Why Integrated Decision-Making Could Transform Expressway Maintenance Prioritization

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Maintaining expressway infrastructure is a complex engineering challenge that extends beyond repairing deteriorated pavement. Transportation agencies must balance pavement condition, traffic demand, accident risk, environmental exposure, and budget limitations when determining which road sections require immediate intervention. Conventional maintenance prioritization methods frequently rely on a limited number of pavement performance indicators, making it difficult to capture the broader operational context of modern highway networks. This study proposes an integrated decision-making framework that combines data-driven scoring, rule-based reasoning, and the Analytic Hierarchy Process (AHP) to support more systematic maintenance planning. The research provides valuable insights into how multiple engineering criteria can be synthesized to improve maintenance prioritization, enhance roadway safety, optimize infrastructure investment, and support sustainable transportation asset management.

Bibliographic Information

Item Information
Article Title Integrated decision-making strategies for expressway maintenance prioritization: Data-driven scoring, rule-based, and AHP approach
Authors Nan Htike Yee Mon, Kyaing, and Moe Thet Thet Aye
Journal Innovation in Engineering
Volume & Issue Volume 2, Issue 1
Publication Year 2025
Pages 1–15
DOI https://doi.org/10.58712/ie.v2i1.17
Publisher Researcher and Lecturer Society
License Creative Commons Attribution 4.0 International (CC BY 4.0)

1. Research Background

  • Expressway maintenance is fundamental to transportation asset management. High-speed road networks require systematic maintenance to preserve pavement performance, ensure user safety, minimize lifecycle costs, and maintain uninterrupted transportation services. Effective prioritization is therefore an essential component of sustainable infrastructure management.
  • Traditional maintenance prioritization often focuses on pavement condition alone. Conventional approaches typically rely on indicators such as the Pavement Condition Index (PCI) or International Roughness Index (IRI). While these measures provide valuable information regarding pavement quality, they may overlook other operational factors that influence maintenance urgency.
  • Road safety is influenced by multiple interacting factors. Accident frequency, accident severity, roadway geometry, traffic demand, environmental exposure, and accessibility all contribute to infrastructure performance. Evaluating only structural pavement deterioration may therefore lead to suboptimal maintenance decisions.
  • Infrastructure agencies face increasing resource constraints. Limited maintenance budgets require engineers to allocate available resources strategically. Decision-making frameworks capable of evaluating multiple criteria simultaneously are becoming increasingly important for maximizing maintenance effectiveness.
  • Existing prioritization methods rarely integrate engineering, operational, and safety indicators into a unified framework. Previous approaches frequently evaluate pavement condition, traffic, or safety independently rather than combining these variables into a comprehensive maintenance prioritization system.
  • The research addresses this methodological gap through an integrated decision-making strategy. The proposed framework combines a quantitative data-driven scoring method, a rule-based maintenance classification system, and the Analytic Hierarchy Process (AHP). Together, these complementary methods support more systematic and transparent maintenance prioritization.
  • The framework incorporates seven key decision variables. Maintenance priorities are evaluated using Pavement Condition Rating (PCR), Accident Density (AD), Weighted Accident Severity Index (WASI), critical horizontal curves, projected traffic volumes, village accessibility, and annual rainfall, enabling a broader assessment of maintenance requirements.
  • The study introduces a practical decision-support model for transportation engineers. Rather than relying on a single indicator, the framework integrates structural pavement condition with operational, environmental, and safety considerations, providing a more balanced basis for infrastructure investment decisions.
  • The proposed methodology is designed to be adaptable beyond the study area. Although developed using the Yangon–Mandalay Expressway in Myanmar, the integrated prioritization framework is intended to serve as a replicable approach for other expressway networks facing similar maintenance challenges.

2. Research Objectives

  • To develop an integrated framework that combines quantitative scoring, rule-based reasoning, and the Analytic Hierarchy Process (AHP) for expressway maintenance prioritization.
  • To establish a multi-criteria scoring system that evaluates maintenance needs using pavement condition, accident statistics, roadway geometry, projected traffic, accessibility, and climatic conditions.
  • To develop a rule-based maintenance classification approach using four pavement performance indicators: Pavement Condition Index (PCI), International Roughness Index (IRI), Present Serviceability Index (PSI), and Present Serviceability Rating (PSR).
  • To prioritize pavement maintenance units according to surface distress characteristics using the Analytic Hierarchy Process (AHP) in accordance with ASTM D6433.
  • To integrate the three complementary approaches into a unified decision-support framework capable of supporting more precise, transparent, and resource-efficient maintenance planning.

