{"id":{"repo_id":"iastate","oai_identifier":"oai:dr.lib.iastate.edu:20.500.12876/106540"},"canonical_url":"https://search.dev.ndltd.org/etd/iastate/oai:dr.lib.iastate.edu:20.500.12876/106540","repository":{"repo_id":"iastate","name":"Iowa State University","base_url":"https://dr.lib.iastate.edu/server/oai/request"},"display":{"title":"Sustainable advancements in rural infrastructure: Enhancing gravel road performance, stabilization, and intelligent monitoring","abstract":"Sustainable gravel road management is essential for maintaining rural infrastructure reliability, safety, and environmental quality. However, persistent challenges, including excessive dust emissions, rapid surface deterioration, and inefficient monitoring, hinder effective maintenance. This dissertation addresses these challenges by developing an integrated context that combines predictive modeling, chemical stabilization, and a framework proposing an AI-driven monitoring approach to enhance gravel road performance and longevity.The first study establishes a predictive model for dust generation, correlating vehicle speed (25–50 mph) and surface fines content with emissions using field-collected data from Iowa gravel roads. The model provides actionable thresholds for dust suppression, enabling agencies to implement targeted speed controls and material treatments. While accurate for typical conditions, performance varied under extreme weather, suggesting opportunities to incorporate additional environmental variables. The second and third studies evaluates chemical stabilizers (ionic, enzymatic, and water-absorbent solutions) through lab and field experiments. Results demonstrate that ionic stabilizers paired with gradation-controlled aggregates improve durability by 35–50% compared to untreated sections. A cost-benefit analysis reveals optimal stabilizer selections for different traffic and climate conditions, though long-term (>1-year) performance requires further study. The fourth study proposes a framework that utilizes AI-powered monitoring system using smartphone-captured images and deep learning models to automate distress detection (potholes, rutting, loose aggregate) with 89–94% accuracy. Integrated GIS mapping enables real-time condition assessment and prioritization. Challenges include model adaptability to seasonal changes, highlighting the need for adaptive training datasets. The final contribution synthesizes these advancements into a holistic management framework. By combining dust prediction, stabilizer selection tools, and AI monitoring, the framework empowers agencies to optimize maintenance planning and resource allocation. Theoretical contributions include novel methodologies for gravel road modeling and condition assessment, while practical methods offer scalable solutions for rural agencies. Future work should explore regional adaptability which bridges critical gaps in gravel road management, promoting sustainable, data-driven infrastructure practices for gravel road rural networks.","abstract_html":"Sustainable gravel road management is essential for maintaining rural infrastructure reliability, safety, and environmental quality. However, persistent challenges, including excessive dust emissions, rapid surface deterioration, and inefficient monitoring, hinder effective maintenance. This dissertation addresses these challenges by developing an integrated context that combines predictive modeling, chemical stabilization, and a framework proposing an AI-driven monitoring approach to enhance gravel road performance and longevity.The first study establishes a predictive model for dust generation, correlating vehicle speed (25–50 mph) and surface fines content with emissions using field-collected data from Iowa gravel roads. The model provides actionable thresholds for dust suppression, enabling agencies to implement targeted speed controls and material treatments. While accurate for typical conditions, performance varied under extreme weather, suggesting opportunities to incorporate additional environmental variables. The second and third studies evaluates chemical stabilizers (ionic, enzymatic, and water-absorbent solutions) through lab and field experiments. Results demonstrate that ionic stabilizers paired with gradation-controlled aggregates improve durability by 35–50% compared to untreated sections. A cost-benefit analysis reveals optimal stabilizer selections for different traffic and climate conditions, though long-term (>1-year) performance requires further study. The fourth study proposes a framework that utilizes AI-powered monitoring system using smartphone-captured images and deep learning models to automate distress detection (potholes, rutting, loose aggregate) with 89–94% accuracy. Integrated GIS mapping enables real-time condition assessment and prioritization. Challenges include model adaptability to seasonal changes, highlighting the need for adaptive training datasets. The final contribution synthesizes these advancements into a holistic management framework. By combining dust prediction, stabilizer selection tools, and AI monitoring, the framework empowers agencies to optimize maintenance planning and resource allocation. Theoretical contributions include novel methodologies for gravel road modeling and condition assessment, while practical methods offer scalable solutions for rural agencies. Future work should explore regional adaptability which bridges critical gaps in gravel road management, promoting sustainable, data-driven infrastructure practices for gravel road rural networks.","abstract_has_math":false,"creators":["Alsheyab, Mohammad Ahmad Saleh"],"institution":"Iowa State University - Thesis & Dissertation","degree_name":"Doctor of Philosophy","degree_level":"dissertation","degree_discipline":"Civil engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Smadi, Omar, G."],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-05","date_published":"2026-05","updated_at":"2026-07-24T02:38:34Z","subjects":["Civil, construction, and environmental engineering"],"languages":["en_US"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://dr.lib.iastate.edu/handle/20.500.12876/106540","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Smadi, Omar, G."]