Data and Code for manuscript titled: <b>Surrogate Model</b><b>l</b><b>ing of Surface Roughness for Asphalt Pavements Using Artificial Neural Networks: A Mechanistic-Empirical Approach</b>
Creators: name: Li, Haoran
name: AzariJafari, Hessam
name: Kirchain, Randolph
name: Oliveira dos Santos, Joao
name: Khazanovich, Lev
Publisher: name: figshare
Year: 2024
Subjects: classificationCode: 400508
subject: Infrastructure engineering and asset management
classificationCode: 400512
subject: Transport engineering
classificationCode: 400510
subject: Structural engineering
classificationCode: 461104
subject: Neural networks

description: Data and Code for - <b>Surrogate Modelling of Surface Roughness for Asphalt Pavements Using Artificial Neural Networks: A Mechanistic-Empirical Approach</b><b>Introduction</b>These data and code accompany the manuscript titled "Surrogate Modelling of Surface Roughness for Asphalt Pavements Using Artificial Neural Networks: A Mechanistic-Empirical Approach" by: Haoran Li, Hessam AzariJafari, Randolph Kirchain, João Santos, and Lev Khazanovich. It was submitted to the International Journal of Pavement Engineering (IJPE) and is under review.The appended folders and files provide the data and code generated and used to analyze the results as described in the study.The following sections explain the structure and content of these files.<b>Data and Code Folder(s) or file(s) and how to use the code or data:</b>"<b>1_Sensitivity Analysis</b>": Contains the data obtained from Pavement ME in performing global sensitivity analysis (GSA). Please run "Paper_Sensitivity Analysis_PieChart.ipynb" to visualize the sensitivity analysis results and reproduce Figure 1 (a) and (b);"<b>Pavement ME Damage Database</b>": Contains the pavement damage databases (train and test) obtained from Pavement ME simulations and were used to develop and validate the surrogate ANN models, including both Model I and Model II;"<b>2_Model I</b>": Contains ANN models in consisting Model I. Please run "Model I Performance Validation.py" to assess its performance in IRI prediction and to reproduce Figure 6 (needs "Pavement ME Damage Database" folder);"<b>3_Model II</b>": Contains ANN models in consisting Model II. Please run "Model II Performance Validation.py" to assess its performance in IRI prediction and to reproduce Figure 7 (needs "Pavement ME Damage Database" folder);"<b>4_Model I vs. Model II</b>": Compare the performance of Model I and Model II in IRI predictions. Please run "Model I vs Model II Performance Validation.py" to compare the performance between Model I and Model II and also to reproduce Figure 8;"<b>5_Validation of Model I and II vs. Pavement ME</b>": Additional validation of Model I and Model II using Pavement ME and sensitivity analysis. Please run "ANN vs Pavement ME IRI_MA Sensitivity Analysis_ALL_IRI_SI.py" to assess the performance of Model I and Model II validated by Pavement ME simulations for the same projects. Also reproduce Figure 9;"<b>6_AC thickness design application using Model II</b>": Apply Model II for asphalt pavement design practice following MEPDG framework. Please run "AC thickness design with Model II.py" to perform a sample AC thickness design using Model II for Boston, MA and Miami, FL and reproduce Figure 10;<br><b>Acknowledgment</b>This work has been supported by the Concrete Sustainability Hub at MIT, with sponsorship provided by the Concrete Advancement Foundation.<b>Disclaimer</b>The code and data provided in this appendix are made available upon reasonable request from the readers to the corresponding author and under the condition that they are used for academic and research purposes only. Sharing the code or data without explicit and written permission from the authors is strictly prohibited. Additionally, using the code or data for another publication without prior authorization is strictly prohibited.The authors retain full ownership of the code and data. Any modification, redistribution, or use of the provided materials for purposes beyond what is outlined here requires the authors' consent. The authors are not responsible for any consequences that may arise from the use of the provided material, including, but not limited to, errors in execution of code or analysis, misinterpretation of results, or any subsequent actions based on this data or code.For further questions or support, please contact the corresponding author, Dr. Haoran Li at haoranli@mit.edu.