Abstract
Very often it is difficult to develop mechanistic models for pavement geotechnical engineering problems due to its complex nature and uncertainty in material parameters. The difficulty in mechanistic analysis has forced the engineers to follows certain empirical correlations. The artificial neural network (ANN) is being as an alternate statistical method, mapping in higher-order spaces, such models can go beyond the existing univariate relationships. The applications of ANNs in pavement geotechnical engineering problems is mostly limited to constitutive modeling, with few applications on prediction of soil layer properties using Falling Weight Deflectometer (FWD), prediction of swelling potential and compute the remaining life of flexible pavements. However, ANN is considered as a ‘Black box’ system being unable to explain interrelation between inputs and output. The ANNs also have inherent drawbacks such as slow convergence speed, less generalizing performance, arriving at local minimum and over-fitting problems. Recently support vector machine (SVM) is being used due to its, better generalization as prediction error and model complexity are simultaneously minimized. SVM is based on statistical learning theory unlike ANNs (biological learning theory). The application of SVM in pavement geotechnical engineering is very much limited and to best of the knowledge such methods have not been applied to pavement geotechnical engineering. However, engineering application of numerical methods is a science as well as an art. This juxtaposition is based on the fact that even though the developed algorithms are based on scientific logic and belong to the special branch of applied mathematics, their successful application to new problems is problem oriented and is an art. As no method can be the panacea to solve all problems to the last details, their application to new areas needs critical evaluation. With above in view, an attempt has been made to develop the art of applying the above artificial intelligence techniques (ANN and SVM) to different pavement engineering problems such as prediction of compaction characteristics, permeability, swelling potential, coefficient of subgrade reaction etc. The parameters associated with the model developments are discussed in terms of guide line for its future
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Samui, P., Das, S.K., Sitharam, T.G. (2009). Application of Soft Computing Techniques to Expansive Soil Characterization. In: Gopalakrishnan, K., Ceylan, H., Attoh-Okine, N.O. (eds) Intelligent and Soft Computing in Infrastructure Systems Engineering. Studies in Computational Intelligence, vol 259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04586-8_11
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