial 4.0 International"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://creativecommons.org/licenses/by-nc/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45095"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/140037"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Reliable characterization of seabed surface sediments is critical for offshore engineering, naval applications, and understanding coastal sediment dynamics. Traditional geotechnical methods provide accurate point measurements but lack spatial continuity, while geophysical surveys offer broad coverage but yield indirect properties that are often difficult to link quantitatively to engineering parameters such as sediment strength or erodibility. This dissertation addresses these limitations by developing and integrating novel probabilistic frameworks utilizing data from Portable Free Fall Penetrometers (PFFP) and Chirp sub-bottom profilers for enhanced shallow-water sediment assessment. First, a probabilistic machine learning model, combining Random Forest and a Bayesian 1D Convolutional Neural Network, is developed to classify sediments based on full PFFP deceleration profiles. This approach moves beyond deterministic methods by providing robust classifications across four behavior types with quantified uncertainty bounds. Second, acknowledging the complementary strengths of geotechnical and geophysical data, a novel data fusion framework based on Gaussian Process Regression and Bayesian methods is introduced. This framework quantitatively integrates the probabilistic classifications derived from sparse, high-accuracy PFFP measurements with continuous, lower-certainty classifications obtained from Chirp sonar data via geophysical inversion. The result is a unified, spatially continuous sediment profile along survey transects with significantly reduced and quantified uncertainty compared to using either data source alone, demonstrated through field case studies. Third, the research establishes a direct, data-driven link between rapid in-situ PFFP measurements and sediment erodibility, specifically the critical shear stress ($tau_c$). A two-step probabilistic model is developed, first correlating PFFP deceleration with sediment grain composition (fines-sand ratio) and subsequently linking this composition to $tau_c$ values derived from laboratory erosion tests. This framework provides the first probabilistic estimates of erodibility directly from PFFP data, highlighting the controlling influence of the fines-sand ratio and revealing significantly higher variability in the erodibility of mixed sediments. Collectively, this dissertation delivers an integrated suite of probabilistic tools that leverage PFFP and Chirp sonar data for rapid, spatially comprehensive, and uncertainty-aware characterization of surficial seabed sediments. These advancements enhance the interpretation of PFFP data, enable robust fusion with geophysical surveys, and provide novel means to assess sediment erodibility, contributing significantly to improved site investigations and predictive capabilities in dynamic coastal and estuarine environments."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Knowing what's on the bottom of our oceans, rivers, and bays is important. Think about building bridges, setting up offshore wind farms, keeping shipping channels clear, or figuring out how coastlines might erode during storms. All these activities depend on understanding the seabed – whether it's soft mud, shifting sand, or packed clay. Getting this information helps us build safer infrastructures, manage waterways better, and protect coastal areas. But mapping the seabed over large areas is a challenge. One way is to take physical samples or deploy instruments to test the bottom directly in a few spots. This gives very accurate information for those specific locations, but it's slow, expensive, and doesn't tell us much about the areas in between, especially if the conditions are energetic or changing quickly. It's like trying to understand a whole park by looking closely at just a few square feet of grass. Another method uses sound waves, much like a fish finder. Tools like Chirp sonar send sound pulses down, and by listening to the echoes bouncing off the bottom and the layers underneath, we can make maps showing the underwater landscape. This method is fast and covers a lot of ground, giving a continuous picture. However, these echoes are like shadows – they give us clues, but figuring out exactly what type of material is down there (like telling different kinds of sand and mud apart) or how easily it might get washed away just from the sound echoes can be uncertain. This research aimed to find better ways to map the seabed by combining the best parts of both methods using smart computer analysis. We focused on two main tools: the Portable Free Fall Penetrometer (PFFP), which is like a small, weighted dart dropped into the water that measures how hard the seabed is by how quickly it slows down, and the Chirp sonar, which uses sound to map the layers below the seabed. Our work led to three main improvements. First, we taught a computer (using Artificial Intelligence) to be much