Prediction of IRI in short and long terms for flexible pavements: ANN and GMDH methods

Abstract

Prediction of pavement condition is one of the most important issues in pavement management systems. In this paper, capabilities of artificial neural networks (ANNs) and group method of data handling (GMDH) methods in predicting flexible pavement conditions were analysed in three levels: in 1 year, in 2 years (short term) and in the pavement life cycle (long term). For this purpose, three effective groups on pavement condition including traffic conditions, environmental changes and pavement structures were studied and nine effective variables were selected as input variables. International roughness index (IRI) was also used as the indicator of pavement performance. Various ANN structures and GMDH models in partial description configurations of 1st, 2nd, 3rd and 4th polynomials were formed and examined. Results indicate that while ANN models predict future condition of pavement with high accuracy in the short and long terms, GMDH models do not have accepted accuracy.

Development of pavement performance prediction model for bituminous hot mix asphalt on interurban road networks

“…Üstyapıların performansını tahmin etmek için çeşitli modelleme yaklaşımları ve istatistik yöntemler kullanılmaktadır [6].…”

Development of Roughness Prediction Models for Laos National Road Network

“…The RMS/PRoMMS database has been improved and updated throughout the period (2012-2016) by the JICA expert team as part of the Project "Improvement of Road Management Capability in Lao PDR" [23]. The pavement deterioration models are essential for PMS and are utilized to define various functions [8], as follows: [21,22,[25][26][27][28][29][30][31][32][33][34][35]. Some of these models were developed utilizing the Long-Term Pavement Performance (LTPP) database, whereas others were derived depending on direct field measurements or the domestic agency database.…”

Comparative Analysis of Machine Learning Models for Prediction of Remaining Service Life of Flexible Pavement

“…Through reviewing the pavement performance models, it was found that the Pavement Condition Index (PCI) [8], International Roughness Index (IRI) [3,[9][10][11][12], Present Serviceability Index (PSI) [5,13], Remaining Service Life (RSL) [14], and Present Serviceability Ratio (PSR) [15] indices were more commonly used by the researchers. In terms of the modelling approach, methods such as artificial neural networks (ANN) [5,12], support vector machine (SVM) [1,11,14,15], radial basis function (RBF) [11], gene expression programming (GEP) [10], regression tree (RT) [13,16], Random Forest (RF) [3,17], and genetic programming (GP) [8] were used in the literature.…”