{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/138137"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/138137","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Network-Level Structural Condition Data for Sustainable Pavement Asset Management","abstract":"Pavement management systems (PMS) are critical tools that agencies use to optimize maintenance and rehabilitation (MandR) decisions. Traditionally, PMS frameworks rely primarily on surface condition indicators such as cracking, rutting, and roughness to trigger treatments and allocate resources. While surface condition measures provide valuable insight into the functional performance of pavements, they do not directly capture the structural capacity that influences long-term performance. As a result, agencies risk selecting treatments that are either insufficient for addressing underlying structural deficiencies or unnecessarily conservative, leading to inefficient allocation of limited budgets. This gap is particularly relevant at the network level, where decisions must balance cost-effectiveness, system performance, and sustainability. The integration of structural condition into PMS has historically been limited by challenges in data collection technologies. The falling weight deflectometer (FWD), while effective for point testing, is unsuitable for network-level applications due to its slow operation and traffic disruption. The traffic speed deflectometer (TSD), a continuous deflection measuring device, overcomes these challenges by collecting structural data at highway speeds, making it practical for large-scale use. This dissertation presents a comprehensive approach for incorporating TSD-based structural condition information into pavement management systems. Four interrelated studies were conducted to evaluate the feasibility, effectiveness, and implications of using TSD-derived structural metrics in network-level practices. Together, these studies cover treatment selection, network needs assessment, life cycle cost implications, and environmental evaluations, providing a holistic assessment of the role of structural data in sustainable pavement management. The first study focused on the feasibility of incorporating TSD-based structural condition into treatment selection processes. A pilot framework was developed to integrate TSD-derived metrics—specifically, the effective structural number (SNeff) and remaining structural service life (RSTL)—with surface condition information in decision trees. This framework was tested on a case study along Route 29 in Virginia. Results showed that including structural data led to significant changes in treatment selection outcomes. In particular, many sections that would have been recommended for costly structural rehabilitation under surface-only assessments were instead identified as candidates for less intensive surface treatments. This adjustment reduced treatment costs by up to 33.2% in a single maintenance cycle, illustrating the potential efficiency gains from better aligning treatments with true structural needs. The second study expanded the treatment selection framework to a larger portion of Virginia's network, encompassing more than 4,250 lane-miles of interstate and primary roads. This broader application demonstrated that structural condition information has measurable impacts on network-level needs assessment and prioritization. Findings revealed that interstate pavements were generally in better structural condition than primary roads, and the integration of TSD-derived indicators such as RSTL provided a more refined understanding of where structural interventions were truly needed. Compared to surface-only approaches, incorporating structural condition enabled agencies to better distinguish between sections requiring surface-level interventions and those needing structural rehabilitation, thereby improving the prioritization of treatments under constrained budgets. The third study examined the long-term economic implications of integrating structural data into PMS by conducting life-cycle cost analyses. Using 30-year planning horizons, the study compared functional-only strategies with those that incorporated structural condition metrics. Results showed that incorporating structural information reduced life-cycle costs by up to 11.2%. These savings were largely attributable to improved treatment timing and more efficient use of resources, including reductions in unnecessary heavy rehabilitation where structural capacity remained sufficient. The findings underscore that the benefits of structural integration extend beyond short-term efficiency to include long-term economic sustainability, particularly at the strategic planning level. The fourth study addressed the environmental dimension of pavement management by enhancing roughness prediction models with structural condition data. Traditional roughness models rely solely on pavement age, limiting their ability to capture the influence of structural condition on roughness progression. In this study, structural metrics such as the surface curvature index (SCI300) and the modified structural index (MSI) were incorporated as explanatory variables in roughness deterioration models. Comparative analyses across 1,513 km (940 mi) of Virginia's primary roads showed that these enhanced models provided improved predictive accuracy. Importantly, their application in environmental life-cycle assessment (LCA) showed that structurally weak pavements deteriorate more rapidly and result in higher use-stage greenhouse gas emissions than stronger pavements. For example, relative to age-only models, 10-year use-stage emissions for a structurally strong