Prediction of stress intensity factors in pavement cracking with neural networks based on semi-analytical FEA
Computation of the stress intensity factors (SIFs) at the crack tip is the basis for pavement crack propagation analysis. Due to the three-dimensional (3-D) nature of cracked pavements and traffic loading, two-dimensional (2-D) finite element analysis (FEA) may be too simple to precisely predict SIFs, and the best choice for calculating the SIFs seems to be 3-D FEA programs. However, the 3-D FEA solutions are often computationally heavy. We had previously developed a semi-analytical FEA with multi-variable regression approach to fill this gap, but its accuracy still needs to be improved. To address this problem, a neural network approach based on semi-analytical FEA is presented in this paper: firstly, a SIFs database was generated through analyzing varieties of pavement structures using elastic semi-analytical FEA program; secondly, from the results in the database, neural network (NN) based SIF equations were developed for practical applications. The determination coefficients (R2) of all the developed NN models were greater than 0.99 and mean square error (MSE) values were less than 1e?4. The comparisons between the prediction results from NN models and multivariable regression models also showed the advantage of NN over multivariable regression on the prediction accuracy. This proposed NN SA-FEA SIF prediction approach has been developed as a pavement crack propagation analysis tool.
prediction stress intensity factor pavement cracking neural networks semi-analytical fea