Understanding the dynamics of granular flow on inclined surfaces is critical for predicting and mitigating hazards in natural and industrial processes [1-2]. Such flows play a major role in natural disasters, including landslides, snow avalanches, and debris flows, where accurate modelling can significantly reduce risks to life and infrastructure [3]. Conventional modelling techniques often rely on discrete element method (DEM) simulations, which predict the motion of individual grains by solving Newton’s laws of motion combined with constitutive models for contact forces and particle interactions, including overlaps between grains and with boundaries. While DEM provides highly accurate representations of non-uniform granular flow, its computational demands are extremely high, limiting the number of particles that can be simulated. As a result, applying DEM to large-scale or real-world granular flow scenarios becomes impractical despite its precision [4]. In this study, we present a continuum model for simulation of granular collapse using the μ(I)-rheology method [5]. Initially, we employ granular collapse experiments to develop a fast, simple and inexpensive method for evaluating the values of the rheological material properties of any granular material. Following validation against experimental data for granular collapses, a systematic analysis is conducted to examine how material properties affect the flow behaviour of granular materials on flat and inclined surfaces. The findings indicate that during collapse, the granular medium segregates into two distinct zones: a quasi-static region characterized by low shear rates and a flowing region where the material exhibits non-Newtonian fluid behaviour. Overall, this work provides a framework for exploring complex granular flows under diverse conditions, offering significant benefits for risk assessment and design optimization in engineering and geophysical applications. The findings enhance our understanding of granular material flow and support the development of preventative strategies to minimize potential hazards.