Informatics Publishing Limited

Suneet Kaur ¹, V. S. Ubboveja ², Alka Agarwal ³

Affiliations
1 Department of Computer Sciences, M.A.N.I.T, Bhopal, IN
2 Mahakal Institute of Science and Technology, Ujjain, IN
3 U.I.T., RGPV, Bhopal, IN

Abstract

Pavement surface deflection of a highway is a primary factor for evaluating the pavement strength of a flexible pavement. Benkelman Beam Deflection (BBD) technique is widely used in the country for evaluating the structural capacity of an existing flexible pavement as also for estimation and design of overlays for strengthening of a weak pavement. The field test for measuring the surface deflection is expensive and time consuming, and alternate modeling methods to estimate surface deflection of a pavement, therefore, would result in substantial savings in time and money in the preparation of detailed project reports for the large highway rehabilitation and strengthening projects being undertaken in the country. An attempt has been made in this paper to compare the results obtained from the models based on Multivariate Regression analysis and Artificial Neural Network to predict reasonably accurate characteristic deflection of flexible pavements. Data used for building the model was collected from field tests conducted by various entities in the state of Madhya Pradesh engaged in the rehabilitation and strengthening of highways in the State passing through extensive black cotton soil areas.

Keywords

Flexible Pavements, Artificial Neural Network, Multivariate Regression Analysis.

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