3. Why This Research Matters

  • Improves transportation infrastructure management. Integrating multiple engineering indicators enables maintenance decisions that better reflect the actual operational condition of expressway networks.
  • Enhances roadway safety. Including accident density, accident severity, and roadway geometry alongside pavement condition allows engineers to identify sections where maintenance can contribute to reducing safety risks.
  • Supports more efficient allocation of maintenance budgets. Multi-criteria prioritization helps transportation agencies direct limited financial resources toward road sections with the greatest overall maintenance need.
  • Promotes sustainable infrastructure management. Timely maintenance interventions can extend pavement service life, reduce deterioration rates, and improve long-term infrastructure sustainability.
  • Advances engineering decision-making. The integration of quantitative scoring, rule-based reasoning, and AHP demonstrates how complementary decision-support techniques can improve maintenance prioritization compared with relying on individual methods.
  • Provides practical value for transportation agencies. The proposed framework offers a systematic and interpretable methodology that can assist engineers, planners, and infrastructure managers in developing evidence-based maintenance programs.
  • Creates opportunities for broader implementation. Because the methodology combines commonly available engineering data with established multi-criteria decision-making techniques, it has potential applicability to expressway and highway networks in other regions facing similar maintenance challenges.

4. Research Methodology

  • Research Type

    This study employed a quantitative engineering research approach focused on developing an integrated decision-support framework for expressway maintenance prioritization. Rather than evaluating a single pavement indicator, the researchers combined three complementary decision-making techniques to establish a comprehensive maintenance prioritization system capable of incorporating structural, operational, environmental, and safety-related factors.

  • Study Area

    The proposed framework was implemented on the Yangon–Mandalay Expressway (YME), Myanmar's first four-lane divided expressway extending approximately 365 miles. The highway has been in operation for more than fourteen years and consists of both asphalt concrete overlay and rigid concrete pavement sections. Due to its strategic importance and long operational history, the expressway provides an appropriate case study for evaluating maintenance prioritization methods.

  • Integrated Data Collection

    Seven engineering and operational variables were collected to evaluate maintenance priorities across eight expressway sections. These variables included Pavement Condition Rating (PCR), Accident Density (AD), Weighted Accident Severity Index (WASI), number of critical horizontal curves, projected future traffic volume, village accessibility, and annual rainfall. The information was compiled from pavement condition surveys, accident records, highway databases, traffic projections, and meteorological data.

  • Integrated Data-Driven Scoring Method

    Each evaluation parameter was normalized onto a common scale ranging from 0 to 1, allowing variables with different units to be compared consistently. The normalized values were subsequently aggregated to produce an overall maintenance priority score for every expressway section. Higher cumulative scores represented greater maintenance urgency by simultaneously considering pavement deterioration, safety performance, operational demand, accessibility, and environmental exposure.

  • Rule-Based Maintenance Classification

    A rule-based decision system was developed using four widely accepted pavement performance indicators: Pavement Condition Index (PCI), International Roughness Index (IRI), Present Serviceability Rating (PSR), and Present Serviceability Index (PSI). Six maintenance categories were established, ranging from routine maintenance to full reconstruction. A pavement unit was assigned to a maintenance category when at least two of the four indicators satisfied the corresponding decision criteria.

    To distinguish pavement units that belonged to the same maintenance category, cumulative indicator ratings were calculated by summing the four performance scores. Units with larger cumulative values were interpreted as requiring more immediate intervention within the same maintenance class.

  • Analytic Hierarchy Process (AHP)

    The Analytic Hierarchy Process (AHP) was incorporated to prioritize pavement maintenance according to pavement distress characteristics. Separate hierarchical models were established for asphalt concrete overlay and rigid concrete pavement. Pairwise comparison matrices were developed using pavement distress significance derived from multiple linear regression analysis, allowing different distress types to receive relative importance weights during prioritization.

  • Pavement Evaluation Indicators

    The framework integrated several established pavement performance measures. PCI was used to evaluate structural pavement condition according to ASTM D6433. IRI represented pavement roughness, while PSR and PSI reflected pavement serviceability from both subjective and objective perspectives. Accident Density and the Weighted Accident Severity Index were additionally incorporated to represent traffic safety performance.

  • Data Processing and Analysis

    The integrated framework followed a sequential workflow beginning with parameter normalization, score aggregation, road section ranking, maintenance classification through the rule-based system, and final prioritization using AHP. Consistency testing was performed during the AHP procedure to ensure logical pairwise comparisons and reliable weighting results.

  • Validation Strategy

    The proposed methodology was evaluated by applying the integrated framework to eight expressway sections and 130 pavement sample units. Comparisons among the scoring system, rule-based classification, and AHP prioritization demonstrated the framework's capability to consistently identify road sections and pavement units requiring the highest maintenance priority.

