Proceedings of International Conference on Applied Innovation in IT  ·  2026/07/22  ·  Vol. 14  ·  Issue 4  ·  pp. 1479–1485
Integrating Data-Driven Pavement Management and Rheological Optimization for Sustainable Asphalt Pavements
Kornél Almássy, Saidkamolhon Jamolov and Mirzokhid Abdurazаkov
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Abstract
Urban asphalt pavements are increasingly subjected to combined effects of traffic growth, climatic variability, and environmental constraints, resulting in accelerated deterioration and reduced service life. Traditional Pavement Management Systems (PMS) effectively support decisions related to maintenance timing and prioritization; but they rarely incorporate material-level strategies aimed at improving pavement durability and resilience. This paper proposes a data-driven framework that integrates digital PMS-based resilience assessment with rheological optimization strategies for asphalt pavements. Pavement condition indicators, including roughness, traffic loading, and environmental exposure, are utilized as inputs for a digital decision-support model to evaluate pavement resilience in terms of performance loss and recovery. Based on the resilience assessment results, material-level solutions derived from previous experimental studies on asphalt modification using surface-active materials and additives are introduced to enhance rheological properties and durability. The proposed approach enables informed selection of asphalt mixtures that reduce deterioration rates, maintenance frequency, and associated environmental impacts. By linking system-level digital decision models with material-level engineering solutions, the framework supports sustainable pavement management and contributes to the development of intelligent transport infrastructure systems.
Keywords
Pavement Management System (PMS) Resilience Index Digital Decision Models Asphalt Durability Rheological Optimization Sustainable Infrastructure Roughness Material Selection Algorithm
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