首页|New Findings from Natural Resources Canada Describe Advances in Machine Learning (Machine Learning Modelling for Predicting Tensile Strain Capacity of Pipelines and Identifying Key Factors)
New Findings from Natural Resources Canada Describe Advances in Machine Learning (Machine Learning Modelling for Predicting Tensile Strain Capacity of Pipelines and Identifying Key Factors)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news reporting from the Natur al Resources Canada by NewsRx journalists, research stated, “Machine learning (M L) techniques have recently gained great attention across a multitude of enginee ring domains, including pipeline materials. However, their application to tensil e strain capacity (TSC) modelling remains unexplored.” Our news reporters obtained a quote from the research from Natural Resources Can ada: “To bridge this gap, this study developed and evaluated an ML model to pred ict the tensile strain capacity of girthwelded pipelines. The model was trained on over 20,000 data points derived from a TSC equation available in the literat ure. The ML model demonstrated robust performance in predicting tensile strain c apacities. Evidence of this lies in the near-zero means, minimal standard deviat ions, and normal distribution of residuals for both the training and test datase ts. These collectively suggest that the model provides a good fit for the data. Furthermore, the model?s loss behavior indicates successful convergence and gene ralization, without signs of overfitting or underfitting. An analysis using the random forest method revealed that the geometry of the flaw, specifically the fl aw depth, is the most influential variable in predicting the TSC. This could be attributed to its significant impact on the fracture toughness of materials. In contrast, material properties and fracture toughness exert less influence relati vely, despite their contributions to the model.”
Natural Resources CanadaCyborgsEmerg ing TechnologiesMachine Learning
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