5. Key Findings

Integrated Multi-Criteria Scoring Successfully Distinguished Maintenance Priorities

The integrated scoring framework effectively differentiated maintenance priorities among the eight sections of the Yangon–Mandalay Expressway by simultaneously considering pavement condition, accident characteristics, roadway geometry, accessibility, projected traffic demand, and rainfall. Rather than depending on a single engineering indicator, the combined evaluation produced a broader representation of infrastructure performance.

The results demonstrated that integrating multiple decision variables provides greater transparency in maintenance planning because infrastructure sections with similar pavement conditions may exhibit substantially different priorities once operational and safety-related factors are incorporated into the evaluation process.

Yangon–Phyu and Phyu–Yangon Emerged as the Highest Maintenance Priorities

Among the evaluated road sections, the Yangon–Phyu segment received the highest overall maintenance priority, followed closely by the opposite travel direction, Phyu–Yangon. Both sections consistently exhibited elevated scores across several important indicators, including pavement deterioration, accident density, accident severity, traffic demand, and roadway geometry.

These findings indicate that maintenance prioritization should consider cumulative infrastructure risks rather than isolated pavement deterioration. The integrated framework therefore provides transportation agencies with stronger evidence for directing maintenance resources toward locations presenting the greatest overall operational need.

The Rule-Based System Improved Prioritization Within Maintenance Categories

The rule-based framework successfully classified pavement units into six maintenance strategies ranging from routine maintenance to complete reconstruction. Because multiple pavement units frequently belonged to the same maintenance category, cumulative ratings derived from PCI, IRI, PSR, and PSI were used to establish priorities within each class.

This additional ranking mechanism allows maintenance planners to distinguish pavement units requiring immediate intervention from others that remain within the same maintenance category but exhibit less severe overall deterioration.

AHP Enhanced Distress-Based Maintenance Decision-Making

The incorporation of the Analytic Hierarchy Process strengthened maintenance prioritization by assigning different importance levels to pavement distress types according to their contribution to pavement deterioration. Pairwise comparisons and consistency evaluation enabled a structured weighting process based on engineering evidence rather than subjective judgment alone.

This hierarchical evaluation supports more rational allocation of maintenance resources because pavement distresses with greater influence on infrastructure performance receive proportionally higher priority during decision-making.

The Framework Integrates Structural, Operational, and Safety Considerations

A notable outcome of the study is the successful integration of structural pavement indicators with operational characteristics and traffic safety variables within a single maintenance prioritization framework. Environmental exposure through rainfall and accessibility through village connections further broaden the scope of infrastructure evaluation.

The resulting framework demonstrates that comprehensive maintenance prioritization can better reflect real operational conditions than conventional approaches based exclusively on pavement condition measurements.

The Proposed Framework Demonstrates Practical Applicability for Infrastructure Management

Application of the methodology to eight expressway sections and 130 pavement sample units illustrates that the integrated framework can be implemented using engineering data commonly collected by highway agencies. The combination of quantitative scoring, rule-based reasoning, and AHP provides maintenance engineers with an interpretable and systematic decision-support tool.

Although developed using the Yangon–Mandalay Expressway, the overall methodology has the potential to be adapted to other highway systems where maintenance prioritization requires balancing infrastructure condition, traffic safety, operational demand, and resource limitations.

6. Scientific Contribution

  • Introduces an integrated maintenance prioritization framework that combines quantitative scoring, rule-based reasoning, and the Analytic Hierarchy Process into a unified engineering decision-support methodology.
  • Expands conventional pavement management approaches by incorporating safety, operational, accessibility, and environmental variables alongside traditional pavement condition indicators.
  • Develops a structured rule-based maintenance classification system that supports objective assignment of pavement units to appropriate maintenance strategies using four established pavement performance indicators.
  • Applies AHP to pavement distress prioritization using distress significance derived from regression analysis, thereby strengthening engineering-based weighting of maintenance decisions.
  • Provides a replicable methodology that can be adapted for maintenance prioritization in other transportation infrastructure systems with similar operational characteristics.
  • Demonstrates the value of integrating multiple engineering decision-making techniques to improve transparency, consistency, and effectiveness in infrastructure asset management.

