AI-Enabled Non-Conformance Prediction and Corrective Action Management in Saudi Mega Infrastructure Projects

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@article{1720112,
      author = {Ziyad Adil Khan},
      title = {AI-Enabled Non-Conformance Prediction and Corrective Action Management in Saudi Mega Infrastructure Projects},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {1},
      pages = {3285-3296},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1720112.pdf},
      abstract = {Saudi Arabia's mega infrastructure pipeline under Vision 2030 requires quality systems that can prevent recurring defects rather than merely record them after inspection. Non-conformance reports (NCRs), corrective actions, preventive actions, audit findings, work inspection requests, laboratory test results and supplier quality records contain large volumes of information about recurring project risks. However, these records are often fragmented across packages, contractors, consultants and digital platforms, limiting their value for early warning, root-cause learning and management review. This review paper examines how artificial intelligence (AI) can enable predictive non-conformance management and closed-loop corrective action management in Saudi mega infrastructure projects. Drawing on recent literature on construction quality management, digital QA/QC, machine learning, natural language processing, explainable AI, digital twins, Quality 4.0 and ISO-based quality governance, the study develops a conceptual framework for converting historical NCR and CAPA records into actionable intelligence. The proposed framework integrates data governance, standardised NCR taxonomy, risk-feature engineering, AI-enabled severity and recurrence prediction, root-cause clustering, human validation, CAPA prioritisation, effectiveness verification and continuous learning. The paper argues that AI-enabled NCR prediction should not replace professional judgement; rather, it should support quality managers, client organisations and project management consultants by identifying high-risk patterns earlier and by strengthening accountability for preventive action. The contribution is a Saudi Vision 2030-aligned quality governance model that connects digital transformation with sustainable infrastructure delivery, reduced rework, improved traceability, faster NCR closure and better handover readiness.},
      keywords = {Artificial Intelligence, Non-Conformance Report, Corrective and Preventive Action, Construction Quality Management, Saudi Vision 2030, Mega Infrastructure, QA/QC, Predictive Analytics, ISO 9001, Quality 4.0.},
      month = {July},
      doi = {https://doi.org/10.64388/IREV10I1-1720112}
  }