Sensitivity analysis (SA) is a crucial component in geotechnical engineering, providing a quantitative framework to assess the influence of input parameters on predictive models. This paper explores the integration of high-performance cloud computing for SA within DAARWIN, a digital geotechnical platform designed for data-driven decision-making in construction and mining. We discuss how sensitivity analysis enhances model optimization, reduces overdesign risks, and improves parameter calibration through advanced computational techniques. Furthermore, we examine the role of real-time sensitivity analysis in uncertainty quantification, model validation, and predictive geotechnical performance assessments.
Geotechnical modeling is inherently uncertain due to natural soil variability, limited site investigation data, and the complexity of constitutive models. Sensitivity analysis provides a systematic approach to understanding the impact of input parameters on model outputs, thereby enabling engineers to refine their designs, prioritize data collection efforts, and enhance model reliability.
DAARWIN integrates high-performance cloud computing to conduct large-scale SA, significantly reducing computational time while providing robust parameter influence assessments. The implementation of Finite Element (FE) models within DAARWIN allows for thousands of simulations to be performed simultaneously, offering high-resolution insights into the stability and behavior of ground conditions.
Methodology
High-Performance Sensitivity Analysis in DAARWIN
DAARWIN’s SA framework employs a parallel cloud-based computational architecture capable of running hundreds of virtual machines concurrently. The system is fully compatible with PLAXIS, supporting a range of constitutive models and boundary conditions. Sensitivity indices are computed to identify the most influential parameters affecting model outputs, allowing engineers to make data-driven decisions in geotechnical design.
The key features of DAARWIN’s SA methodology include:
Multi-Parameter Influence Analysis: Simultaneous evaluation of multiple soil and structural parameters, identifying direct and interactive effects.
Advanced Indexing Techniques: Implementation of Sobol indices and Morris screening methods to quantify parameter impact.
Computational Efficiency: Parallel processing significantly reduces analysis time, enabling real-time design adjustments.
Visualization Tools: Graphical sensitivity plots facilitate rapid interpretation of results and model refinements.
Data Integration and Model Calibration
The sensitivity analysis module within DAARWIN is designed to work in synergy with real-time monitoring data, improving calibration accuracy for geotechnical models. By continuously adjusting parameters based on observed site conditions, DAARWIN enables a dynamic feedback loop that refines predictions and mitigates uncertainties.
Applications in Construction and Mining
Optimization of Geotechnical Designs
Traditional design methods often incorporate conservative safety factors, leading to overdesign and inefficient resource allocation. By leveraging SA, engineers can determine which parameters significantly affect stability, reducing unnecessary conservatism while ensuring compliance with safety standards.
Risk Mitigation in Tunneling Operations
Tunnel excavation processes, particularly those involving Tunnel Boring Machines (TBM), require real-time adjustments based on ground conditions. DAARWIN’s SA module aids in the prediction of critical factors such as thrust force, cutter wear, and penetration rate, allowing engineers to proactively adjust TBM operational strategies to enhance efficiency and mitigate risks.
Backanalysis and Model Validation
Sensitivity analysis is instrumental in real-time backanalysis, where observed deformations and performance data are used to refine soil parameters. DAARWIN integrates genetic algorithms to iteratively calibrate input variables, reducing discrepancies between theoretical models and actual ground behavior.
Conclusion and Future Outlook
Sensitivity analysis, powered by high-performance cloud computing, is revolutionizing geotechnical engineering by providing actionable insights into model behavior and parameter significance. The integration of DAARWIN’s SA module enables optimized design strategies, reduced uncertainty, and enhanced predictive capabilities in both construction and mining applications.
Future advancements in SA will likely incorporate machine learning-driven parameter selection, Bayesian inference methods for probabilistic analysis, and automated uncertainty quantification frameworks, further refining geotechnical decision-making.