7. Industrial Implications

  • Supports evidence-based highway maintenance planning. Transportation agencies can prioritize maintenance using multiple engineering criteria rather than relying solely on pavement condition assessments.
  • Improves allocation of maintenance budgets. Limited financial resources can be directed toward road sections presenting the highest combined operational, structural, and safety risks.
  • Enhances transportation safety. Incorporating accident density and accident severity into maintenance prioritization enables infrastructure managers to address locations with elevated traffic safety concerns.
  • Strengthens pavement asset management. The framework provides a systematic methodology for evaluating maintenance priorities throughout the pavement lifecycle, supporting longer infrastructure service life.
  • Supports digital engineering initiatives. The integration of quantitative evaluation methods, structured decision rules, and multi-criteria decision-making aligns with data-driven infrastructure management practices increasingly adopted within digital engineering environments.
  • Facilitates sustainable transportation infrastructure. Better maintenance prioritization contributes to extending pavement durability, improving operational efficiency, reducing unnecessary rehabilitation, and optimizing long-term infrastructure investment.
  • Provides a transferable framework for highway authorities. Because the methodology relies on widely available engineering data and established analytical techniques, it offers practical potential for implementation by road agencies responsible for national and regional highway networks.

8. Research Limitations

  • The proposed framework was evaluated using a single case study on the Yangon–Mandalay Expressway. Although the selected expressway represents an important transportation corridor, additional validation across different highway networks, climatic conditions, and traffic environments would further demonstrate the general applicability of the methodology.
  • The integrated scoring model relies on seven predefined evaluation parameters. Other engineering factors that may influence maintenance prioritization, such as axle load distribution, drainage performance, subgrade condition, maintenance history, or lifecycle cost analysis, were beyond the scope of the present study.
  • The rule-based maintenance classification is based on predefined threshold values derived from established pavement performance indicators. Future implementations may require calibration to accommodate regional maintenance standards, pavement characteristics, or agency-specific operational policies.
  • The Analytic Hierarchy Process (AHP) assigns relative importance to pavement distress types using structured pairwise comparisons. Although consistency testing improves reliability, the resulting weights remain dependent on the selected evaluation criteria and engineering judgment adopted within the analytical framework.
  • Traffic demand, accident records, and rainfall data represent the available conditions during the study period. Changes in future traffic growth, environmental conditions, or infrastructure usage may require periodic updating of the prioritization model to maintain decision accuracy.
  • The research focuses primarily on maintenance prioritization rather than economic optimization. Detailed cost-benefit analysis, maintenance scheduling, funding allocation, and lifecycle investment planning were outside the objectives of the present investigation.

9. Future Research Opportunities

  • Validate the proposed framework using expressway and highway networks in different countries to evaluate its adaptability under varying climatic, operational, and traffic conditions.
  • Integrate lifecycle cost analysis and economic optimization to support maintenance prioritization that simultaneously considers engineering performance and financial efficiency.
  • Incorporate real-time pavement monitoring technologies, intelligent transportation systems (ITS), and Internet of Things (IoT) sensors to enable dynamic maintenance prioritization.
  • Expand the decision-making framework by including additional engineering variables such as axle loading, drainage condition, bridge connectivity, pavement age, and historical maintenance records.
  • Compare the integrated framework with other multi-criteria decision-making techniques, including TOPSIS, VIKOR, PROMETHEE, DEMATEL, and fuzzy decision models.
  • Investigate the integration of machine learning and artificial intelligence techniques to improve prediction of pavement deterioration and maintenance prioritization.
  • Develop a GIS-based decision support system that visualizes maintenance priorities spatially to assist transportation agencies in infrastructure planning.
  • Evaluate the long-term effectiveness of the proposed framework by comparing predicted maintenance priorities with actual pavement performance following maintenance implementation.
  • Assess the environmental impacts of maintenance prioritization by incorporating carbon emissions, energy consumption, and sustainability indicators into future decision models.
  • Explore the applicability of the integrated methodology for other transportation infrastructure assets, including urban roads, bridges, airport pavements, and railway networks.

10. Potential for Public Policy Citation (Overton)

This article demonstrates meaningful potential for citation in public policy and infrastructure planning documents because it addresses a practical challenge faced by transportation agencies: allocating limited maintenance resources using transparent and evidence-based decision-making. The proposed framework integrates engineering performance, traffic safety, accessibility, and environmental considerations into a structured prioritization methodology, making it relevant for transportation governance beyond academic research.

The study could provide useful technical references for government agencies responsible for highway asset management, national road maintenance strategies, transportation infrastructure development plans, and engineering guidelines related to pavement management systems. Its emphasis on multi-criteria prioritization also aligns with broader objectives of sustainable infrastructure investment and data-driven public administration.

Although the research is based on a single expressway case study rather than nationwide implementation, the methodology itself is sufficiently general to support adaptation in technical manuals, infrastructure management frameworks, and long-term transportation planning documents. Consequently, the article possesses moderate-to-high potential for future citation within engineering policy literature, provided that subsequent studies further validate the framework under diverse operational environments.

