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AI in the Built Environment: Opportunities, Risks, and Future Directions

artificial intelligence built environment construction urban planning ai governance machine learning

Building Research & Information

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Editors

  • Sara Wilkinson, University of Technology Sydney
  • Johnny Wong, University of Technology Sydney
  • Biyanka Ekanayake, University of Technology Sydney

Description

This special issue examines how artificial intelligence is transforming the built environment across construction, real estate, facilities management, and urban planning. While AI technologies offer significant opportunities for innovation and efficiency through applications like energy optimization, smart building control, and predictive maintenance, the sector faces critical challenges regarding data integrity, algorithmic bias, privacy, and transparent decision-making.

The special issue seeks high-quality original research exploring AI's use, impact, risks, and future potential in the built environment. It welcomes contributions spanning construction automation, property valuation, smart buildings, urban planning, generative design, AI governance and ethics, sustainability applications, security systems, human-AI collaboration, and professional education in AI.

Potential topics

  • AI in construction: automation, robotics, quality control, progress monitoring, risk and safety analytics
  • AI in property and real estate: valuation models, market intelligence, customer engagement, asset management, predictive analytics
  • Smart buildings and facilities management: IoT-enabled optimisation, energy management, predictive maintenance, AI-driven knowledge systems
  • AI in urban planning and infrastructure: spatial modelling, forecasting, environmental assessment, transport and mobility analytics
  • Design and engineering applications: generative design, simulation, optimisation, digital twins, parametric modelling
  • Explainable AI (XAI): AI governance, ethics, and regulation: transparency, bias, data integrity, privacy, and responsible GenAI in the built environment
  • AI for sustainability and ESG: carbon modelling, resource optimisation, lifecycle assessment
  • Security and risk management: computer vision for surveillance, access control, anomaly detection
  • Multimodal and agentic AI: advanced AI systems applied to complex built environment challenges
  • Human-AI collaboration: professional adoption, skills, workflows, and organisational change
  • The challenge of educating and training built environment professionals in AI