GitHub - ramonbotella/RAP-data-RILEM-TCRAP-TG5: Repository with data and code for the prediction of RAP DoA using Compaction Temperature, Air voids and ITS with machine learning techniques

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Repository with data and code for the prediction of RAP DoA using Compaction Temperature, Air voids and ITS with machine learning techniques

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Name		Name	Last commit message	Last commit date
Latest commit History 57 Commits
ANN		ANN
MPR		MPR
RFR		RFR
SVR		SVR
Readme.txt		Readme.txt

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This repository contains the different codes that train validate and test supervised and unsupervised machine learning models related with the TG-5 of the RILEM TC-264
on Reclaimed Asphalt Pavement. Each folder contains all the files necessary to run each of the models.
/MPR: Multivariate Polynomial Regression that predicts DoA (% max. ITS) using compaction temperature (ºC), air voids (%) and ITS (MPa)
/ANN: Artificial Neural Network (2 hidden layer with 5 hidden units each) that predicts DoA (% max. ITS) using compaction temperature (ºC), air voids (%) and ITS (MPa)
/RFR: Random Forest Regression that predicts DoA (% max. ITS) using compaction temperature (ºC), air voids (%) and ITS (MPa)