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Last updated on 2024年9月24日

How do you assess and mitigate the risks and uncertainties in rock mechanics software modeling?

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Rock mechanics software modeling is a powerful tool for geotechnical engineering, but it also involves many sources of uncertainty and risk. How can you ensure that your models are reliable, accurate, and robust enough to support your design and decision making? In this article, you will learn some practical tips and best practices to assess and mitigate the risks and uncertainties in rock mechanics software modeling.

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Dr. Sanjay Rana

Professional Geophysicist | Geophysics Trainer | Founder & Managing Director, Parsan Overseas (P) Limited | Chairman…
Ahmad Zarei

Senior Geotechnical Engineer | CP Eng | Rock Mechanics | Geotechnical Solutions | Project Management

1 Identify the sources of uncertainty

The first step is to identify the sources of uncertainty that affect your model. These can be classified into three types: input uncertainty, model uncertainty, and output uncertainty. Input uncertainty refers to the variability and incompleteness of the data and parameters that you use to define your model, such as rock properties, boundary conditions, loading scenarios, and initial conditions. Model uncertainty refers to the simplifications and assumptions that you make to represent the complex behavior of the rock mass, such as the choice of constitutive models, numerical methods, meshing, and calibration. Output uncertainty refers to the sensitivity and variability of the results that you obtain from your model, such as stress, strain, displacement, and failure modes.

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Dr. Sanjay Rana

Professional Geophysicist | Geophysics Trainer | Founder & Managing Director, Parsan Overseas (P) Limited | Chairman, Aqua Foundation
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start by identifying the uncertainties, whether it's data, rock properties, or assumptions....use sensitivity or probabilistic analysis to see how these affect results. To reduce risks, gather better data, validate your model with real-world data, and refine assumptions. Make sure to communicate any uncertainties clearly and update the model as new data comes in to improve accuracy.

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2 Quantify the uncertainty

The second step is to quantify the uncertainty that you have identified. This can be done using various methods, such as statistical analysis, sensitivity analysis, probabilistic analysis, or reliability analysis. Statistical analysis involves using descriptive and inferential statistics to summarize and interpret the data and parameters that you use for your model. Sensitivity analysis involves testing how the output of your model changes with respect to changes in the input or model parameters. Probabilistic analysis involves using probability distributions and random sampling to account for the variability and randomness of the input or model parameters. Reliability analysis involves using probability theory and failure criteria to estimate the probability of failure or exceedance of a limit state for your model.

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3 Mitigate the uncertainty

The third step is to mitigate the uncertainty that you have quantified. This can be done using various strategies, such as data collection, model validation, model refinement, or design optimization. Data collection involves acquiring more or better data and parameters to reduce the input uncertainty and improve the confidence in your model. Model validation involves comparing your model results with experimental or field observations to check the accuracy and adequacy of your model. Model refinement involves adjusting or improving your model assumptions and parameters to reduce the model uncertainty and enhance the robustness of your model. Design optimization involves finding the optimal solution or trade-off between performance and safety for your model.

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Ahmad Zarei

Senior Geotechnical Engineer | CP Eng | Rock Mechanics | Geotechnical Solutions | Project Management
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Geological uncertainty, categorized into macro and micro-scale, depends on the scale of uncertain parameters. Macro-scale uncertainty involves field/reservoir scale parameters like reservoir compartmentalization, water/oil and gas/oil contact depths, existence of aquifers, etc. This uncertainty, represented via a range or probability distribution, is easier to account for in field development than micro-scale uncertainty. Mitigation of uncertainty uses quantified uncertainty to guide decision-making. This could include design adjustments, additional safety measures, or further investigations to reduce uncertainty. For instance, if soil material properties have high uncertainty, additional soil testing could be conducted.

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4 Communicate the uncertainty

The fourth step is to communicate the uncertainty that you have mitigated. This can be done using various tools, such as graphs, tables, maps, reports, or presentations. Graphs can be used to visualize the distribution, sensitivity, or reliability of your model results. Tables can be used to summarize the statistics, parameters, or probabilities of your model results. Maps can be used to spatially display the variability or risk of your model results. Reports can be used to document the methods, assumptions, and limitations of your model. Presentations can be used to explain and justify your model results to your clients, stakeholders, or peers.

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5 Manage the uncertainty

The fifth step is to manage the uncertainty that you have communicated. This can be done using various techniques, such as monitoring, updating, or revising your model. Monitoring involves collecting and analyzing new data and information to track the performance and behavior of your model. Updating involves incorporating new data and information into your model to improve its accuracy and reliability. Revising involves modifying or changing your model to adapt to new conditions or requirements.

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6 Learn from the uncertainty

The sixth and final step is to learn from the uncertainty that you have managed. This can be done using various approaches, such as feedback, review, or improvement of your model. Feedback involves seeking and receiving constructive criticism and suggestions from your clients, stakeholders, or peers to evaluate your model. Review involves assessing and reflecting on your model performance and outcomes to identify its strengths and weaknesses. Improvement involves applying the lessons learned from your model to enhance your skills and knowledge in rock mechanics software modeling.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Ahmad Zarei

Senior Geotechnical Engineer | CP Eng | Rock Mechanics | Geotechnical Solutions | Project Management
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Rock mechanics software modelling involves risk identification and mitigation using complex techniques: - Risk-Based Design: Utilizing probabilistic methods for stress modelling. - Uncertainty Assessment: Implementing techniques such as FOSM for explicit uncertainty evaluation. - 3D Modeling: Employing 3D rockfall runout modelling for comprehensive hazard assessment and countermeasure optimization. - Expert Consultation: Engaging with subject matter experts to address geotechnical uncertainty. Continuous site monitoring and model updates are vital. Each project presents unique challenges, necessitating bespoke risk assessment and mitigation strategies. I would highly recommend that you collaborate with rock mechanics professionals.

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How do you assess and mitigate the risks and uncertainties in rock mechanics software modeling?

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1 Identify the sources of uncertainty

2 Quantify the uncertainty

3 Mitigate the uncertainty

4 Communicate the uncertainty

5 Manage the uncertainty

6 Learn from the uncertainty

7 Here’s what else to consider

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