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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Autores principales:	Kennedy C. Onyelowe, Jamshid Shakeri, Hasel Amini-Khoshalann, A. Bunyamin Salahudeen, Emmanuel E. Arinze, Hyginus U. Ugwu
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	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) Materials of engineering and construction. Mechanics of materials TA401-492
Acceso en línea:	https://doaj.org/article/5cf724c35fa74b5b85c5fc21d814417a
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spelling	oai:doaj.org-article:5cf724c35fa74b5b85c5fc21d814417a2021-12-01T05:07:00ZApplication of ANFIS hybrids to predict coefficients of curvature and uniformity of treated unsaturated lateritic soil for sustainable earthworks2772-397610.1016/j.clema.2021.100005https://doaj.org/article/5cf724c35fa74b5b85c5fc21d814417a2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2772397621000058https://doaj.org/toc/2772-3976Unsaturated 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.Kennedy C. OnyeloweJamshid ShakeriHasel Amini-KhoshalannA. Bunyamin SalahudeenEmmanuel E. ArinzeHyginus U. UgwuElsevierarticleSoft 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)Materials of engineering and construction. Mechanics of materialsTA401-492ENCleaner Materials, Vol 1, Iss , Pp 100005- (2021)
institution	DOAJ
collection	DOAJ
language	EN
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) Materials of engineering and construction. Mechanics of materials TA401-492
spellingShingle	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) Materials of engineering and construction. Mechanics of materials TA401-492 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
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.
format	article
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
title	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_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_sort	application of anfis hybrids to predict coefficients of curvature and uniformity of treated unsaturated lateritic soil for sustainable earthworks
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/5cf724c35fa74b5b85c5fc21d814417a
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Application of ANFIS hybrids to predict coefficients of curvature and uniformity of treated unsaturated lateritic soil for sustainable earthworks

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