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

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.HIGHLIGHTSA FEM-ANN method is utilised to predict the voids beneath concrete slabsThe WOA-BP model exhibits the best comprehensive performance of the four ANN models.${\rm W}_d$Wd and pavement mechanical responses have a positive effect on ${\rm A}_v$Av opposite to ${\rm K}_d$Kd and pavement structure.The base modulus is the primary factor in predicting the voids output.

• Record URL:
  • https://doi.org/10.1080/10298436.2023.2191198
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/44544515
• Supplemental Notes:
  • © 2023 Informa UK Limited, trading as Taylor & Francis Group 2023. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Shi, Bin
  • Wang, Xiang
  • Dong, Qiao
  • Chen, Xueqin
  • Gu, Xingyu
  • Yang, Bohan
  • Yan, Shiao
  • Wang, Sike
• Publication Date: 2023

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 2191198
• Serial:
  • International Journal of Pavement Engineering
  • Volume: 24
  • Issue Number: 1
  • Publisher: Taylor & Francis
  • ISSN: 1029-8436
  • Serial URL: http://www.tandf.co.uk/journals/titles/10298436.html

Subject/Index Terms

• TRT Terms: Air voids; Algorithms; Backpropagation; Composite pavements; Concrete; Finite element method; Mathematical prediction; Neural networks; Slabs
• Subject Areas: Highways; Pavements;

Filing Info

• Accession Number: 01906751
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 2 2024 4:14PM