BaNTERA: A Bayesian Network for Third-Party Excavation Risk Assessment

Palazuelos, Andrés Ruiz-Tagle; Lewis, Austin D.; Schell, Colin A.; Lever, Ernest; Groth, Katrina M.

doi:10.1016/j.ress.2022.108507

Cited by 14 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, before considering the use of the developed FEA model simulation to enhance test design or evaluation, it is necessary to comprehend the degree to which a simulation model approximates the actual system and the domains in which it is more restricted with respect to the intended use. Several activities are described in the previous studies [ 11 , 12 ] regarding model validation; these activities can be broadly categorized as external validation and sensitivity analysis. The process of comparing the output of a model with observations from the system that is being modeled is an example of external validation.…”

Section: Introductionmentioning

confidence: 99%

Mitigating Measurement Inaccuracies in Digital Twins of Construction Machinery through Multi-Objective Optimization

Abebe,

Cho,

Han

et al. 2024

Sensors

View full text Add to dashboard Cite

The advent of digital twins facilitates the generation of high-fidelity replicas of actual systems or assets, thereby enhancing the design’s performance and feasibility. When developing digital twins, precise measurement data is essential to ensure alignment between the actual and digital models. However, inherent uncertainties in sensors and models lead to disparities between observed and predicted (simulated) behaviors. To mitigate these uncertainties, this study originally proposes a multi-objective optimization strategy utilizing a Gaussian process regression surrogate model, which integrates various uncertain parameters, such as load angle, bucket cylinder stroke, arm cylinder stroke, and boom cylinder stroke. This optimization employs a genetic algorithm to indicate the Pareto frontiers regarding the pressure exerted on the boom, arm, and bucket cylinders. Subsequently, TOPSIS is applied to ascertain the optimal candidate among the identified Pareto optima. The findings reveal a substantial congruence between the experimental and numerical outcomes of the devised virtual model, in conjunction with the TOPSIS-derived optimal parameter configuration.

show abstract

Section: Introductionmentioning

confidence: 99%