Assessing the AI Readiness of Construction Datasets: A Multidimensional Operational Framework

Key points

• However, construction datasets vary widely in documentation, quality, governance, and evaluation support, and consistent criteria for assessing AI readiness remain limited.1
• This study develops a Construction Dataset AI Readiness Framework with 30 attributes across four dimensions: Metadata Completeness, Data Quality and Representativeness, Data Governance and Accessibility, and AI Utility.1
• The framework uses attribute-specific scoring, applicability screening, and automated and manual verification.1
• The framework supports consistent assessment of heterogeneous construction datasets and identification of readiness gaps.1

Sources (1)

• [1]Assessing the AI Readiness of Construction Datasets: A Multidimensional Operational Framework
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 07:38 PM

  The rapid growth of artificial intelligence (AI) applications in construction has increased the demand for datasets suitable for reliable model development, evaluation, and reuse.

  However, construction datasets vary widely in documentation, quality, governance, and evaluation support, and consistent criteria for assessing AI readiness remain limited.

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