Call for Papers
Theme of the Conference
AIRISE 2027 invites industry professionals/ academicians/ researchers to submit their original, previously unpublished, and high-quality research papers. The conference will be focused on addressing research challenges in the field of Sustainability Analysis of Materials and AI Applications in Infrastructure, but not limited to the following:
Track-1: Artificial Intelligence for Structural Health Monitoring and Infrastructure Resilience
- AI-based structural damage detection and condition assessment
- Smart sensors, IoT, and real-time infrastructure monitoring
- Predictive maintenance and resilience modeling of critical infrastructure
- AI applications for seismic, wind, and multi-hazard risk assessment
Track-2: AI-Driven Design, Optimization, and Performance Modeling of Sustainable Engineering Materials
- AI-assisted mix design and optimization of sustainable materials
- Machine learning for strength, durability, and service-life prediction
- Data-driven modeling of geopolymer, recycled, and low-carbon materials
- AI for smart, self-healing, and bio-inspired construction materials
- Multi-objective optimization of material performance, cost, and sustainability
Track-3: Life Cycle Assessment, Carbon Modeling, and Circular Infrastructure Systems
- AI-enabled life cycle assessment and embodied carbon analysis
- Carbon modeling and net-zero strategies for buildings and infrastructure
- Circular economy approaches in construction materials and systems
- Resource efficiency, waste valorization, and material flow intelligence
- Integrated life cycle cost, carbon, and sustainability decision frameworks
Track-4: Smart Construction Technologies, Robotics, and Digital Twins
- Robotics, automation, and autonomous systems in construction
- Digital twins for infrastructure monitoring, control, and asset management
- BIM, AI, and data integration for smart project delivery
- 3D printing, additive manufacturing, and advanced construction methods
Track-5: AI & ML in Water Resources Management and Engineering
- AI/ML-based hydrological modeling, runoff prediction, and flood forecasting
- Smart water distribution systems, leak detection, and demand prediction
- Machine learning for groundwater assessment, recharge estimation, and contamination mapping
- AI-enabled water quality monitoring, treatment optimization, and wastewater management
- Data-driven decision support for irrigation systems, reservoir operation, and sustainable water resources planning