pavement decreased by up to 43.1 tons of CO2-equivalent per lane-mile. These findings highlight the significance of structural condition in quantifying environmental impacts and the role of TSD-enhanced models in supporting sustainability-oriented pavement management. Collectively, the results of the four studies show that incorporating TSD-based structural condition data into PMS strengthens decision-making across tactical, strategic, and sustainability dimensions. At the tactical level, structural data improve treatment selection and reduce costs by aligning interventions with true pavement needs. At the strategic level, structural integration lowers long-term life-cycle costs and improves the prioritization of investments. From an environmental perspective, enhanced prediction models enable more improved evaluation of use-stage emissions and could support greenhouse gas reduction initiatives. Overall, results of this research suggest that the integration of structural condition into PMS enhances the technical, economic, and environmental sustainability of pavement management systems.","abstract_html":"Pavement management systems (PMS) are critical tools that agencies use to optimize maintenance and rehabilitation (MandR) decisions. Traditionally, PMS frameworks rely primarily on surface condition indicators such as cracking, rutting, and roughness to trigger treatments and allocate resources. While surface condition measures provide valuable insight into the functional performance of pavements, they do not directly capture the structural capacity that influences long-term performance. As a result, agencies risk selecting treatments that are either insufficient for addressing underlying structural deficiencies or unnecessarily conservative, leading to inefficient allocation of limited budgets. This gap is particularly relevant at the network level, where decisions must balance cost-effectiveness, system performance, and sustainability. The integration of structural condition into PMS has historically been limited by challenges in data collection technologies. The falling weight deflectometer (FWD), while effective for point testing, is unsuitable for network-level applications due to its slow operation and traffic disruption. The traffic speed deflectometer (TSD), a continuous deflection measuring device, overcomes these challenges by collecting structural data at highway speeds, making it practical for large-scale use. This dissertation presents a comprehensive approach for incorporating TSD-based structural condition information into pavement management systems. Four interrelated studies were conducted to evaluate the feasibility, effectiveness, and implications of using TSD-derived structural metrics in network-level practices. Together, these studies cover treatment selection, network needs assessment, life cycle cost implications, and environmental evaluations, providing a holistic assessment of the role of structural data in sustainable pavement management. The first study focused on the feasibility of incorporating TSD-based structural condition into treatment selection processes. A pilot framework was developed to integrate TSD-derived metrics—specifically, the effective structural number (SNeff) and remaining structural service life (RSTL)—with surface condition information in decision trees. This framework was tested on a case study along Route 29 in Virginia. Results showed that including structural data led to significant changes in treatment selection outcomes. In particular, many sections that would have been recommended for costly structural rehabilitation under surface-only assessments were instead identified as candidates for less intensive surface treatments. This adjustment reduced treatment costs by up to 33.2% in a single maintenance cycle, illustrating the potential efficiency gains from better aligning treatments with true structural needs. The second study expanded the treatment selection framework to a larger portion of Virginia's network, encompassing more than 4,250 lane-miles of interstate and primary roads. This broader application demonstrated that structural condition information has measurable impacts on network-level needs assessment and prioritization. Findings revealed that interstate pavements were generally in better structural condition than primary roads, and the integration of TSD-derived indicators such as RSTL provided a more refined understanding of where structural interventions were truly needed. Compared to surface-only approaches, incorporating structural condition enabled agencies to better distinguish between sections requiring surface-level interventions and those needing structural rehabilitation, thereby improving the prioritization of treatments under constrained budgets. The third study examined the long-term economic implications of integrating structural data into PMS by conducting life-cycle cost analyses. Using 30-year planning horizons, the study compared functional-only strategies with those that incorporated structural condition metrics. Results showed that incorporating structural information reduced life-cycle costs by up to 11.2%. These savings were largely attributable to improved treatment timing and more efficient use of resources, including reductions in unnecessary heavy rehabilitation where structural capacity remained sufficient. The findings underscore that the benefits of structural integration extend beyond short-term efficiency to include long-term economic sustainability, particularly at the strategic planning level. The fourth study addressed the environmental dimension of pavement management by enhancing roughness prediction