A Sampling-Based Sensitivity Analysis Method Considering the Uncertainties of Input Variables and Their Distribution Parameters

This article studies the role of model uncertainties in sensitivity and probability analysis of reliability. The measure of reliability is failure probability. The failure probability is analysed using the Bernoulli distribution with binary outcomes of success (0) and failure (1). Deeper connections between Shannon entropy and variance are explored. Model uncertainties increase the heterogeneity in the data 0 and 1. The article proposes a new methodology for quantifying model uncertainties based on the equality of variance and entropy. This methodology is briefly called “variance = entropy”. It is useful for stochastic computational models without additional information. The “variance = entropy” rule estimates the “safe” failure probability with the added effect of model uncertainties without adding random variables to the computational model. Case studies are presented with seven variants of model uncertainties that can increase the variance to the entropy value. Although model uncertainties are justified in the assessment of reliability, they can distort the results of the global sensitivity analysis of the basic input variables. The solution to this problem is a global sensitivity analysis of failure probability without added model uncertainties. This paper shows that Shannon entropy is a good sensitivity measure that is useful for quantifying model uncertainties.

Section: Reliability-oriented Sensitivity Measuresmentioning

confidence: 99%

Quantification of Model Uncertainty Based on Variance and Entropy of Bernoulli Distribution

Kala

2022

“…In the realm of engineering applications, Nguyen et al (2020) applied Sobol's GSA to assess the in-plane elastic buckling of steel arches, considering random input parameters [24]. A sampling-based sensitivity analysis method for engineering products with uncertain input variables, reducing computational complexity and increasing efficiency, is discussed in [25]. This method enables assessing input factors impact on model responses without predefined probability distributions, enhancing sensitivity analysis applicability in complex engineering scenarios.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis, Synthesis and Gait Classification of Reconfigurable Klann Legged Mechanism

Hayat,

Megalingam,

Kumar

et al. 2024

Legged locomotion is essential for navigating challenging terrains where conventional robotic systems encounter difficulties. This study investigates the sensitivity of the reconfigurable Klann legged mechanism (KLM) to variations in the input geometric parameters, such as joint position location, link lengths, and angles between linkages, on the continuous coupler curve, which represents the output trace of the leg movement.The continuous coupler curve’s sensitivity is explored using global sensitivity analysis based on Sobol’s sensitivity method. Furthermore, a novel reconfigurability strategy is presented for the Klann mechanism, aiming to reduce the number of required actuators and the complexity in control. In simulation, the coupler curves obtained from the reconfigurable KLM are classified as hammering, digging, jam avoidance, and step climbing using machine learning approaches. Experimental validation is presented, discussing an approach to identifying geometric parameters and the resultant coupler curve. Illustrations of the the complete assembly of the reconfigured KLM with the obtained gaits using limited experiments are also highlighted.

“…Hence, slope stability is often determined by a combination of internal and external factors. However, the degree of influence of these factors on the slope is complex, ambiguous, and variable [13][14][15][16]. Sensitivity analysis of slope stability factors involves quantitatively analyzing the correlation between the factors that affect slope stability and slope safety coefficient, studying the influence of changes in each factor on slope safety coefficient, identifying the dominant factor of slope instability, and providing targeted guidance for the prevention and control of slope instability disasters.…”

Section: Introductionmentioning

confidence: 99%

Global Sensitivity Analysis Method for Embankment Dam Slope Stability Considering Seepage–Stress Coupling under Changing Reservoir Water Levels

Zhou

Shen

et al. 2023

Ensuring the long-term, efficient, and safe operation of reservoir dams relies on the slope stability of embankment dams. Periodic fluctuations of the reservoir water level due to reservoir scheduling operations make the slope of the reservoir bank vulnerable to instability. To investigate the influence of various factors and their interactions with embankment dam slope stability under changing reservoir water levels, a global sensitivity analysis method is proposed that accounts for seepage–stress coupling. An embankment dam in Shaanxi Province, China, is studied as an example, with COMSOL Multiphysics software simulating the seepage and slope stability of the dam under fluctuating reservoir water level conditions and seepage–stress coupling. The global sensitivity analysis of factors affecting dam slope stability is accomplished by combining Plackett–Burman and Box–Behnken experimental designs, with ANOVA determining the sensitivity of each factor and interaction term. The results demonstrate that during the impoundment period of the reservoir, the saturation line is concave, and the overall stability safety of the dam slope increases first and then tends to be stable, according to the coefficient. The internal friction angle φ, cohesion c, and soil density ρs represent the three most sensitive factors affecting the stability and safety of the dam slope, while c × ρs is a second-order interaction term with significant sensitivity to the stability and safety coefficient of the dam slope. The reservoir drainage period infiltration line is convex, and dam slope stability first reduced and then increased. The magnitude of water level change H, internal friction angle φ, cohesion c, and soil density ρs are the four most sensitive factors for the coefficient of safety of dam slope stability, while c × ρs, H × ρs, and φ × ρs are the second-order interaction terms with significant sensitivity to the coefficient of safety of dam slope stability. These research findings and methods can offer valuable technical support and reference for the investigation and evaluation of the stability of embankment dam slopes.