THE APPLICATION OF BOOSTING ALGORITHMS IN THE PREDICTION OF BOND STRENGTH BETWEEN THIN STEEL BARS AND CONCRETE

Resumo

The current study discusses the application of intelligent algorithms and machine learning techniques
to predict the bond strength between steel and concrete. The paper focuses on three boosting algorithms employed
for this prediction task. The research exploited a database derived from pull-out tests conducted on thin steel bars
to assess the bond between steel and concrete. The experimental program involved the use of three different classes
of concrete and two types of steel bars. The goal was to analyze the steel-concrete bond strength, which is
influenced by various factors. For the computational simulations, the input variables considered in this study were
the bar surface, bar diameter (φ), concrete compressive strength (fc), and anchorage length (Ld). The output was
the pull-out strength at the steel-concrete interface. It is important to highlight that most previous studies in this
field have mainly focused on bars with diameters greater than 10.0 mm, while there is limited research available
to evaluate the performance of bars with diameters smaller than 10.0 mm. The paper describes the computational
experiments conducted using different boosting algorithms: Adaptive Boosting (AdaBoost), Gradient Boosting
(GB), and Extreme Gradient Boosting (XGB). These machine learning-based models achieved highly accurate
predictions, applying specific hyperparameters. The following metrics were used to compare the performance of
the different methods: Root Mean Squared Error (RMSE), the coefficient of variation (CV), and the error. These
metrics were used to evaluate the reliability of each algorithm in predicting the bond strength in the samples. The
results indicate the accuracy and goodness of fit of the model's predictions. Based on them, it can be concluded
that the presented model can satisfactorily predict the bond strength of samples between thin steel bars and
concrete.