models with structural condition data. Traditional roughness models rely solely on pavement age, limiting their ability to capture the influence of structural condition on roughness progression. In this study, structural metrics such as the surface curvature index (SCI300) and the modified structural index (MSI) were incorporated as explanatory variables in roughness deterioration models. Comparative analyses across 1,513 km (940 mi) of Virginia's primary roads showed that these enhanced models provided improved predictive accuracy. Importantly, their application in environmental life-cycle assessment (LCA) showed that structurally weak pavements deteriorate more rapidly and result in higher use-stage greenhouse gas emissions than stronger pavements. For example, relative to age-only models, 10-year use-stage emissions for a structurally strong pavement decreased by up to 43.1 tons of CO2-equivalent per lane-mile. These findings highlight the significance of structural condition in quantifying environmental impacts and the role of TSD-enhanced models in supporting sustainability-oriented pavement management. Collectively, the results of the four studies show that incorporating TSD-based structural condition data into PMS strengthens decision-making across tactical, strategic, and sustainability dimensions. At the tactical level, structural data improve treatment selection and reduce costs by aligning interventions with true pavement needs. At the strategic level, structural integration lowers long-term life-cycle costs and improves the prioritization of investments. From an environmental perspective, enhanced prediction models enable more improved evaluation of use-stage emissions and could support greenhouse gas reduction initiatives. Overall, results of this research suggest that the integration of structural condition into PMS enhances the technical, economic, and environmental sustainability of pavement management systems.","abstract_has_math":false,"creators":["Murekye, Angello"],"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":["Flintsch, Gerardo W."],"committee_members":["Katicha, Samer Wehbe","Brand, Alexander S.","Diefenderfer, Brian Keith"],"year":2025,"date_issued":"2025-10-10","date_published":"2025-10-10","updated_at":"2026-07-22T22:19:49Z","subjects":["Traffic speed deflectometer","Network-level structural condition","Pavement management system","Life-cycle cost analysis","Life-cycle assessment","Treatment selection","Environmental sustainability"],"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:44701"],"render_values":[{"text":"vt_gsexam:44701","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/138137","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Flintsch, Gerardo W."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Katicha, Samer Wehbe","Brand, Alexander S.","Diefenderfer, Brian Keith"]},{"key":"dc:contributor.department","label":"Department","values":["Civil and Environmental Engineering"]},{"key":"dc:creator","label":"Author","values":["Murekye, Angello"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-10-11T08:00:22Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-10-11T08:00:22Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-10-10"]},{"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":["Traffic speed deflectometer","Network-level structural condition","Pavement management system","Life-cycle cost analysis","Life-cycle assessment","Treatment selection","Environmental sustainability"]}]},{"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:44701"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/138137"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Pavement management systems (PMS) are critical tools that agencies use to optimize maintenance and rehabilitation (MandR) decisions. Traditionally, PMS frameworks rely primarily on surface condition indicators such as cracking, rutting, and roughness to trigger treatments and allocate resources. While surface condition measures provide valuable insight into the functional performance of pavements, they do not directly capture the structural capacity that influences long-term performance. As a result, agencies risk selecting treatments that are either insufficient for addressing underlying structural deficiencies or unnecessarily conservative, leading to inefficient allocation of limited budgets. This gap is particularly relevant at the network level, where decisions must balance cost-effectiveness, system performance, and sustainability. The integration of structural condition into PMS has historically been limited by challenges in data collection technologies. The falling weight deflectometer (FWD), while effective for point testing, is unsuitable for network-level applications due to its slow operation and traffic disruption. The traffic speed deflectometer (TSD), a continuous deflection measuring device, overcomes these challenges by collecting structural data at highway speeds, making it practical for large-scale use. This dissertation presents a comprehensive approach for incorporating TSD-based structural condition information into pavement management systems. Four interrelated studies were conducted to evaluate the feasibility, effectiveness, and implications of using TSD-derived structural metrics in network-level practices. Together, these studies cover treatment selection, network needs assessment, life cycle cost implications, and environmental evaluations, providing a holistic assessment of the role of structural data in sustainable pavement management. The first study focused on the feasibility of incorporating TSD-based structural condition into treatment selection processes. A