Rock Mass Rating (RMR)

Rock Mass Rating (RMR)

When you look at a bench face, you are only seeing a fraction of the story. To achieve an optimal blast, you need to understand the structural integrity of the rock hidden into the bench. That is where Rock Mass Rating (RMR) comes in.

Originally developed for underground mining and tunneling to predict roof cave-ins and support requirements, RMR has become a vital metric for surface drill and blast engineers. But what exactly is it, and how does Strayos help you calculate it automatically?

1. Prerequisites

Before running the RMR Tool, ensure you have completed the following:

  1. Processed 3D Model: You must have a fully processed drone photogrammetry model of your site.

  2. Rock Mass AI Completed: You must run the Rock Mass AI module first to automatically detect, map, and cluster the joints, bedding planes, and fractures on your rock face.

Info

2. What is RMR?

Originally developed by Z.T. Bieniawski in 1973, the Rock Mass Rating (RMR) system is a comprehensive geotechnical classification method used to evaluate the overall quality of a rock mass. Rather than looking at just one isolated variable, RMR combines multiple geological and structural parameters into a single, practical rating (from 0 to 100).

The traditional RMR classification is constructed from five fundamental components:

  1. Strength of intact rock: How much raw force the solid rock can withstand.

  2. Rock Quality Designation (RQD): A quantifiable metric of how heavily fractured the ground is, usually derived from core samples.

  3. Spacing of Discontinuities: The physical distance between natural breaks, joints, or faults in the rock.

  4. Condition of Discontinuities: The characteristics of the fractures themselves (e.g., aperture width, surface roughness, weathering, and clay infilling).

  5. Groundwater Conditions: The presence and pressure of water within the rock mass, which acts as a natural lubricant and weakens structural integrity.

By summing the ratings of these components, engineers classify the rock mass into categories ranging from "Very Poor" to "Very Good." This classification serves as a baseline for engineering applications like tunneling, slope stability analysis, and, crucially, drill and blast design.



3. Why Bring RMR into Drill and Blast?

For decades, RMR was considered strictly "geotech data" for calculating static slope stability. However, research has proven that RMR directly dictates the dynamic stability of the rock—specifically, the Peak Particle Velocity (PPV) thresholds required to initiate rock damage.

If you treat a bench as a uniform block and apply a standard powder factor, you are flying blind. Understanding the RMR allows you to safely associate threshold values directly with your Powder Factor:

  • High RMR (e.g., 60–100): The rock mass is highly competent. You can confidently apply a higher powder factor to overcome the rock's natural strength and achieve your target fragmentation.

  • Low RMR (e.g., < 40): The rock is heavily fractured or weathered. Applying a standard powder factor here will exceed the PPV threshold, resulting in blown-out faces, excessive fines, dangerous flyrock, and severe damage to the remaining highwall.

4. How Strayos Calculates RMR and RQD in 3D

Traditionally, RMR is assessed manually by geologists looking at a few discrete "characteristic windows" on a rock face, leaving you to guess the rock quality deep inside the bench.

Strayos adapts this geomechanical standard directly into the drill and blast workflow by taking the structural data generated by our Rock Mass AI and projecting it across a 3D spatial grid.

How RQD is Calculated: Instead of relying on sparse core samples, Strayos calculates the Rock Quality Designation (RQD) continuously throughout the block. By determining the distance between the intersecting joint sets mapped on your face, the software calculates the Volumetric Joint Count (Jv)—which is the number of joints per cubic meter. Strayos then automatically applies the Palmström (1982) formula to determine the RQD for every point in the grid:


The result is a continuous 3D RMR block model that reveals how rock-mass quality varies everywhere throughout your bench.

5. Step-by-Step Guide

Step 1: Open the Rock Mass AI Module

Navigate to your project dashboard and open the Rock Mass AI module. Ensure that your discontinuity sets have been accurately detected.

Step 2: Navigate to the RMR Tab

Inside the Rock Mass AI module, locate and click on the RMR tab to open the setup menu.



Step 3: Define Your RMR Parameters

To generate an accurate 3D block, the system needs baseline data for the RMR components:

  • Strength & Groundwater: Input your site's known Uniaxial Compressive Strength (UCS) and the current groundwater conditions. 


6. Analysing & Reporting

Step 1- Navigate through the parameters as represented below:

The values displayed do not represent the actual value of the parameter, it shows the rating/weight of each one (see table at point 2). So, for the example of the RMR Strayos will generate a continuous 3D point cloud of the data. The visualizer color-codes the rock mass based on the final 0–100 score, categorized into the five standard Bieniawski RMR classes:

    • Class I (81–100): Very Good Rock (Massive, highly competent rock requiring maximum explosive energy).

    • Class II (61–80): Good Rock

    • Class III (41–60): Fair Rock

    • Class IV (21–40): Poor Rock (High risk of overbreak and vibration damage; consider lower powder factors or decking).

    • Class V (0–20): Very Poor Rock (Heavily fractured, weathered, or unstable ground).

Step 2: To include specific visualizations to the report, use the option screenshot as the image below: 



For reference, below are the visualization of all the parameters on the report:








Step 3: Because surface points can obstruct your view of the rock deep inside the bench, use this specialized tool to interact with your data:

  • The RMR Slicing Tool: Use the slicing feature to create a plane that cuts directly through the 3D point cloud. This allows you to view the exact RMR rating at any specific depth or drill hole location.


Step 4: Generate a Rock Mass Rating report— edit the details, include the slices and screenshots you want, and export as PDF for sharing. It will generate for all of the sctructures on the dataset (user can delete the pages that do not want to use).


The final output of your analysis is the RMR Statistics dashboard. As seen below, this panel provides a concise breakdown of your results. You can quickly review the active joint sets, the Volumetric Joint Count (Jv), the derived RQD, and the individual parameter ratings that culminate in your final overall Rock Mass Rating (RMR). 


    • Related Articles

    • Rock Mass AI

      Rock Mass AI is a Strayos Site Analytics module that automatically detects, maps and analyses geological discontinuities directly on a 3D terrain model. From a single reconstructed model of a bench or outcrop, it identifies the crack and joint ...

    • Rock Joint and Seam Annotation Tool

      Aside from Strayos using AI to detect rock mass features, there is also a manual annotation tool that can be used to mark such features on a highwall. Our Rock Joint and Seam annotation tool is found on the annotation tool bar. When selected, you ...

    • Fragmentation AI: Customizing Particle Size Ranges and Editing Individual Rock Nodes

      When using the fragmentation AI within Strayos, users have the option to manually adjust and categorize the rock sizing and muck pile boundaries as needed to ensure the accuracy of data being reported. To begin, select the post blast dataset you are ...

    • Understanding Slope Stability AI

      Strayos' Slope Stability AI is a tool that allows users to automatically obtain a Safety Factor rating for their site's slope analysis. Strayos utilizes the 3D model in combination with Rock Mass data, and specific material properties to calculate ...

    • Slope Stability AI

      Strayos’ Slope Stability AI is a site analytics module that automatically calculates and visualizes a Safety Factor (SF) rating for your site’s slopes directly on a 3D terrain model. By combining your 3D model, Digital Elevation Model (DEM), and ...