Application of ANFIS hybrids to predict coefficients of curvature and uniformity of treated unsaturated lateritic soil for sustainable earthworks
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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
Cleaner Materials
Abstract
Unsaturated lateritic soils are complex soils to work with due to moisture effects. So, the determination of its
properties requires lots of time, labor and equipment. For this reason, the application of evolutionary learning
techniques has been adopted to overcome these complexities. Lateritic soil under unsaturated condition classified
as poorly graded and A‐7–6 group was subjected to treatment by using hybrid cement and nanostructured
quarry fines in a stabilization method. The clay activity, clay content and frictional angle were
determined through multiple experiments at different proportions of the additives. 121 datasets were collected
through the multiple testing of treated specimens and 70% and 30% of the datasets were used in the model
training and testing, respectively to predict the coefficients of curvature and uniformity (Cc and Cu) of the
unsaturated lateritic soil. Fist, the multi‐linear regression (MLR) model showed that the selected input parameters
correlated well with the output parameters. The model performance evaluation and validation selected
indicators; R2, RMSE and MAE showed that ANFIS with 0.9999, 0.0021 and 0.0015 respectively, for the training
and 0.9994, 0.0077 and 0.0059 respectively outclassed all its hybrid techniques and MLR in both training
and testing. However, ANFIS‐PSO with performance indicators 0.9996, 0.0062 and 0.0050 respectively (training)
and 0.9989, 0.0095 and 0.0073 respectively (testing); followed by ANFIS‐GA; 0.9991, 0.0094, and 0.0065
respectively (training) and 0.0089, 0.0099, and 0.0079 (testing) outclassed the other learning techniques for
the Cc prediction model while ANFIS‐GA; 0.9949, 0.1000, and 0.0798 respectively (training) and 0.9954,
0.0983, and 0.0807 respectively, followed by ANFIS‐PSO; 0.9893, 0.1347, and 0.1011 respectively (training)
and 0.9951, 0.1127, and 0.0924 respectively outclassed the other techniques for the Cu prediction model.
Finally, ANFIS and its evolutionary hybrid techniques have shown their usefulness and flexibility in predicting
stabilized unsaturated soil properties for sustainable earthwork design, construction and foundation
performance monitoring.
Description
Keywords
Soft computing, Unsaturated lateritic soil coefficients of curvature and uniformity, Hybrid Cement (HC), Adaptive Neuro Fuzzy Inference System (ANFIS): ANFIS‐PSO, ANFIS‐ACO, ANFIS‐GA and ANFIS‐DE, Multiple linear regression, Nanostructured Quarry Fines (NQF)
Citation
Onyelowe, K. C., Shakeri, J., Amini-Khoshalann, H., Salahudeen, A. B., Arinze, E. E., & Ugwu, H. U. (2021). Application of ANFIS hybrids to predict coefficients of curvature and uniformity of treated unsaturated lateritic soil for sustainable earthworks. Cleaner Materials, 1, 100005. https://doi.org/10.1016/j.clema.2021.100005