Data-Driven Measurement Uncertainty and Quality Risk Management in Saudi Arabia’s Power and Infrastructure Projects

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@article{1723863,
      author = {Umair Hussain},
      title = {Data-Driven Measurement Uncertainty and Quality Risk Management in Saudi Arabia’s Power and Infrastructure Projects},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {930-941},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723863.pdf},
      abstract = {Measurement uncertainty is often treated as a laboratory reporting requirement, while project quality risk is managed through separate inspection, nonconformance, and contractual processes. This review argues that the separation is increasingly inadequate for Saudi Arabia’s rapidly expanding power and infrastructure portfolio, where digital measurement streams, intelligent inspection, building information modeling, and asset analytics are becoming central to acceptance decisions. The paper synthesizes recent literature on metrological uncertainty, data quality, machine learning, digital twins, power-system condition monitoring, computer vision, and Saudi construction governance to develop an integrated view of measurement-informed quality risk. The review shows that uncertainty enters projects through instrument calibration, sampling, environmental effects, synchronization, data transmission, model error, human interpretation, and algorithmic prediction. These sources can propagate into false acceptance, false rejection, unnecessary rework, latent defects, schedule disruption, and lifecycle reliability loss. A structured framework transforms traceable measurements into uncertainty-aware conformity evidence, links it to risk severity and consequence, and updates it through digital project records. The synthesis further indicates that Saudi implementation should prioritize common data semantics, calibrated digital instruments, uncertainty budgets for critical tests, model validation under local operating conditions, auditable decision rules, and governance that connects project quality records with asset-management systems. The review concludes that data-driven quality management should not seek to eliminate uncertainty, but to quantify, communicate, and incorporate it explicitly into engineering decisions.},
      keywords = {Measurement uncertainty; quality risk; metrology; power infrastructure; digital twin; construction quality; Saudi Arabia; data-driven inspection},
      month = {October},
  }