pilot framework was developed to integrate TSD-derived metrics—specifically, the effective structural number (SNeff) and remaining structural service life (RSTL)—with surface condition information in decision trees. This framework was tested on a case study along Route 29 in Virginia. Results showed that including structural data led to significant changes in treatment selection outcomes. In particular, many sections that would have been recommended for costly structural rehabilitation under surface-only assessments were instead identified as candidates for less intensive surface treatments. This adjustment reduced treatment costs by up to 33.2% in a single maintenance cycle, illustrating the potential efficiency gains from better aligning treatments with true structural needs. The second study expanded the treatment selection framework to a larger portion of Virginia's network, encompassing more than 4,250 lane-miles of interstate and primary roads. This broader application demonstrated that structural condition information has measurable impacts on network-level needs assessment and prioritization. Findings revealed that interstate pavements were generally in better structural condition than primary roads, and the integration of TSD-derived indicators such as RSTL provided a more refined understanding of where structural interventions were truly needed. Compared to surface-only approaches, incorporating structural condition enabled agencies to better distinguish between sections requiring surface-level interventions and those needing structural rehabilitation, thereby improving the prioritization of treatments under constrained budgets. The third study examined the long-term economic implications of integrating structural data into PMS by conducting life-cycle cost analyses. Using 30-year planning horizons, the study compared functional-only strategies with those that incorporated structural condition metrics. Results showed that incorporating structural information reduced life-cycle costs by up to 11.2%. These savings were largely attributable to improved treatment timing and more efficient use of resources, including reductions in unnecessary heavy rehabilitation where structural capacity remained sufficient. The findings underscore that the benefits of structural integration extend beyond short-term efficiency to include long-term economic sustainability, particularly at the strategic planning level. The fourth study addressed the environmental dimension of pavement management by enhancing roughness prediction models with structural condition data. Traditional roughness models rely solely on pavement age, limiting their ability to capture the influence of structural condition on roughness progression. In this study, structural metrics such as the surface curvature index (SCI300) and the modified structural index (MSI) were incorporated as explanatory variables in roughness deterioration models. Comparative analyses across 1,513 km (940 mi) of Virginia's primary roads showed that these enhanced models provided improved predictive accuracy. Importantly, their application in environmental life-cycle assessment (LCA) showed that structurally weak pavements deteriorate more rapidly and result in higher use-stage greenhouse gas emissions than stronger pavements. For example, relative to age-only models, 10-year use-stage emissions for a structurally strong pavement decreased by up to 43.1 tons of CO2-equivalent per lane-mile. These findings highlight the significance of structural condition in quantifying environmental impacts and the role of TSD-enhanced models in supporting sustainability-oriented pavement management. Collectively, the results of the four studies show that incorporating TSD-based structural condition data into PMS strengthens decision-making across tactical, strategic, and sustainability dimensions. At the tactical level, structural data improve treatment selection and reduce costs by aligning interventions with true pavement needs. At the strategic level, structural integration lowers long-term life-cycle costs and improves the prioritization of investments. From an environmental perspective, enhanced prediction models enable more improved evaluation of use-stage emissions and could support greenhouse gas reduction initiatives. Overall, results of this research suggest that the integration of structural condition into PMS enhances the technical, economic, and environmental sustainability of pavement management systems."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Roads are one of our most important public investments. Each year, transportation agencies must decide which roads to fix, when to fix them, and how to make the best use of limited funding. To support these decisions, most transportation agencies use pavement management systems—computer-based tools that assess road conditions and help prioritize repairs. However, these systems often rely only on assessing surface conditions like cracks and roughness, without considering the structural health of the pavement underneath. This can lead to poor decisions, such as performing light repairs when a full rebuild is needed—or replacing a road that's still structurally sound. This research explored how to improve decision-making by using a new technology called the Traffic Speed Deflectometer (TSD), which collects information about the strength of the road while driving at normal traffic speeds. Unlike older tools, the TSD makes it possible to scan thousands of miles of road without interrupting traffic. The study focused on how to turn this new structural data into useful information for road maintenance planning, budgeting, and