Application of ANFIS hybrids to predict coefficients of curvature and uniformity of treated unsaturated lateritic soil for sustainable earthworks
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...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-12-01
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| Series: | Cleaner Materials |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772397621000058 |
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| author | Kennedy C. Onyelowe Jamshid Shakeri Hasel Amini-Khoshalann A. Bunyamin Salahudeen Emmanuel E. Arinze Hyginus U. Ugwu |
| author_facet | Kennedy C. Onyelowe Jamshid Shakeri Hasel Amini-Khoshalann A. Bunyamin Salahudeen Emmanuel E. Arinze Hyginus U. Ugwu |
| author_sort | Kennedy C. Onyelowe |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T14:14:45Z |
| format | Article |
| id | doaj.art-5cf724c35fa74b5b85c5fc21d814417a |
| institution | Directory Open Access Journal |
| issn | 2772-3976 |
| language | English |
| last_indexed | 2024-12-14T14:14:45Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Cleaner Materials |
| spelling | doaj.art-5cf724c35fa74b5b85c5fc21d814417a2022-12-21T22:58:13ZengElsevierCleaner Materials2772-39762021-12-011100005Application of ANFIS hybrids to predict coefficients of curvature and uniformity of treated unsaturated lateritic soil for sustainable earthworksKennedy C. Onyelowe0Jamshid Shakeri1Hasel Amini-Khoshalann2A. Bunyamin Salahudeen3Emmanuel E. Arinze4Hyginus U. Ugwu5Department of Civil and Mechanical Engineering, Kampala International University, Kampala, Uganda; Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, Nigeria; Corresponding authors.Department of Mining Engineering, Hamedan University of Technology, Hamadan, IranDepartment of Mining Engineering, Faculty of Engineering, University of Kurdistan, IranDepartment of Civil Engineering, Faculty of Engineering, University of Jos, NigeriaDepartment of Civil Engineering, Michael Okpara University of Agriculture, Umudike, NigeriaDepartment of Mechanical Engineering, Michael Okpara University of Agriculture, Umudike, NigeriaUnsaturated 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.http://www.sciencedirect.com/science/article/pii/S2772397621000058Soft computingUnsaturated lateritic soil coefficients of curvature and uniformityHybrid Cement (HC)Adaptive Neuro Fuzzy Inference System (ANFIS): ANFIS-PSO, ANFIS-ACO, ANFIS-GA and ANFIS-DEMultiple linear regressionNanostructured Quarry Fines (NQF) |
| spellingShingle | Kennedy C. Onyelowe Jamshid Shakeri Hasel Amini-Khoshalann A. Bunyamin Salahudeen Emmanuel E. Arinze Hyginus U. Ugwu Application of ANFIS hybrids to predict coefficients of curvature and uniformity of treated unsaturated lateritic soil for sustainable earthworks Cleaner Materials 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) |
| title | Application of ANFIS hybrids to predict coefficients of curvature and uniformity of treated unsaturated lateritic soil for sustainable earthworks |
| title_full | Application of ANFIS hybrids to predict coefficients of curvature and uniformity of treated unsaturated lateritic soil for sustainable earthworks |
| title_fullStr | Application of ANFIS hybrids to predict coefficients of curvature and uniformity of treated unsaturated lateritic soil for sustainable earthworks |
| title_full_unstemmed | Application of ANFIS hybrids to predict coefficients of curvature and uniformity of treated unsaturated lateritic soil for sustainable earthworks |
| title_short | Application of ANFIS hybrids to predict coefficients of curvature and uniformity of treated unsaturated lateritic soil for sustainable earthworks |
| title_sort | application of anfis hybrids to predict coefficients of curvature and uniformity of treated unsaturated lateritic soil for sustainable earthworks |
| topic | 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) |
| url | http://www.sciencedirect.com/science/article/pii/S2772397621000058 |
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