Pavement Roughness Modeling Using Regression And Ann Methods For LTPP Western Region

Abstract

This study aims to develop pavement roughness models using multiple linear regression equation and artificial neural network (ANN) modeling approaches. The model database uses International Roughness Index (IRI) data included in a national database for the Western region. Datasets for asphalt pavement with bound base at 32 different locations are considered in the analysis. The variables included are IRI, pavement age, design structural number, equivalent single axle load (ESAL), and also a dummy variable for construction number. The LTPP data was used to compare predicted IRI values using the improved linear regression equation with R of 0.573, with different types of ANN models. The ANN models considered are static ANN, feedback ANN, and dynamic ANN. The verifications of the improved dummy regression equation predictions for 39 data points showed only-2.9% difference compared to the mean IRI. Comparisons between the mean predicted IRI values showed that the enhanced dummy regression equation gave a better prediction compared to the ANN model with a minimum error of 37.9%. Out of three ANN models, the feedback ANN provided a better prediction compared to the static and dynamic ANNs. Further analysis showed that the predictions using training all data sets are more accurate with R ranged from 0.740 to 0.827. It is important to consider ESAL and construction number for an accurate and reliable future IRI prediction.

Development of pavement roughness regression models based on smartphone measurements

“…Martin et al (2011) developed a pavement roughness deterioration model based on four different contributing components which were traffic, climatic, cracking and rutting effects. There is a wide range of studies that adopted regression analysis to develop relationships between pavement roughness and several pavement parameters, such as HOQUE et al (2008), Mubaraki (2010); Hong and Prozzi (2010) and Jaafar (2019).…”

Novel Soft-Computing Approach to Better Predict Flexible Pavement Roughness

“…In addition to the pavement age, traffic and structural features have been extensively employed to develop IRI performance models. Equivalent single axle load (ESAL) and cumulative ESAL (CESAL) have been widely used as a traffic loading feature by previous researchers ( 11 – 17 ). In addition, structural number (SN), as an important structural feature, has been broadly employed in IRI-prediction models ( 11–13 , 15 , 16 ).…”

“…Equivalent single axle load (ESAL) and cumulative ESAL (CESAL) have been widely used as a traffic loading feature by previous researchers ( 11 – 17 ). In addition, structural number (SN), as an important structural feature, has been broadly employed in IRI-prediction models ( 11–13 , 15 , 16 ). Choi et al ( 12 ) utilized CESAL, asphalt content, and SN to form an IRI-prediction model using multiple linear regression (MLR) and backpropagation neural network.…”

“…Jaafar and Fahmi ( 15 ) employed initial IRI, pavement age, ESAL, construction number, and SN to develop two IRI performance models using 34 observations from the Long-Term Pavement Performance (LTPP) database. The MLR and artificial neural network (ANN) techniques were utilized to establish the models.…”