environmental evaluation. The research included four major studies. First, it developed new tools to combine road surface data with structural data for better repair decisions. This was tested on a section of Route 29 in Virginia and showed that including structural data could reduce repair costs by more than 30%. Second, the study expanded the approach to thousands of miles of state roads and found that structural data helped identify roads that needed different repairs than surface data alone would suggest. Third, it analyzed long-term costs and found that considering structural condition could reduce road repair spending by over 11% across 30 years. Finally, the research developed better models to predict road roughness over time and showed that these models, when improved with structural data, could impact environmental assessments results—indicating that weak roads deteriorate faster and result in higher emissions at the use stage than strong roads. Overall, this research shows that using TSD technology to assess road strength can lead to smarter, more cost-effective, and environmentally sustainable road maintenance decisions. Transportation agencies can adopt these methods to better manage aging infrastructure and achieve long-term savings and climate goals."]},{"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":["Network-Level Structural Condition Data for Sustainable Pavement Asset Management"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Flintsch, Gerardo W."],"dc:contributor.committeemember":["Katicha, Samer Wehbe","Brand, Alexander S.","Diefenderfer, Brian Keith"],"dc:contributor.department":["Civil and Environmental Engineering"],"dc:creator":["Murekye, Angello"],"dc:date.accessioned":["2025-10-11T08:00:22Z"],"dc:date.available":["2025-10-11T08:00:22Z"],"dc:date.issued":["2025-10-10"],"dc:description.abstract":["Pavement management systems (PMS) are critical tools that agencies use to optimize maintenance and rehabilitation (MandR) decisions. Traditionally, PMS frameworks rely primarily on surface condition indicators such as cracking, rutting, and roughness to trigger treatments and allocate resources. While surface condition measures provide valuable insight into the functional performance of pavements, they do not directly capture the structural capacity that influences long-term performance. As a result, agencies risk selecting treatments that are either insufficient for addressing underlying structural deficiencies or unnecessarily conservative, leading to inefficient allocation of limited budgets. This gap is particularly relevant at the network level, where decisions must balance cost-effectiveness, system performance, and sustainability. The integration of structural condition into PMS has historically been limited by challenges in data collection technologies. The falling weight deflectometer (FWD), while effective for point testing, is unsuitable for network-level applications due to its slow operation and traffic disruption. The traffic speed deflectometer (TSD), a continuous deflection measuring device, overcomes these challenges by collecting structural data at highway speeds, making it practical for large-scale use. This dissertation presents a comprehensive approach for incorporating TSD-based structural condition information into pavement management systems. Four interrelated studies were conducted to evaluate the feasibility, effectiveness, and implications of using TSD-derived structural metrics in network-level practices. Together, these studies cover treatment selection, network needs assessment, life cycle cost implications, and environmental evaluations, providing a holistic assessment of the role of structural data in sustainable pavement management. The first study focused on the feasibility of incorporating TSD-based structural condition into treatment selection processes. A pilot framework was developed to integrate TSD-derived metrics—specifically, the effective structural number (SNeff) and remaining structural service life (RSTL)—with surface condition information in decision trees. This framework was tested on a case study along Route 29 in Virginia. Results showed that including structural data led to significant changes in treatment selection outcomes. In particular, many sections that would have been recommended for costly structural rehabilitation under surface-only assessments were instead identified as candidates for less intensive surface treatments. This adjustment reduced treatment costs by up to 33.2% in a single maintenance cycle, illustrating the potential efficiency gains from better aligning treatments with true structural needs. The second study expanded the treatment selection framework to a larger portion of Virginia's network, encompassing more than 4,250 lane-miles of interstate and primary roads. This broader application demonstrated that structural condition information has measurable impacts on network-level needs assessment and prioritization. Findings revealed that interstate pavements were generally in better structural condition than primary roads, and the integration of TSD-derived indicators such as RSTL provided a more refined understanding of where structural interventions were truly needed. Compared to surface-only approaches, incorporating structural condition enabled agencies to better distinguish between sections requiring surface-level interventions and those needing structural rehabilitation, thereby improving the prioritization of treatments under constrained budgets. The third study examined the long-term economic implications of integrating structural data into PMS by conducting life-cycle cost analyses. Using 30-year