Monitoring to model: harnessing extensometer data to enhance the geotechnical model for better predictions

Authors: Wilson, A; Subur, R; Cowan, M; Barnett, D Download Paper

DOI https://doi.org/10.36487/ACG_repo/2625_35

Cite As:
Wilson, A, Subur, R, Cowan, M & Barnett, D 2026, 'Monitoring to model: harnessing extensometer data to enhance the geotechnical model for better predictions', in D Cumming-Potvin & J Wesseloo (eds), Ground Support 2026: Proceedings of the 11th International Conference on Ground Support in Mining, Australian Centre for Geomechanics, Perth, pp. 1-14, https://doi.org/10.36487/ACG_repo/2625_35



Abstract:
Extensometers in their various forms are used in underground applications for the monitoring of rock displacement or deformation and to manage risk associated where large spans, narrow pillars or squeezing ground are present (to name a few). Whilst the data obtained from these instruments may primarily be most useful in giving warning prior to movement or failure, the data can also be used to identify the method in which failures occur at the site-specific scale and may well prove to be useful tools in calibrating numerical models used in geotechnical planning and design of mining shapes. In this fashion, the extensometer can and should also be viewed as a tool in the iterative process of model calibration for underground operations, rather than just as a warning device. To ensure the integrity and reliability of such an instrument when used in this way; however, the full process from instrument installation, monitoring and data collection, data interrogation, analysis, and model evaluation needs to be planned and executed correctly. Tropicana Gold Mine (TGM) has used SMART cable data in conjunction with other underground rock failure data to calibrate its primary numerical model and determine the likely mode of failure/deformation expected mining depths where monitoring took place. This case study focuses on a scenario where SMART cable data was obtained from the backs of a 4-way intersection inside a large, multi-stage bulk stope, and although the instrument was installed primarily for risk management purposes, the data has shown its worth in numerical model calibration. Additionally, the data demonstrates the potential for this methodology to evaluate ground support performance and to assist with forecasting future ground support requirements at depth (pending further independent validation). The installation process, data interrogation and analysis steps are outlined throughout as a reference for others attempting a similar endeavour of model calibration using underground extensometers.

Keywords: extensometer, numerical modelling, model calibration, ground support, instrumentation

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