Enabling Sustainable Road Infrastructure: AI‑Empowered Innovation in Pavement Construction, Operation and Maintenance

Dear Colleagues,

Road infrastructure construction is undergoing a paradigm shift toward sustainability, driven by the urgent need to mitigate climate change and reduce environmental footprints. Concurrently, the rapid advancement of Artificial Intelligence (AI) technologies, particularly machine learning (ML), deep neural networks (DNN), and computer vision (CV), has opened unprecedented opportunities to revolutionize traditional road engineering practices. In the domain of asphalt pavement construction, AI-driven approaches have demonstrated remarkable potential in optimizing material design, predicting performance degradation, and enabling intelligent quality control, whose convergences are reshaping how sustainable road infrastructure is conceived, built, and maintained. Despite growing interest, several critical gaps remain for further exploration, including the integration of AI techniques with asphalt material design and maintenance, promoting the real-time capability of carbon footprint quantification by life cycle assessment (LCA) methodologies, predicting gaseous emissions from asphalt mixtures through AI-based predictive models and so forth.

Addressing these gaps is not merely an academic pursuit, but also a prerequisite for achieving global carbon neutrality targets in the infrastructure sector. This Special Issue seeks to bridge the divide between cutting-edge AI methodologies and sustainable road engineering, fostering a new generation of data-driven, environmentally conscious pavement solutions.

The primary aim of this Special Issue is to provide a platform for disseminating high-quality, original research that advances the understanding and application of AI technologies in promoting sustainable road infrastructure development. By focusing on the intersection of AI and sustainable road construction and maintenance, this Special Issue contributes to the journal's commitment to publishing transformative research that addresses global environmental challenges, aligning directly with the journal's core mission of advancing sustainable development and low-carbon ecological construction.

Topics covered in this Special Issue may include (but are not limited to) the following: 

Theme 1: AI-Driven Asphalt Pavement Material Design and Optimization

• Machine‑learning‑aided surrogate modelling for mix‑design of recycled and bio‑based asphalt materials;
• Multi‑objective performance environmental trade‑off optimization for sustainable pavement material design;
• AI-assisted development of low-carbon binder alternatives and warm-mix asphalt technologies;
• Physics‑informed predictive modelling for long‑term performance assessment of sustainable pavement materials.

Theme 2: Lifecycle Carbon Emission Quantification and Intelligent Assessment

• AI-enhanced lifecycle assessment (LCA) models for road infrastructure;
• Data-driven carbon footprint tracking across pavement construction and maintenance phases;
• Integration of building information modeling (BIM) with AI for real-time emission monitoring;
• Uncertainty quantification analysis for carbon‑accounting in road‑related life‑cycle assessment.

Theme 3: Machine Vision and Intelligent Construction Monitoring

• Automatic visual inspection for pavement paving and compaction‑quality assessment;
• Real‑time detection and identification of construction‑related defects and anomalies for on‑site construction monitoring;
• UAV-based intelligent surveying and 3D reconstruction for road infrastructure.

Theme 4: AI-Based Analysis of Pavement Gaseous Emissions and Pollutants

• Predictive modeling of VOC and fume emissions from asphalt mixtures using neural networks;
• Machine learning approaches to quantify and mitigate construction-phase air pollutants;
• Data-driven assessment of pavement-related environmental health impacts;
• Intelligent sensor networks for continuous emission monitoring.

Theme 5: Neural Network Analytics for Pavement Maintenance and Asset Management

• Image‑based pavement‑distress detection and severity‑level grading for condition evaluation;
• Predictive‑maintenance scheduling and life‑cycle‑cost optimization for road‑asset operation;
• Digital‑twin framework development for road‑infrastructure asset management;
• Cross‑regional pavement‑performance prediction under heterogeneous geographical conditions.

Theme 6: Data Fusion and Intelligent Decision Support Systems

• Multi-source data fusion (sensor‑derived measurements, image datasets, together with contextual environmental metadata) for holistic pavement analysis;
• Intelligent compaction system development and construction‑process optimization for pavement construction;
• Explainable AI (XAI) for transparent decision-making in sustainable pavement engineering;
• Edge computing and IoT-enabled intelligent road systems;
• Standardization and benchmarking of AI models in pavement engineering applications.

Dr. Fusong Wang
Dr. Heyu Zhou
Dr. Weijie Wang
Dr. Zenggang Zhao
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• infrastructure sustainability
• pavement construction
• AI-based analysis
• intelligent maintenance
• environmental impacts