Practical machine learning-based prediction model for axial capacity of square CFST columns
Tien-Thinh Le
Abstract
In this paper, a surrogate Machine-Learning (ML) model based on Gaussian Process Regression (GPR) was developed to predict the axial load of square concrete-filled steel tubular (CFST) columns under compression. For this purpose, an experimental database was extracted from the available literature and used for the development and training of the GPR model. The GPR model’s performance is superior to that of existing models in relation to the axial load of square CFST columns. For practical application, a Graphical User Interface (GUI) was developed for researchers, engineers to support the teaching and interpretation of the axial behavior of CFST columns.
Topics & Concepts
KrigingSupport vector machineGround-penetrating radarStructural engineeringEngineeringSquare (algebra)Relation (database)Process (computing)Computer scienceMachine learningArtificial intelligenceData miningMathematicsRadarGeometryTelecommunicationsOperating systemStructural Load-Bearing AnalysisStructural Engineering and Vibration AnalysisStructural Behavior of Reinforced Concrete