The growing challenges of climate change, urbanization, and resource scarcity demand innovative approaches to designing infrastructure that is not only intelligent and adaptive but also environmentally sustainable and socially responsible. This Collection in Discover Civil Engineering aims to showcase the latest research at the intersection of machine learning (ML), smart materials, and infrastructure engineering to promote systems that are more resilient, efficient, and aligned with the United Nations Sustainable Development Goals (SDGs).

We invite original research articles, reviews, and case studies that demonstrate the transformative role of ML in the development, characterization, and application of smart materials for future-ready civil infrastructure. Key areas of interest include, but are not limited to:

· ML-assisted predictive modeling of smart material properties and behaviors

· Sustainable design and optimization of smart construction materials (e.g., self-sensing concrete, phase-change materials, piezoelectric composites)

· ML-driven sensor fusion and data analytics for structural health monitoring and predictive maintenance

· Data-driven lifecycle assessment of smart materials contributing to reduced carbon footprint and resource efficiency

· Integration of ML in adaptive infrastructure systems for climate resilience and disaster mitigation

· Hybrid frameworks combining physical models with ML to enhance performance and interpretability

· Applications of deep learning, reinforcement learning, and transfer learning in infrastructure material systems

· Case studies demonstrating measurable impacts on sustainability, resilience, and SDG alignment

This Collection seeks to build a platform for interdisciplinary collaboration among civil engineers, material scientists, environmental researchers, and data scientists. Contributions should aim to demonstrate how ML can accelerate the deployment of smart materials to meet global sustainability goals and build resilient infrastructure for the future.

This Collection supports and amplifies research related to: SDG 9, and SDG 11.

Keywords: Machine learning, smart cities, sustainable infrastructure, SDGs, urban resilience, digital twins, intelligent transportation, energy efficiency, climate adaptation, predictive maintenance

The market for 3D printing buildings is experiencing rapid growth worldwide. As reported by Straits Research, the global 3D printing construction market size was worth USD 1,10 billion in 2021 and it is expected to reach USD 585,84 billion by 2030, growing at an impressive CAGR during the upcoming period (2022-2030). Construction companies, technology providers, and architectural firms are actively exploring and investing in 3D printing technologies to enhance efficiency, reduce costs, and address sustainability concerns. In few words, the implementation of additive manufacturing in the construction industry is globally on the rise.

In this context, additive manufacturing has become a very attractive topic for researchers too, specifically for those working in the field of construction materials, design of structures and construction technologies. Current research aims to solve some gaps still currently present when designing, testing, assessing, and modeling 3D printed structures.

With this Topical Collection, entitled “New Trends in Additive Manufacturing for Sustainable Construction Materials”, we invite the scientific community to report on the most recent advances, novel insights, and case-studies on the following topics (but not limited to):

• Technologies for AM of construction materials;

• Development, optimization, testing eco-efficient printable materials;

• Durability, deterioration mechanisms, service life evaluation of 3D printed building materials;

• Artificial Intelligence applied to 3DP, material development and printing processes;

• Structural analysis, modelling, assessment and testing of additively manufactured materials and construction elements;

• Structural health monitoring of 3D-printed structures;

• Life cycle analysis (LCA), life cycle cost (LCC) and multi-criteria decision analysis (MCDA) of additively manufactured building materials and construction elements;

• Integration of AM into project management and building information modeling tools (BIM);

• Legal frameworks in the field of construction authorization practices for 3D-printed structures;

• Relevant case-studies (design, testing, field production).

Keywords: Additive manufacturing; sustainability; 3D printing; LCA; recycled materials; concrete; metals; earth-materials; geopolymers; digitalization.