Building Sustainability Using Statistical, Geospatial, Artificial Intelligence and Machine Learning Techniques

Sustainability in the built environment is a critical global priority, shaped by pressures related to energy demand, raw material consumption, carbon emissions, and accelerating climate change. At the same time, rapid progress in cloud-based data logging and analytics together with the expanding reach of the Internet of Things (IoT) is transforming how infrastructure can be monitored, managed, and maintained. These developments create strong opportunities for artificial intelligence (AI) and machine learning (ML) to support decision-making across the infrastructure life cycle.Aging management of structures increasingly relies on integrated approaches that combine statistical analysis, geospatial methods, and AI/ML tools to enable more timely, robust, and cost-effective interventions. This Special Collection will address these challenges and opportunities, focusing on how data-driven and geospatially enabled methods can improve the sustainability, resilience, and performance of infrastructure systems.Aims and ScopeThe goal of this Special Collection is to highlight advantages, innovations, and practical implementations of geospatial, statistical, and AI/ML techniques applied to infrastructure. Contributions may span the full infrastructure life cycle from construction materials and construction practices to assessment of existing assets using non-destructive or partially destructive tests, structural health monitoring (SHM), and risk-informed decision frameworks.The collection also welcomes work on corrosion prevention, durability design, and risk-informed life extension strategies that improve long-term asset performance while reducing environmental and economic costs. Advancing sustainable infrastructure directly supports Sustainable Development Goal (SDG) 9 (Industry, Innovation and Infrastructure) and SDG 11 (Sustainable Cities and Communities), contributing to the 2030 Sustainable Development Agenda.Article TypesThis Special Collection welcomes: Original Research, Reviews, Technical Notes, and Case Studies.Authors are encouraged to emphasize actionable, real-world applications that support sustainable infrastructure through statistical, GIS-based, and AI/ML-driven methods.Keywords / Themes (non-exhaustive)The scope includes, but is not limited to:o Design of corrosion-free structureso Risk-informed life extension of structureso Structural health monitoring (SHM)o Condition and health assessment of structureso Aging management of the built environmento IoT and geospatial techniques for infrastructure managemento Non-destructive and partially destructive testing: sampling, execution, and interpretationo Machine learning applications for SHM and condition/health assessmento Artificial intelligence in SHMo Materials for sustainable infrastructureo Related topics aligned with the Research Topic theme and objectives Topic editors: Saha Dauji, Punyajoy Saha, Sharvil Alex Faroz Source: https://www.frontiersin.org/research-topics/78386/building-sustainability-using-statistical-geospatial-artificial-intelligence-and-machine-learning-techniques