Unconfined compressive strength prediction of recycled cement-treated base mixes using soft computing techniques

The study evaluates the viability of using Full-depth reclamation (FDR) as an eco-friendly approach for constructing roads. The research employs chemical stabilizers in reclaimed asphalt pavement (RAP) material to create a cement-treated base (CTB) layer. The study uses artificial neural network (ANN) models to predict the 7-days unconfined compressive strength (UCS) of RAP material-based CTB mixes. The Levenberg-Marquardt backpropagation-based ANN (LM-BP-ANN) and Scaled Conjugate Gradient backpropagation-based ANN (SCG-BP-ANN) models are used to forecast the UCS values. The models are assessed based on regression coefficient (R) and mean squared error (MSE), and the LM-BP-ANN model outperforms the SCG-BP-ANN model with an R value of 0.99556 and MSE of 0.0305. The findings suggest that the proposed models have the potential to forecast UCS values of recycled cement-treated base mixes.

• Record URL:
  • https://doi.org/10.1080/14680629.2023.2199889
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/14680629
• Supplemental Notes:
  • © 2023 Informa UK Limited, trading as Taylor & Francis Group 2023. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Chhabra, Rishi Singh
  • Mahadeva, Rajesh
  • Ransinchung R.N., G. D.
• Publication Date: 2024-2

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: pp 423-437
• Serial:
  • Road Materials and Pavement Design
  • Volume: 25
  • Issue Number: 2
  • Publisher: Taylor & Francis
  • ISSN: 1468-0629
  • EISSN: 2164-7402
  • Serial URL: http://www.tandfonline.com/loi/trmp20

Subject/Index Terms

• TRT Terms: Cement treated soils; Compressive strength; Full-depth reclamation; Neural networks; Recycled materials; Soft computing
• Subject Areas: Data and Information Technology; Design; Highways; Materials; Pavements;

Filing Info

• Accession Number: 01906391
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 30 2024 9:25AM