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In this study, soft computing and multilinear regression techniques were employed to develop models for prediction of progression of chip seal percent embedment depth ( Pe). The model uses inputs such as cumulative equivalent traffic volume, Vialit test results, dust content of aggregates, and initial embedment depth. Multilinear regression, adaptive neuro-fuzzy system, and artificial neural network techniques were used to estimate the Pe. The contribution of the variables affecting Pe was evaluated through a sensitivity analysis. The results indicate that while most of the proposed models were able to predict the Pe reasonably, the artificial neural network model performed the best.