planning horizons, the study compared functional-only strategies with those that incorporated structural condition metrics. Results showed that incorporating structural information reduced life-cycle costs by up to 11.2%. These savings were largely attributable to improved treatment timing and more efficient use of resources, including reductions in unnecessary heavy rehabilitation where structural capacity remained sufficient. The findings underscore that the benefits of structural integration extend beyond short-term efficiency to include long-term economic sustainability, particularly at the strategic planning level. The fourth study addressed the environmental dimension of pavement management by enhancing roughness prediction models with structural condition data. Traditional roughness models rely solely on pavement age, limiting their ability to capture the influence of structural condition on roughness progression. In this study, structural metrics such as the surface curvature index (SCI300) and the modified structural index (MSI) were incorporated as explanatory variables in roughness deterioration models. Comparative analyses across 1,513 km (940 mi) of Virginia's primary roads showed that these enhanced models provided improved predictive accuracy. Importantly, their application in environmental life-cycle assessment (LCA) showed that structurally weak pavements deteriorate more rapidly and result in higher use-stage greenhouse gas emissions than stronger pavements. For example, relative to age-only models, 10-year use-stage emissions for a structurally strong pavement decreased by up to 43.1 tons of CO2-equivalent per lane-mile. These findings highlight the significance of structural condition in quantifying environmental impacts and the role of TSD-enhanced models in supporting sustainability-oriented pavement management. Collectively, the results of the four studies show that incorporating TSD-based structural condition data into PMS strengthens decision-making across tactical, strategic, and sustainability dimensions. At the tactical level, structural data improve treatment selection and reduce costs by aligning interventions with true pavement needs. At the strategic level, structural integration lowers long-term life-cycle costs and improves the prioritization of investments. From an environmental perspective, enhanced prediction models enable more improved evaluation of use-stage emissions and could support greenhouse gas reduction initiatives. Overall, results of this research suggest that the integration of structural condition into PMS enhances the technical, economic, and environmental sustainability of pavement management systems."],"dc:description.abstractgeneral":["Roads are one of our most important public investments. Each year, transportation agencies must decide which roads to fix, when to fix them, and how to make the best use of limited funding. To support these decisions, most transportation agencies use pavement management systems—computer-based tools that assess road conditions and help prioritize repairs. However, these systems often rely only on assessing surface conditions like cracks and roughness, without considering the structural health of the pavement underneath. This can lead to poor decisions, such as performing light repairs when a full rebuild is needed—or replacing a road that's still structurally sound. This research explored how to improve decision-making by using a new technology called the Traffic Speed Deflectometer (TSD), which collects information about the strength of the road while driving at normal traffic speeds. Unlike older tools, the TSD makes it possible to scan thousands of miles of road without interrupting traffic. The study focused on how to turn this new structural data into useful information for road maintenance planning, budgeting, and environmental evaluation. The research included four major studies. First, it developed new tools to combine road surface data with structural data for better repair decisions. This was tested on a section of Route 29 in Virginia and showed that including structural data could reduce repair costs by more than 30%. Second, the study expanded the approach to thousands of miles of state roads and found that structural data helped identify roads that needed different repairs than surface data alone would suggest. Third, it analyzed long-term costs and found that considering structural condition could reduce road repair spending by over 11% across 30 years. Finally, the research developed better models to predict road roughness over time and showed that these models, when improved with structural data, could impact environmental assessments results—indicating that weak roads deteriorate faster and result in higher emissions at the use stage than strong roads. Overall, this research shows that using TSD technology to assess road strength can lead to smarter, more cost-effective, and environmentally sustainable road maintenance decisions. Transportation agencies can adopt these methods to better manage aging infrastructure and achieve long-term savings and climate goals."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:44701"],"dc:identifier.uri":["https://hdl.handle.net/10919/138137"],"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":["Traffic speed deflectometer","Network-level structural condition","Pavement management system","Life-cycle cost analysis","Life-cycle assessment","Treatment selection","Environmental sustainability"],"dc:title":["Network-Level Structural Condition Data for Sustainable Pavement Asset Management"],"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":"2026-07-22T22:19:49Z"}