A new method of concrete strength evaluation

The accuracy of concrete strength evaluation has a great influence on the safety assessment of the structure. In the study,adaptive neuro-fuzzy inference system (ANFIS) based on Takagi-Sugeno rules is built up to evaluate concrete strength. According to the expert experience, the relationship between the rebound value and concrete strength tends to power function. So we adopt common logarithms of rebound value and strength value as the inputs and outputs of the ANFIS. System parameter sets are iteratively adjusted according to input and output data samples by a hybrid-learning algorithm. In the system, condition parameter sets can be determined by the back propagation gradient descent method and conclusion parameter sets can be determined by the least squares method. Then, the concrete strength can be inferred by the fuzzy inference. The method takes full advantage of the characteristics of the abilities of Fuzzy Neural Networks (FNN) including automatic learning, generation and fuzzy logic inference. The experiment shows that the average relative error of the predicted results is 10.316% and relative standard error is 12.895% over all the 508 samples,which are satisfied with the requirements of practical engineering. The method efficiently maps the complex non-linear relationship between the concrete strength values and the rebound values, and provides a new way for the concrete strength evaluation.