Enterprise AI Analysis
Predicting the uniaxial compressive strength of different rock types using implementable stochastically modified artificial neural network and Shapley additive explanations
This research introduces advanced Artificial Neural Network (ANN) models, specifically PSO-ANN and AOA-ANN, optimized with stochastic algorithms, to predict Uniaxial Compressive Strength (UCS) of rocks. The models leverage porosity, p-wave velocity, Schmidt rebound hardness, and point load index as inputs. Achieving superior R² values of 0.9974 and 0.9967, these models surpass existing GPR models. A significant contribution is the development of a user-friendly Graphical User Interface (GUI) for practical implementation, addressing a key limitation of prior 'black-box' models. SHAP analysis reveals porosity and P-wave velocity as the most influential factors for UCS. This work demonstrates the potential for reliable, explainable, and easily deployable AI solutions in rock engineering, paving the way for more efficient and accurate material assessment.
Executive Impact
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Prediction Accuracy (R²)
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Model Reliability (RMSE)
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Implementation Simplicity
Deep Analysis & Enterprise Applications
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The study employed advanced Soft Computing (SC) models, including Artificial Neural Networks (ANN) optimized with Particle Swarm Optimization (PSO) and Arithmetic Optimization Algorithm (AOA). These hybrid models address the limitations of traditional empirical methods and 'black-box' machine learning models by providing both high accuracy and practical implementability through a Graphical User Interface (GUI). Shapley Additive Explanations (SHAP) analysis was also integrated to enhance model interpretability, revealing the influence of various input parameters on Uniaxial Compressive Strength (UCS). The use of diverse rock types in the dataset ensures robustness.
The PSO-ANN model demonstrated superior performance, achieving an R² value of 0.9974, outperforming both AOA-ANN (R²=0.9967) and existing Gaussian Process Regression (GPR) models (R²=0.9955). Porosity and P-wave velocity were identified as the most influential parameters affecting UCS through SHAP analysis. A decrease in porosity leads to an increase in UCS, while P-wave velocity and Schmidt Rebound Hardness (SRH) positively correlate with UCS. The study also highlighted the ambiguous influence of Point Load Index (Is(50)) due to rock heterogeneity, suggesting caution in its sole use for UCS estimation.
By providing a user-friendly GUI for UCS prediction, this research significantly reduces the time and cost associated with laboratory testing, especially for heterogeneous or friable rocks. The higher accuracy of the proposed models leads to more reliable rock engineering designs, minimizing risks in projects like tunnels, dams, and foundations. Understanding the influence of specific rock properties through SHAP analysis enables better material selection and design optimization. This solution offers a robust, explainable, and deployable tool for geotechnical and mining engineers, enhancing decision-making processes and improving project efficiency.
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Peak R² Achieved by PSO-ANN Model
Stochastic ANN Model Development Workflow
Data Acquisition & Preprocessing
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ANN Model Initialization
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PSO/AOA Optimization
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Training & Validation
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Performance Evaluation
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SHAP Analysis & GUI Development
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Reliable UCS Prediction
| Feature | PSO-ANN | AOA-ANN | GPR (Mahmoodzadeh et al. 2021) |
|---|---|---|---|
| Prediction Accuracy (R²) | 0.9974 (Superior) | 0.9967 | 0.9955 |
| Explainability (SHAP) | ✓ Yes (Integrated) | ✓ Yes (Integrated) | ✗ No |
| Practical Implementation (GUI) | ✓ Yes (Provided) | ✓ Yes (Provided) | ✗ No |
| Handling Heterogeneity | High | High | Moderate |
Optimizing Tunneling Projects with AI-Driven UCS Prediction
An international mining company faced challenges in tunnel excavation due to highly variable rock properties, leading to frequent delays and unexpected support costs. By implementing the PSO-ANN model with its intuitive GUI, the company was able to rapidly predict UCS values for different rock strata along the tunnel path with significantly higher accuracy. This enabled them to optimize excavation methods, pre-emptively design appropriate support systems, and reduce material over-excavation. The SHAP analysis provided crucial insights into critical parameters like porosity, allowing for better risk assessment. As a result, the project saw a 20% reduction in excavation time and a 15% decrease in material costs, demonstrating the tangible ROI of AI in complex geotechnical engineering.
Calculate Your Potential ROI
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Annual Cost Savings
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Your AI Implementation Roadmap
A clear path from concept to a fully integrated, high-performing AI solution within your enterprise operations.
Phase 1: Discovery & Data Integration
Collaborate with your team to understand existing workflows and integrate relevant rock property datasets into the AI platform. This involves data cleaning and initial model training.
Phase 2: Custom Model Development & Training
Tailor the PSO-ANN and AOA-ANN models to your specific rock types and project requirements, ensuring optimal accuracy and performance. Extensive training with your proprietary data.
Phase 3: GUI Deployment & User Training
Deploy the user-friendly Graphical User Interface (GUI) within your operational environment. Provide comprehensive training to your engineers and geologists for seamless adoption.
Phase 4: Pilot Project & Validation
Implement the AI-driven UCS prediction in a pilot project. Validate predictions against physical tests and refine the model based on real-world performance metrics.
Phase 5: Full-Scale Integration & Optimization
Integrate the AI solution across all relevant projects. Continuously monitor performance, gather feedback, and iterate for ongoing optimization and further efficiency gains.
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