##plugins.themes.bootstrap3.article.main##

Tran-Hieu Nguyen https://orcid.org/0000-0002-1446-5859 Anh-Tuan Vu

Abstract

In this paper, a machine learning-based framework is developed to quickly evaluate the structural safety of trusses. Three numerical examples of a 10-bar truss, a 25-bar truss, and a 47-bar truss are used to illustrate the proposed framework. Firstly, several truss cases with different cross-sectional areas are generated by employing the Latin Hypercube Sampling method. Stresses inside truss members as well as displacements of nodes are determined through finite element analyses and obtained values are compared with design constraints. According to the constraint verification, the safety state is assigned as safe or unsafe. Members’ sectional areas and the safety state are stored as the inputs and outputs of the training dataset, respectively. Three popular machine learning classifiers including Support Vector Machine, Deep Neural Network, and Adaptive Boosting are used for evaluating the safety of structures. The comparison is conducted based on two metrics: the accuracy and the area under the ROC curve. For the two first examples, three classifiers get more than 90% of accuracy. For the 47-bar truss, the accuracies of the Support Vector Machine model and the Deep Neural Network model are lower than 70% but the Adaptive Boosting model still retains the high accuracy of approximately 98%. In terms of the area under the ROC curve, the comparative results are similar. Overall, the Adaptive Boosting model outperforms the remaining models. In addition, an investigation is carried out to show the influence of the parameters on the performance of the Adaptive Boosting model.

Comments

  1. Latest Oldest Top Comments

    ##plugins.themes.bootstrap3.article.details##

    Section
    SI: Steels and Composites for Engineering Structures

    How to Cite

    Nguyen, T.-H. and Vu, A.-T. (2021) “Evaluating structural safety of trusses using Machine Learning”, Frattura ed Integrità Strutturale, 15(58), pp. 308–318. doi: 10.3221/IGF-ESIS.58.23.