Automated machine learning recognition to diagnose flood resilience of railway switches and crossings

HIGHLIGHTS

• who: Jessada Sresakoolchai from the DepartmentUniversity of have published the research: Automated machine learning recognition to diagnose flood resilience of railway switches and crossings, in the Journal: Scientific Reports Scientific Reports
• what: As mentioned, this study explores the potential of the use of features in three different formats, two time-series data of ABAs, displacement of crossing noses, and the combination of both. Data used to train the model is numerical data obtained from LS-DYNA as FEM model simulations. The contribution of the study is the developed machine_learning model can be used to detect . . .
  

   

  

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