Use of Artificial Neural Network to Determine the Pavement Layer Properties Based on Automated Plate Load Test-Reference-Cited by-同舟云学术

Use of Artificial Neural Network to Determine the Pavement Layer Properties Based on Automated Plate Load Test

Published:2024-02-22 Issue: Volume:1709 Page:307-316
ISSN:
Container-title:Geo-Congress 2024
language:
Short-container-title:

Author:

Khan Md. Ashrafuzzaman¹²²³²,Ramineni Krishneswar¹²²³²,Deshmukh Aditya¹²²³²,Banerjee Aritra¹²²³²,Puppala Anand J.¹²²³²

Affiliation:

1. Corsair Consulting LLC, Arlington, TX.

2. Zachry Dept. of Civil and Environmental Engineering, Texas A&M Univ., College Station, TX.

3. Dept. of Civil and Environmental Engineering, South Dakota State Univ., Brookings, SD.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784485354.030

Reference9 articles.

1. Field Evaluation of Geocell Use in Flexible Pavements;Al-Qadi I. L.;Transportation Research Record: Journal of the Transportation Research Board,2000

2. Beltrán, G., and M. Romo. 2014. Assessing artificial neural network performance in estimating the layer properties of pavements Evaluación del desempeño de redes neuronales artificiales para estimar propiedades de capas de pavimentos Introduction 1 2.

3. Field Performance of Geocell Reinforced Recycled Asphalt Pavement Base Layer

4. Khan M. A. N. Biswas A. Banerjee and A. J. Puppala. 2023. “Effects of Traffic Loading Magnitude and Frequency on the Performance of Geocell-Reinforced Flexible Pavements.” Geo-Congress 2023 517–525. Reston VA: American Society of Civil Engineers. https://doi.org/10.1061/9780784484685.052.

5. Using Artificial Neural Networks as a Forward Approach to Backcalculation;Meier R. W.;Transportation Research Record: Journal of the Transportation Research Board,1997