AI-Based Material Design, Performance Evaluation and Construction Quality Control of Asphalt Pavement
A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Construction and Building Materials".
Deadline for manuscript submissions: 20 March 2026 | Viewed by 917
Special Issue Editors
Interests: multi-physical/multi-scale characterization of pavement materials; genome encoding and AI-driven design of materials; intelligent construction and monitoring of structure
Special Issues, Collections and Topics in MDPI journals
Interests: multiscale mechanical design of asphalt/cement-based material; intelligent monitoring; resource utilization design of solid waste materials; reliability assessment of material design
Special Issues, Collections and Topics in MDPI journals
Interests: road materials and pavement design; nondestructive testing technology; structural health monitoring; bridge and infrastructure engineering
Special Issues, Collections and Topics in MDPI journals
Interests: structural durability; bayesian updating; structural reliability; value of information; decision making
Interests: asphalt pavement recycling; sustainable pavement materials; mechnical performance evaluation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Amid the dual challenges of accelerated aging of asphalt pavement and the deepening implementation of the "dual-carbon" strategy (carbon peak and carbon neutrality), the development of pavement materials that combine green and low-carbon attributes, environmental adaptability, and long-term durability has became a critical solution to mitigating escalating challenges posed by heavy traffic loads and extreme weather conditions in complex service environments. Current research on asphalt pavement materials mainly relies on scientists' continuous exploration of complex theories and the gradual accumulation of experimental data, a process often characterized by long cycles and low efficiency, severely hindering the rapid development and practical application of performance-oriented new-material design. With the rapid advancement of artificial intelligence (AI), guided by the Materials Genome Initiative (MGI) framework and empowered by high-throughput computing and AI-driven approaches, it is now possible to overcome the spatiotemporal limitations of traditional trial-and-error material design methods in pavement. This enables precise analysis and inverse design of composition-structure-performance relationships in materials. Such an AI-aided paradigm, which integrates data-driven approaches with fundamental physical mechanisms, dramatically improves both the efficiency of performance-targeted pavement material design and its inherent adaptive capabilities.
This Special Issue, entitled “AI-Based Material Design, Performance Evaluation and Construction Quality Control of Asphalt Pavement”, aims to gather original research papers related to the performance prediction and intelligent design of bituminous materials. The scope of this Special Issue includes, but is not limited to, the following topics:
- High-throughput computing and evaluation of asphalt/cement-based material;
- Genome encoding and AI-driven design of asphalt/cement-based material;
- Multi-physical/multi-scale characterization of asphalt/cement-based material;
- Mechanical inversion and reverse design of asphalt/cement-based material;
- Intelligent construction and quality assessment of pavement structure;
- Intelligent monitoring and risk assessment technology of pavement structure;
- Green and sustainable materials design and durability assessment of pavements.
You may choose our Joint Special Issue in Applied Sciences.
Dr. Xunhao Ding
Dr. Yanshun Jia
Dr. Wensheng Wang
Guest Editors
Dr. Xiong Xiao
Guest Editor Assistant
Dr. Bo Li
Guest Editor
Manuscript Submission Information
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Keywords
- asphalt/cement-based material
- high-throughput computing
- genome interpretation of materials
- AI-driven inverse design of materials
- pavement performance evaluation
- green and sustainable materials
- intelligent construction and monitoring
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