Two-stage machine learning framework for developing probabilistic strength prediction models of structural components: An application for RHS-CHS T-joint. (1st September 2022) - Hu, Shuling; Wang, Wei; Lin, Xiaogang
Graphical abstract: Highlights: An innovative two-stage machine learning framework (TMLF) was proposed. The uncertainties of material and geometrics were considered. The strengths of the RHS-CHS T-joint follow the gamma distribution. ANN model shows the highest accuracy in predicting the RHS-CHS T-joints' strength. The TMLF can accurately predict the distribution of structural component strength. Abstract: Machine learning technics have been extensively used in the strength prediction of structural components in recent years, nevertheless, these established strength prediction models usually can not address the inherent uncertainties introduced by the material and geometrics of structural components. This paper intends to propose an innovative two-stage machine learning framework for developing probabilistic strength prediction models of structural components with the consideration of uncertainties of material and geometrics based on limited test results. Rectangular hollow section to circular hollow section (RHS-CHS) T-joints are used as an example for evaluating the proposed framework. To this end, 58, 744 training and 19, 870 validation datasets are first generated through numerical simulation. Three machine learning algorithms are used and evaluated in this paper to develop the best model for predicting the strength of the RHS-CHS T-joints. The analysis results show that the artificial neural networks (ANN) showed the best generalization performance. The combination of the uncertainties of material and geometrics is considered through the Latin hypercube sampling method. Then, a new strength database considering the randomness of the structural parameters (e.g., material and geometrics) are developed through the trained ANN model and the probabilistic distribution of the strength of the RHS-CHS T-joints is analyzed through the Anderson-Darling test method. The analysis results indicate that the random strengths of the RHS-CHS T-joints follow a gamma distribution while the material and geometrics follow the normal distribution. Finally, the machine learning models for probabilistic strength prediction of the RHS-CHS T-joints are developed and validated. The analysis results indicate that the developed machine learning model can accurately capture the distribution of the strength of RHS-CHS T-joints, confirming the efficiency of the proposed two-stage machine learning framework for developing probabilistic strength prediction models of structural components. A software named "Probabilistic strength prediction of RHS-CHS T-joint" is proposed based on the developed probabilistic strength prediction model for practical application.