Effect of Noisy Data on Pavement Performance Prediction by Artificial Neural Networks

Abstract

Artificial neural networks are increasingly employed in prediction modeling and are particularly advantageous when the relationship between the response and the predictor variables is complex. For the purposes of prediction, neural networks are to be trained with data that are accurately compiled. Frequently, the data collected either from field or laboratory observations are noisy in nature. The effect of noisy data on the predictive capability of neural networks has been studied. Present serviceability rating (PSR) of pavements is the attribute to be predicted. Six noisy databases are created and are employed to train the neural networks to predict PSR. Regression equations are developed with the same noisy databases, and the predictions from neural networks are compared with those of regression. The results show that the neural networks predict PSR as accurately as regression models with a given noisy data. In addition, neural networks are trained with data containing no noise. If no noise is present in the data, neural networks predict PSR accurately while properly capturing the effect of each explanatory variable on the response variable.

Prediction of Marshall test results for polypropylene modified dense bituminous mixtures using neural networks

“…Kim and Kim (1998) used artificial neural networks for prediction of layer module from falling weight deflectometer (FWD) and surface wave measurements. Shekharan (1998) studied the effect of noisy data on pavement performance prediction by an artificial neural network with genetic algorithm. Attoh-Okine (2001) uses the self-organizing map or competitive unsupervised learning model of Kohonen for grouping of pavement condition variables (such as the thickness and age of pavement, average annual daily traffic, alligator cracking, wide cracking, potholing, and rut depth) to develop a model for evaluation of pavement conditions.…”

Polypropylene fiber-reinforced bitumen

“…Marshall Quotient values, which are in fact of 'pseudo-stiffness', are also related to stability and flow values, therefore their prediction is also very important. Detailed knowledge about the applications of artificial neural networks in transportation and pavement engineering can be found in the relevant literature (Tapkın 2004;ritchie et al 1991;kaseko and ritchie 1993;Gagarin et al 1994;Eldin and Senouci 1995;cal 1995;razaqpur et al 1996;roberts and attoh-Okine 1998;Owusu-ababia 1998;alsugair and al-Qudrah 1998;kim and kim 1998;Shekharan 1998;attoh-Okine 2001attoh-Okine , 2005Lee and Lee 2004;Mei et al 2004;Bosurgi and Trifiro 2005;zeghal 2008;Xue et al 2009;Alavi et al 2011;Mirzahosseini et al 2011). Throughout this part of the study, artificial neural networks were utilised in order to predict the stability, flow and Marshall Quotient values of asphalt concrete specimens obtained from a series of Marshall designs, based on experimental results described above.…”

Artificial Intelligence in Transportation: Information for Application

“…Network performance was then verified using data obtained from experimental surveys. NNs were also used to predict the present serviciability rating (PSR) of pavements (Shekharan, 1998). The input variables were structural number, age and cumulative equivalent single-axle loads.…”