Soft computing technique in prediction of pavement condition

Authors:
Devinder Kaur;Debargha Datta
Affiliations:
Department of Electrical Engineering and Computer Science and Department of Civil Engineering, University of Toledo, Toledo, Ohio;Department of Electrical Engineering and Computer Science and Department of Civil Engineering, University of Toledo, Toledo, Ohio
Venue:
CIMMACS'07 Proceedings of the 6th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Year:
2007

Citing 1
Cited 1

Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence

Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence

Comparitive analysis of fuzzy decision tree and logistic regression methods for pavement treatment prediction

WSEAS Transactions on Information Science and Applications

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Abstract

This paper presents a soft computing technique using neuro fuzzy approach to predict the future pavement condition based on the current pavement age and current pavement condition. The Ohio Department of Transportation (ODOT) database for the asphalt pavement sections of Interstates and US routes was used to build the prediction model. Both grid partitioning and subtractive clustering based pattern recognition followed by back propagation learning algorithm was followed to build and optimize the models. The performances of both these models were compared with the conventional Markov chain method of pavement performance prediction. The study reveals that grid partitioning based model outperforms both the Markov chain model and the subtractive clustering based model.