# The use of intelligent algorithms in the prediction of bonding strength in steel-concrete interfaces

## Palavras-chave:

Intelligent Systems, Multiple Linear Regression, Machine Learning, Pull-out test, Support Vector Machines## Resumo

The current study proposes the use of intelligent systems and a statistical technique to predict the

strength of steel-concrete bond using a database of academic literature. The work used as the reference deals with

the pull-out test that evaluated the steel-concrete bond behavior in thin bars. The experimental program employed

concretes of class C25, C35 and C40, and CA-50 ribbed bars (with diameters of 6.3, 8.0, and 10 mm) and CA-60

notched bars (with diameters of 5.0, 6.0, 8.0, and 9.5 mm). The database was subjected to data mining strategies

for statistical treatment. Conventionally, bond strength is obtained by pull-out and beam tests, as proposed by BS

EN:10080, involving expensive and lengthy experimental tests. Alternative ways are the application of machine

learning-based methods and the use of statistical techniques. Using these methods it is possible to assess their

efficiency in predicting the maximum pull-out force. In the present research two particular methods are used to

solve the problem. One technique is Multiple Linear Regression which is a generalization of Least Squares, where

the minimization of the sums of the n-th powers of the residuals is considered. Multiple regression analysis is also

very useful in experimental situations, where the experimenter can control the predictor variables. The other model

is based on statistical learning theory and is called Support Vector Machines (SVM). It is a machine learning and

computational intelligence technique, where it is possible to obtain a classification of data from the same domain

in which the learning is performed. The method uses a principle called induction, where it is possible to draw

generic conclusions from a training set. The main objective of this work is to propose an alternative way to predict

the bond strength of thin bars using computational methods and a statistical technique. The methods used is

compared via performance metrics to verify which one proves to be more reliable to predict steel-concrete interface

bond, considering the safety coefficients used in engineering.