EPIA 2026

2 - 4 Sep, 2026 University of Madeira, Colégio dos Jesuítas do Funchal
Promoting research in all areas of AI — theory, foundations and applications. Hosted with the patronage of APPIA.

AI for Architecture, Engineering and Conservation

Track Description

We are excited to invite submissions for the upcoming special issue/conference track on AI for Architecture, Engineering, and Conservation. This track highlights the methodological advancements in Artificial Intelligence within the above-mentioned sectors and beyond, covering advancements such as customized large foundation models, computer vision, and AI frameworks applied to infrastructure resilience, monitoring, and construction management. These technologies, including object detection, predictive modeling, and feature extraction, are driving innovation in identifying and addressing defects across areas such as climate-resilient infrastructure, cultural heritage preservation, structural integrity, and environmental monitoring. AI is increasingly vital for tasks like detecting deterioration, reconstructing historical artifacts in 3D, and predictive maintenance of pavements, water systems, and other critical infrastructure. With growing access to data from satellites, drones, IoT sensors, and global collaborations, AI is revolutionizing how we analyze, understand, and act on complex, large-scale datasets in diverse infrastructure domains. The track highlights innovations in AI-driven methods, fostering collaboration between researchers, practitioners, and industry experts working at the intersection of architectural and urban design, engineering, environmental monitoring, and conservation.

Topics of Interest

  • AI-based defect detection for infrastructure and heritage
  • AI for digital twins in infrastructure life cycle, monitoring and urban development
  • IoT monitoring using AI and Data Stream Mining
  • UAV and LiDAR data analysis
  • AI in material analysis and non-destructive testing
  • 3D reconstruction and modeling of artifacts and structures
  • AI for predictive maintenance
  • Data-driven approaches to classify and monitor deterioration patterns
  • AI applications in urban planning and architecture
  • Software architecture analysis and evaluation for AI systems in conservation and engineering
  • AI for Climate-Resilient Infrastructure
  • AI Frameworks for Infrastructure Monitoring

Track Chairs

  • Daniele Corradetti - Instituto Superior Técnico
  • Nuno Marques - Universidade Nova de Lisboa
  • Roberta Spallone - Politecnico di Torino

Steering Committee

  • José Delgado Rodrigues
  • João Gama
  • Michele Russo
  • Daniele Corradetti
  • Nuno Marques
  • Roberta Spallone

Sponsors & Partners