A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Investigation of Artificial Neural Network Models for Predicting the International Roughness Index of Rigid Pavements
The International Roughness Index (IRI) is the one of the most important roughness indexes to quantify road surface roughness. In this study, the backpropagation (BP) algorithm and conjugate gradient backpropagation algorithm were used to develop artificial neural network (ANN) model for the prediction of the IRI. A total of 913 samples in the case study of the experimental study was the Vietnamese Highway No.5 between Hanoi and Hai Phong, located in the northern part of Vietnam including 6 inputs and 1 output were collected for training and testing the ANN model. The reliability of ANN model is evaluated by some criteria such as correlation coefficient (R), root mean square error (RMSE) and mean absolute error (MAE). The best ANN architecture could be considered as a new tool for accurate prediction of the IRI for evaluation of quality of road surface roughness.
Investigation of Artificial Neural Network Models for Predicting the International Roughness Index of Rigid Pavements
The International Roughness Index (IRI) is the one of the most important roughness indexes to quantify road surface roughness. In this study, the backpropagation (BP) algorithm and conjugate gradient backpropagation algorithm were used to develop artificial neural network (ANN) model for the prediction of the IRI. A total of 913 samples in the case study of the experimental study was the Vietnamese Highway No.5 between Hanoi and Hai Phong, located in the northern part of Vietnam including 6 inputs and 1 output were collected for training and testing the ANN model. The reliability of ANN model is evaluated by some criteria such as correlation coefficient (R), root mean square error (RMSE) and mean absolute error (MAE). The best ANN architecture could be considered as a new tool for accurate prediction of the IRI for evaluation of quality of road surface roughness.
Investigation of Artificial Neural Network Models for Predicting the International Roughness Index of Rigid Pavements
Lecture Notes in Civil Engineering
Ha-Minh, Cuong (editor) / Tang, Anh Minh (editor) / Bui, Tinh Quoc (editor) / Vu, Xuan Hong (editor) / Huynh, Dat Vu Khoa (editor) / Ngo, Quoc Trinh (author) / Nguyen, Hoang-Long (author) / Le, Thanh-Hai (author)
CIGOS 2021, Emerging Technologies and Applications for Green Infrastructure ; Chapter: 187 ; 1851-1858
2021-10-28
8 pages
Article/Chapter (Book)
Electronic Resource
English
International roughness index (IRI) , Rigid pavements , Concrete , Artificial neural network , Machine learning Engineering , Geoengineering, Foundations, Hydraulics , Sustainable Architecture/Green Buildings , Sustainable Development , Structural Materials , Cyber-physical systems, IoT , Professional Computing
Artificial neural network modelling to predict international roughness index of rigid pavements
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