better at understanding the PFFP results. Instead of just looking at simple numbers, the computer program learned to analyze the entire pattern of how the PFFP slowed down as it sank into the seabed. By comparing these patterns to locations where we already knew the sediment type from lab tests, the program can now predict the sediment type (like sandy, muddy, or mixed) more reliably just from the PFFP data. It also tells us how confident it is in its prediction, which is crucial for making decisions. Second, we developed a new way to combine the detailed PFFP information (from specific spots) with the broader Chirp sonar maps (covering the whole area). Think of the PFFP data points as trustworthy landmarks. Our method uses these landmarks to help correct and improve the interpretation of the sonar map in the areas between the PFFP drops. It blends the information from both tools, considering how reliable each one is, to create a single, continuous map of the seabed type that's much more accurate than using either tool alone. This combined map also shows where our understanding is strong and where it's still a bit uncertain. Third, we found a way to use the PFFP measurements to quickly estimate how easily the seabed sediment might be eroded or washed away by currents or waves. We discovered a link between how the PFFP slows down, the mix of sand and mud in the sediment, and results from lab tests that directly measure erodibility. Now, a quick PFFP test in the field can give us a good idea of how stable the seabed is in that location, helping us identify potential erosion hotspots much faster than before and guiding where more detailed lab tests might be needed. In short, this research provides new, practical tools for getting a clearer picture of what's under the water in our coastal areas. By combining field instruments in smarter ways and using computer analysis that accounts for uncertainty, we can get faster, more reliable, and more complete information about the seabed. This helps engineers, scientists, and planners make better decisions to safely use and protect our important underwater environments."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Doctor of Philosophy"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Probabilistic Characterization of Sediments Using a Combined Geotechnical and Geophysical Approach"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Rodriguez-Marek, Adrian","Stark, Nina"],"dc:contributor.committeemember":["Castellanos, Bernardo Antonio","Beemer, Ryan D.","Dorgan, Kelly"],"dc:contributor.department":["Civil and Environmental Engineering"],"dc:creator":["Rahman, Md Rejwanur"],"dc:date.accessioned":["2025-12-19T09:00:36Z"],"dc:date.available":["2025-12-19T09:00:36Z"],"dc:date.issued":["2025-12-18"],"dc:description.abstract":["Reliable characterization of seabed surface sediments is critical for offshore engineering, naval applications, and understanding coastal sediment dynamics. Traditional geotechnical methods provide accurate point measurements but lack spatial continuity, while geophysical surveys offer broad coverage but yield indirect properties that are often difficult to link quantitatively to engineering parameters such as sediment strength or erodibility. This dissertation addresses these limitations by developing and integrating novel probabilistic frameworks utilizing data from Portable Free Fall Penetrometers (PFFP) and Chirp sub-bottom profilers for enhanced shallow-water sediment assessment. First, a probabilistic machine learning model, combining Random Forest and a Bayesian 1D Convolutional Neural Network, is developed to classify sediments based on full PFFP deceleration profiles. This approach moves beyond deterministic methods by providing robust classifications across four behavior types with quantified uncertainty bounds. Second, acknowledging the complementary strengths of geotechnical and geophysical data, a novel data fusion framework based on Gaussian Process Regression and Bayesian methods is introduced. This framework quantitatively integrates the probabilistic classifications derived from sparse, high-accuracy PFFP measurements with continuous, lower-certainty classifications obtained from Chirp sonar data via geophysical inversion. The result is a unified, spatially continuous sediment profile along survey transects with significantly reduced and quantified uncertainty compared to using either data source alone, demonstrated through field case studies. Third, the research establishes a direct, data-driven link between rapid in-situ PFFP measurements and sediment erodibility, specifically the critical shear stress ($tau_c$). A two-step probabilistic model is developed, first correlating PFFP deceleration with sediment grain composition (fines-sand ratio) and subsequently linking this composition to $tau_c$ values derived from laboratory erosion tests. This framework provides the first probabilistic estimates of erodibility directly from PFFP data, highlighting the controlling influence of the fines-sand ratio and revealing significantly higher variability in the erodibility of mixed sediments. Collectively, this dissertation delivers an integrated