Voids prediction beneath cement concrete slabs using a FEM-ANN method

ABSTRACT

The voids beneath cement concrete slabs are a major invisible disease, resulting in a rapid decrease in service performance in the composite pavement. Accurate voids prediction is essential for the extensive application and long-term service of composite pavement. This research provides a FEM-ANN (Finite Element Modelling-Artificial Neural Network) method to predict the voids beneath concrete slabs. These ANN models include the original back propagation (BP), the particle swarm optimisation (PSO) BP model, the genetic algorithm (GA) BP model, and the whale optimisation algorithm (WOA) BP model. The voids FEM model is established and validated by the measured data in the field, and the relative error of measured and simulated results is within 4%. The cross-validation results show that the WOA-BP model has the best prediction performance, with the highest score of 8, which refers to the overall score of the mean value and variance of these evaluation indices. Therefore, this FEM-ANN framework is an efficient method for estimating the voids beneath concrete slabs. Furthermore, it is discovered that the base modulus with the highest contribution degree of 20.34% is the most dominant factor in predicting the voids output.

HIGHLIGHTS

• A FEM-ANN method is utilised to predict the voids beneath concrete slabs

• The WOA-BP model exhibits the best comprehensive performance of the four ANN models.

• ${\mathrm{W}}_d$ and pavement mechanical responses have a positive effect on ${\mathrm{A}}_v$ opposite to ${\mathrm{K}}_d$ and pavement structure.

• The base modulus is the primary factor in predicting the voids output.

KEYWORDS:

• Voids prediction
• cement concrete slabs
• finite element modelling
• optimised algorithm
• comprehensive evaluation
• sensitivity analysis

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All data, models, and code generated or used during the study appear in the submitted article.

Additional information

Funding

This work was supported by National Natural Science Foundation of China: [grant no 51978163, 52208439]; Jiangsu Province Natural Science Foundation [grant number BK20200468].