Abstract
In the present study, various soft computing techniques were employed for predicting the Marshall stability (MS) of asphalt concrete reinforced with polypropylene fibre, namely Multiple Linear Regression (MLR), Reduced Error Pruning (REP), Random tree (RT), Random Forest (RF), M5P_pruned and M5P_unpruned-based models, and the performance of these models was evaluated statistically. A total data set of 138 samples has been collected from various reliable and authentic published experimental researches, out of which 96 samples are used for training and 42 samples for testing using 70:30 split ratio. For each soft computing technique, coefficient of correlation (CC), Wilmott index (WI), mean absolute error (MAE), root-mean-square error (RMSE), Nash–Sutcliffe model efficiency coefficient (NSE), scattering index (SI) and mean absolute percentage error (MAPE) were calculated as the statistical indicators for analysing the performance of each soft computing technique. The RF model's dominance over the other models was confirmed by statistical indicators with values of CC as 0.9141, WI as 0.950, MAE as 1.1028, RMSE as 1.661, NSE as 1.0006, SI as 0.12077 and MAPE as 7.802, while the RT model also has shown competitive prediction ability over the M5P_pruned and M5P_unpruned, REP and MLR models. A sensitivity analysis presented that the bitumen content was the most effective parameter for predicting the Marshall stability using RF-based model. The results of this computational evaluation clearly showed that the applied soft-computing technique was capable of accurately calculating the Marshall stability of the asphalt concrete.
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The contribution of authors is as follows: S.J., M.S. helped in conceptualization and methodology; S.J. collected the data and writing—original draft; M.S. contributed to supervision, writing—review and editing and analysis and interpretation. All the authors have read and approved the final version of manuscript.
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Jalota, S., Suthar, M. Prediction of Marshall stability of asphalt concrete reinforced with polypropylene fibre using different soft computing techniques. Soft Comput 28, 1425–1444 (2024). https://doi.org/10.1007/s00500-023-08339-x
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DOI: https://doi.org/10.1007/s00500-023-08339-x