Implementation of deep neural networks and statistical methods to predict the resilient modulus of soils-Reference-Cited by-同舟云学术

Implementation of deep neural networks and statistical methods to predict the resilient modulus of soils

Published:2023-09-20 Issue:1 Volume:24 Page:
ISSN:1029-8436
Container-title:International Journal of Pavement Engineering
language:en
Short-container-title:International Journal of Pavement Engineering

Author:

Polo-Mendoza Rodrigo¹,Duque Jose¹²^ORCID,Mašín David¹^ORCID,Turbay Emilio³⁴,Acosta Carlos²⁵

Affiliation:

1. Faculty of Science, Charles University, Prague, Czech Republic

2. Department of Civil & Environmental Engineering, Universidad de la Costa, Barranquilla, Colombia

3. Department of Civil & Environmental Engineering, Universidad del Norte, Barranquilla, Colombia

4. Department of Civil, Construction and Environmental Engineering, North Carolina State University, Raleigh, NC, USA

5. Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin, Austin, TX, USA

Funder

Center for Geosphere Dynamics

Czech Science Foundation

Publisher

Informa UK Limited

Subject

Mechanics of Materials,Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/10298436.2023.2257852

Reference177 articles.

1. AASHTO. 2021. T307-99: Standard Method of Test for Determining the Resilient Modulus of Soils and Aggregate Materials. In American Association of State Highway and Transportation Officials (pp. 1–41).

2. Detecting Fake Websites: The Contribution of Statistical Learning Theory

3. Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

4. Characterisation and modelling of subgrade resilient modulus for pavement structural design in Egypt

5. Impact of activation functions and number of layers on detection of exudates using circular Hough transform and convolutional neural networks

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