Pavement Engineering Overview

Principles

Definition

Pavement engineering is a research discipline focused on the principles and practices for the design, construction, evaluation, and preservation of rigid and flexible pavements. This field integrates concepts from materials science, structural mechanics, and geotechnical engineering to optimize pavement performance, safety, and economic efficiency under varying traffic and environmental conditions.

Ontological type

Pavement Materials

Design Methodologies

Structural Performance

Key developments

Key Figures

Physics-based Predictive Modeling era

Dallas N. Little [1] is a leading figure in pavement engineering whose work in this era spanned institutions such as Texas A&M University [3] and Washington State University [4]. His key contribution in this era is the One‐Dimensional Constitutive Modeling of Asphalt Concrete [6], published in 1990, which supplied a tractable constitutive framework for predicting asphalt behavior under traffic and climate and thereby enabled calibration and validation of physics-based mechanistic-empirical models. Y. Richard Kim [2] is a prominent figure associated with North Carolina State University [5] during this era. Kim's work on the One‐Dimensional Constitutive Modeling of Asphalt Concrete [6] helped advance physics-based predictive pavement modeling by providing a practical framework for interpreting material response under service conditions, supporting mechanistic design and reliability analyses in the era.

Sustainable Material Innovation era

Fereidoon Moghadas Nejad [1] has affiliations with École Polytechnique Fédérale de Lausanne [2] and Kansas State University [3] within the Sustainable Material Innovation era (2004-2017). In this era, his key contribution was the development of nonlinear genetic-based models for predicting flow number of asphalt mixtures, demonstrated in the 2010 paper Nonlinear Genetic-Based Models for Prediction of Flow Number of Asphalt Mixtures [4], which offered improved predictive capability for pavement performance and supported sustainable mix design. The author [1] extended the impact of this work by reinforcing predictive modeling as a central tool for durable, low-carbon pavements in practice. Nejad's contributions in 2004-2017 exemplify how nonlinear, data-driven approaches can drive the adoption of sustainable asphalt technologies [1].

AI-driven Pavement Management era

Haibin Ling [1] is a pavement engineering researcher associated with the State University of New York [2] and Stony Brook University [3] during the AI-driven Pavement Management era (2018–2024). A key contribution is the Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection [4], which advances multi-scale defect detection in pavement imagery and supports automated inspection in AI-enabled management systems. This work laid groundwork for automated inspection pipelines that enable data-informed maintenance decisions, aligning with the era's push toward probabilistic life-cycle forecasts and predictive maintenance in AI-driven pavement management [4]. Ling [1] continued to influence AI-based inspection approaches in this era, illustrating how robust crack-detection techniques can underpin management deployment and data-driven decision making.