},{"key":"dc:creator","label":"Author","values":["Alsheyab, Mohammad Ahmad Saleh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-06-09T16:04:12Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-05"]},{"key":"dc:type","label":"Dc Type","values":["Text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Iowa State University - Thesis & Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Civil, construction, and environmental engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://dr.lib.iastate.edu/handle/20.500.12876/106540"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["May2026"]},{"key":"dc:description.abstract","label":"Abstract","values":["Sustainable gravel road management is essential for maintaining rural infrastructure reliability, safety, and environmental quality. However, persistent challenges, including excessive dust emissions, rapid surface deterioration, and inefficient monitoring, hinder effective maintenance. This dissertation addresses these challenges by developing an integrated context that combines predictive modeling, chemical stabilization, and a framework proposing an AI-driven monitoring approach to enhance gravel road performance and longevity.The first study establishes a predictive model for dust generation, correlating vehicle speed (25–50 mph) and surface fines content with emissions using field-collected data from Iowa gravel roads. The model provides actionable thresholds for dust suppression, enabling agencies to implement targeted speed controls and material treatments. While accurate for typical conditions, performance varied under extreme weather, suggesting opportunities to incorporate additional environmental variables. The second and third studies evaluates chemical stabilizers (ionic, enzymatic, and water-absorbent solutions) through lab and field experiments. Results demonstrate that ionic stabilizers paired with gradation-controlled aggregates improve durability by 35–50% compared to untreated sections. A cost-benefit analysis reveals optimal stabilizer selections for different traffic and climate conditions, though long-term (>1-year) performance requires further study. The fourth study proposes a framework that utilizes AI-powered monitoring system using smartphone-captured images and deep learning models to automate distress detection (potholes, rutting, loose aggregate) with 89–94% accuracy. Integrated GIS mapping enables real-time condition assessment and prioritization. Challenges include model adaptability to seasonal changes, highlighting the need for adaptive training datasets. The final contribution synthesizes these advancements into a holistic management framework. By combining dust prediction, stabilizer selection tools, and AI monitoring, the framework empowers agencies to optimize maintenance planning and resource allocation. Theoretical contributions include novel methodologies for gravel road modeling and condition assessment, while practical methods offer scalable solutions for rural agencies. Future work should explore regional adaptability which bridges critical gaps in gravel road management, promoting sustainable, data-driven infrastructure practices for gravel road rural networks."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["PDF"]},{"key":"dc:title","label":"Title","values":["Sustainable advancements in rural infrastructure: Enhancing gravel road performance, stabilization, and intelligent monitoring"]}]}],"canonical_facts":{"dc:contributor.advisor":["Smadi, Omar, G."],"dc:creator":["Alsheyab, Mohammad Ahmad Saleh"],"dc:date.accessioned":["2026-06-09T16:04:12Z"],"dc:date.issued":["2026-05"],"dc:description":["May2026"],"dc:description.abstract":["Sustainable gravel road management is essential for maintaining rural infrastructure reliability, safety, and environmental quality. However, persistent challenges, including excessive dust emissions, rapid surface deterioration, and inefficient monitoring, hinder effective maintenance. This dissertation addresses these challenges by developing an integrated context that combines predictive modeling, chemical stabilization, and a framework proposing an AI-driven monitoring approach to enhance gravel road performance and longevity.The first study establishes a predictive model for dust generation, correlating vehicle speed (25–50 mph) and surface fines content with emissions using field-collected data from Iowa gravel roads. The model provides actionable thresholds for dust suppression, enabling agencies to implement targeted speed controls and material treatments. While accurate for typical conditions, performance varied under extreme weather, suggesting opportunities to incorporate additional environmental variables. The second and third studies evaluates chemical stabilizers (ionic, enzymatic, and water-absorbent solutions) through lab and field experiments. Results demonstrate that ionic stabilizers paired with gradation-controlled aggregates improve durability by 35–50% compared to untreated sections. A cost-benefit analysis reveals optimal stabilizer selections for different traffic and climate conditions, though long-term (>1-year) performance requires further study. The fourth study proposes a framework that utilizes AI-powered monitoring system using smartphone-captured images and deep learning models to automate distress detection (potholes, rutting, loose aggregate) with 89–94% accuracy. Integrated GIS mapping enables real-time condition assessment and prioritization. Challenges include model adaptability to seasonal changes, highlighting the need for adaptive training datasets. The final contribution synthesizes these advancements into a holistic management framework. By combining dust prediction, stabilizer selection tools, and AI monitoring, the framework empowers agencies to optimize maintenance planning and resource allocation. Theoretical contributions include novel methodologies for gravel road modeling and condition assessment, while practical methods offer scalable solutions for rural agencies. Future work should explore regional adaptability which bridges critical gaps in gravel road management, promoting sustainable, data-driven infrastructure practices for gravel road rural networks."],"dc:format.mimetype":["PDF"],"dc:identifier.uri":["https://dr.lib.iastate.edu/handle/20.500.12876/106540"],"dc:language.iso":["en_US"],"dc:subject":["Civil, construction, and environmental engineering"],"dc:title":["Sustainable advancements in rural infrastructure: Enhancing gravel road performance, stabilization, and intelligent monitoring"],"dc:type":["Text"],"thesis:degree_discipline":["Civil engineering"],"thesis:degree_level":["dissertation"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Iowa State University - Thesis & Dissertation"]},"updated_at":"2026-07-24T02:38:34Z"}