suite of probabilistic tools that leverage PFFP and Chirp sonar data for rapid, spatially comprehensive, and uncertainty-aware characterization of surficial seabed sediments. These advancements enhance the interpretation of PFFP data, enable robust fusion with geophysical surveys, and provide novel means to assess sediment erodibility, contributing significantly to improved site investigations and predictive capabilities in dynamic coastal and estuarine environments."],"dc:description.abstractgeneral":["Knowing what's on the bottom of our oceans, rivers, and bays is important. Think about building bridges, setting up offshore wind farms, keeping shipping channels clear, or figuring out how coastlines might erode during storms. All these activities depend on understanding the seabed – whether it's soft mud, shifting sand, or packed clay. Getting this information helps us build safer infrastructures, manage waterways better, and protect coastal areas. But mapping the seabed over large areas is a challenge. One way is to take physical samples or deploy instruments to test the bottom directly in a few spots. This gives very accurate information for those specific locations, but it's slow, expensive, and doesn't tell us much about the areas in between, especially if the conditions are energetic or changing quickly. It's like trying to understand a whole park by looking closely at just a few square feet of grass. Another method uses sound waves, much like a fish finder. Tools like Chirp sonar send sound pulses down, and by listening to the echoes bouncing off the bottom and the layers underneath, we can make maps showing the underwater landscape. This method is fast and covers a lot of ground, giving a continuous picture. However, these echoes are like shadows – they give us clues, but figuring out exactly what type of material is down there (like telling different kinds of sand and mud apart) or how easily it might get washed away just from the sound echoes can be uncertain. This research aimed to find better ways to map the seabed by combining the best parts of both methods using smart computer analysis. We focused on two main tools: the Portable Free Fall Penetrometer (PFFP), which is like a small, weighted dart dropped into the water that measures how hard the seabed is by how quickly it slows down, and the Chirp sonar, which uses sound to map the layers below the seabed. Our work led to three main improvements. First, we taught a computer (using Artificial Intelligence) to be much better at understanding the PFFP results. Instead of just looking at simple numbers, the computer program learned to analyze the entire pattern of how the PFFP slowed down as it sank into the seabed. By comparing these patterns to locations where we already knew the sediment type from lab tests, the program can now predict the sediment type (like sandy, muddy, or mixed) more reliably just from the PFFP data. It also tells us how confident it is in its prediction, which is crucial for making decisions. Second, we developed a new way to combine the detailed PFFP information (from specific spots) with the broader Chirp sonar maps (covering the whole area). Think of the PFFP data points as trustworthy landmarks. Our method uses these landmarks to help correct and improve the interpretation of the sonar map in the areas between the PFFP drops. It blends the information from both tools, considering how reliable each one is, to create a single, continuous map of the seabed type that's much more accurate than using either tool alone. This combined map also shows where our understanding is strong and where it's still a bit uncertain. Third, we found a way to use the PFFP measurements to quickly estimate how easily the seabed sediment might be eroded or washed away by currents or waves. We discovered a link between how the PFFP slows down, the mix of sand and mud in the sediment, and results from lab tests that directly measure erodibility. Now, a quick PFFP test in the field can give us a good idea of how stable the seabed is in that location, helping us identify potential erosion hotspots much faster than before and guiding where more detailed lab tests might be needed. In short, this research provides new, practical tools for getting a clearer picture of what's under the water in our coastal areas. By combining field instruments in smarter ways and using computer analysis that accounts for uncertainty, we can get faster, more reliable, and more complete information about the seabed. This helps engineers, scientists, and planners make better decisions to safely use and protect our important underwater environments."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:45095"],"dc:identifier.uri":["https://hdl.handle.net/10919/140037"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["Creative Commons Attribution-NonCommercial 4.0 International"],"dc:rights.uri":["http://creativecommons.org/licenses/by-nc/4.0/"],"dc:subject":["Sediment Characterization","Marine Geotechnical Engineering"],"dc:title":["Probabilistic Characterization of Sediments Using a Combined Geotechnical and Geophysical Approach"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:18Z"}