Veltin, K, McMillan, R & Lawrence, K 2025, 'Enhancing data quality and transparency in geotechnical data collection', in JJ Potter & J Wesseloo (eds), SSIM 2025: Fourth International Slope Stability in Mining Conference, Australian Centre for Geomechanics, Perth, https://doi.org/10.36487/ACG_repo/2535_45
(https://papers.acg.uwa.edu.au/p/2535_45_Lawrence/)
Abstract: Confidence and reliability in the data obtained from field programs are an important, if not the most critical, component of geotechnical design. Geotechnical data quality is of the utmost importance, and achieving highquality geotechnical data necessitates stringent standards, meticulous procedures, real-time quality assurance and quality control (QAQC), and transparent communication with ongoing refinement. Though QAQC is pivotal in improving confidence in geotechnical datasets, it can often involve repetitive and laborious tasks. Recognising this, the authors identified the need for improved and automated processes to streamline routine QAQC tasks, thereby providing field staff and engineers with the time to focus on conceptual comprehension and rock mass characterisation. A set of Python tools was developed to automatically identify and reconcile errors in geotechnical core logging data. These processes identify inconsistencies such as incongruent depth values, oriented core angles exceeding logical limits, inflated Rock Quality Designation (RQD) values for weak rock, and instances of omissions and typographical errors. Moreover, the tools generate geotechnical strip blogs to visually represent the data, assisting in the review and identification of conceptual errors such as disparities between field estimate strength and point load indices and inaccuracies in fault classification based on logged characteristics.
By automating these processes, QAQC can be completed daily by an onsite field supervisor or offsite engineering support team, providing immediate feedback to the data-collection personnel. This improves communication during the execution of large field programs, increasing the knowledge and confidence of morejunior field staff, and resulting in higher-quality data for use in future geotechnical designs. The processes are currently being integrated into a data-management and QAQC framework, underpinned by a novel project dashboard, which details QAQC, execution, and health and safety metrics and is accessible to clients, field personnel, and offsite engineering support. Additional features of the QAQC framework are also being considered based on user and client feedback. This framework has been applied in recent largescale drilling programs involving multiple drill rigs and logging personnel and has demonstrated reduced logging errors, validating its effectiveness in improving confidence in the collected data and collaboration and transparency among key stakeholders.

Keywords: quality control, quality assurance, automation, geotechnical data