11. Who Should Read This Paper?

  • Transportation infrastructure researchers.
  • Highway and pavement engineers.
  • Civil engineering graduate students.
  • Transportation planners and asset management specialists.
  • Government agencies responsible for road maintenance.
  • Infrastructure consultants and engineering practitioners.
  • Researchers working in pavement management systems and multi-criteria decision-making.
  • Professionals interested in sustainable transportation infrastructure.
  • Policy analysts involved in national transportation development.
  • Educators teaching transportation engineering, pavement engineering, and infrastructure management.

12. Final Thoughts

This study presents a well-structured and practically oriented approach to one of the most important challenges in transportation infrastructure management: determining where limited maintenance resources should be allocated. Rather than relying solely on conventional pavement condition indicators, the authors integrate quantitative scoring, rule-based reasoning, and the Analytic Hierarchy Process into a unified decision-support framework that considers structural condition, operational demand, traffic safety, accessibility, and environmental influences simultaneously.

One of the principal strengths of the research lies in its balanced integration of established engineering assessment methods. The methodology remains transparent and interpretable while expanding maintenance evaluation beyond traditional pavement performance metrics. Applying the framework to the Yangon–Mandalay Expressway demonstrates its capability to identify maintenance priorities systematically and consistently using multiple complementary decision criteria.

Although further validation across different transportation networks would strengthen its broader applicability, the proposed framework represents a valuable contribution to pavement asset management and infrastructure decision-making. The study illustrates how multi-criteria engineering analysis can support more efficient allocation of maintenance resources while promoting roadway safety, infrastructure sustainability, and evidence-based transportation planning. As transportation agencies increasingly adopt digital and data-driven asset management practices, integrated methodologies such as the one presented in this research are likely to become increasingly important for supporting resilient and sustainable highway infrastructure management.

Suggested Citation

UNP–Teknomekanik Style

Mon, N. H. Y., Kyaing, & Aye, M. T. T. (2025). Integrated decision-making strategies for expressway maintenance prioritization: Data-driven scoring, rule-based, and AHP approach. Innovation in Engineering, 2(1), 1–15. DOI: https://doi.org/10.58712/ie.v2i1.17

APA (7th Edition)

Mon, N. H. Y., Kyaing, & Aye, M. T. T. (2025). Integrated decision-making strategies for expressway maintenance prioritization: Data-driven scoring, rule-based, and AHP approach. Innovation in Engineering, 2(1), 1–15. https://doi.org/10.58712/ie.v2i1.17

IEEE Style

N. H. Y. Mon, Kyaing, and M. T. T. Aye, "Integrated decision-making strategies for expressway maintenance prioritization: Data-driven scoring, rule-based, and AHP approach," Innovation in Engineering, vol. 2, no. 1, pp. 1–15, 2025, doi: 10.58712/ie.v2i1.17 .

Harvard Style

Mon, N.H.Y., Kyaing & Aye, M.T.T., 2025. Integrated decision-making strategies for expressway maintenance prioritization: Data-driven scoring, rule-based, and AHP approach. Innovation in Engineering, 2(1), pp.1–15. Available at: https://doi.org/10.58712/ie.v2i1.17 .

Vancouver Style

Mon NHY, Kyaing, Aye MTT. Integrated decision-making strategies for expressway maintenance prioritization: Data-driven scoring, rule-based, and AHP approach. Innovation in Engineering. 2025;2(1):1–15. Available from: https://doi.org/10.58712/ie.v2i1.17

Chicago (Author–Date)

Mon, Nan Htike Yee, Kyaing, and Moe Thet Thet Aye. 2025. "Integrated Decision-Making Strategies for Expressway Maintenance Prioritization: Data-Driven Scoring, Rule-Based, and AHP Approach." Innovation in Engineering 2 (1): 1–15. https://doi.org/10.58712/ie.v2i1.17 .

MLA (9th Edition)

Mon, Nan Htike Yee, Kyaing, and Moe Thet Thet Aye. "Integrated Decision-Making Strategies for Expressway Maintenance Prioritization: Data-Driven Scoring, Rule-Based, and AHP Approach." Innovation in Engineering, vol. 2, no. 1, 2025, pp. 1–15. https://doi.org/10.58712/ie.v2i1.17 .

Editorial Note

Editorial Note: This blog post is an independent scholarly review intended for educational and scientific communication purposes. It summarizes and discusses the published article in the author's own words while providing full attribution to the original publication, consistent with the principles of the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

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