<![CDATA[The reliability of laboratory testing data is a critical factor in ensuring effective quality assurance within asphalt materials engineering. However, current reporting workflows for asphalt rheological tests, including Kinematic Viscosity and Dynamic Shear Rheometer (DSR) measurements, still rely on manual spreadsheet-based procedures that are prone to human error, inefficiency, and operator-dependent variability. This study proposes a deterministic computational validation framework that transforms conventional semi-subjective data processing into a fully automated, rule-based evaluation system. The framework integrates Python-based non-linear regression with JSON-based data serialisation to standardise computational workflows and ensure reproducibility. System performance was evaluated using a post-facto comparative analysis of 20 historical laboratory datasets. Processing efficiency was assessed using a paired sample t-test, while numerical data accuracy was evaluated using the root mean square error (RMSE). The results indicate that the automated computational system reduced the average processing time per sample from 7.47 minutes to 1.38 minutes (t(19) = 20.32, p < 0.001), corresponding to an efficiency improvement of 81.60%. In addition, deterministic processing reduced operator-induced variability, improving accuracy from 99.12% (RMSE = 1.37) to 99.78% (RMSE = 0.34). These findings demonstrate that deterministic computational validation not only improves efficiency and accuracy but also enables reproducible, traceable, and operator-independent laboratory reporting, supporting the advancement of a digital quality assurance system. Keywords: Laboratory Automation, Asphalt Rheology, Dynamic Shear Rheometer (DSR), Data Integrity, Deterministic Validation]]>
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