Staff View: A hybrid intelligence approach to enhance the prediction accuracy of local scour depth at complex bridge piers

A hybrid intelligence approach to enhance the prediction accuracy of local scour depth at complex bridge piers

Local scour depth at complex piers (LSCP) cause expensive costs when constructing bridges. In this study, a hybrid artificial intelligence approach of random subspace (RS) meta classifier, based on the reduced error pruning tree (REPTree) base classifier, namely RS-REPTree, was proposed to predict t...

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Main Authors:	Bui, D. T., Shirzadi, A., Amini, A., Shahabi, H., Al-Ansari, N., Hamidi, S., Singh, S. K., Pham, B. T., Ahmad, B. B., Ghazvinei, P. T.
Format:	Article
Language:	English
Published:	MDPI AG 2020
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.utm.my/id/eprint/86878/1/DieuTienBui2020_AHybridIntelligenceApproach.pdf http://eprints.utm.my/id/eprint/86878/ https://dx.doi.org/10.3390/su12031063
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spelling	my.utm.868782020-10-22T04:09:16Z http://eprints.utm.my/id/eprint/86878/ A hybrid intelligence approach to enhance the prediction accuracy of local scour depth at complex bridge piers Bui, D. T. Shirzadi, A. Amini, A. Shahabi, H. Al-Ansari, N. Hamidi, S. Singh, S. K. Pham, B. T. Ahmad, B. B. Ghazvinei, P. T. TA Engineering (General). Civil engineering (General) Local scour depth at complex piers (LSCP) cause expensive costs when constructing bridges. In this study, a hybrid artificial intelligence approach of random subspace (RS) meta classifier, based on the reduced error pruning tree (REPTree) base classifier, namely RS-REPTree, was proposed to predict the LSCP. A total of 122 laboratory datasets were used and portioned into training (70%: 85 cases) and validation (30%: 37 cases) datasets for modeling and validation processes, respectively. The statistical metrics such as mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (R), and Taylor diagram were used to check the goodness-of-fit and performance of the proposed model. The capability of this model was assessed and compared with four state-of-the-art soft-computing benchmark algorithms, including artificial neural network (ANN), support vector machine (SVM), M5P, and REPTree, along with two empirical models, including the Florida Department of Transportation (FDOT) and Hydraulic Engineering Circular No. 18 (HEC-18). The findings showed that machine learning algorithms had the highest goodness-of-fit and prediction accuracy (0.885 < R < 0.945) in comparison to the other models. The results of sensitivity analysis by the proposed model indicated that pile cap location (Y) was a more sensitive factor for LSCP among other factors. The result also depicted that the RS-REPTree ensemble model (R = 0.945) could well enhance the prediction power of the REPTree base classifier (R = 0.885). Therefore, the proposed model can be useful as a promising technique to predict the LSCP. MDPI AG 2020 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/86878/1/DieuTienBui2020_AHybridIntelligenceApproach.pdf Bui, D. T. and Shirzadi, A. and Amini, A. and Shahabi, H. and Al-Ansari, N. and Hamidi, S. and Singh, S. K. and Pham, B. T. and Ahmad, B. B. and Ghazvinei, P. T. (2020) A hybrid intelligence approach to enhance the prediction accuracy of local scour depth at complex bridge piers. Sustainability (Switzerland), 12 (3). ISSN 2071-1050 https://dx.doi.org/10.3390/su12031063 DOI:10.3390/su12031063
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
language	English
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Bui, D. T. Shirzadi, A. Amini, A. Shahabi, H. Al-Ansari, N. Hamidi, S. Singh, S. K. Pham, B. T. Ahmad, B. B. Ghazvinei, P. T. A hybrid intelligence approach to enhance the prediction accuracy of local scour depth at complex bridge piers
description	Local scour depth at complex piers (LSCP) cause expensive costs when constructing bridges. In this study, a hybrid artificial intelligence approach of random subspace (RS) meta classifier, based on the reduced error pruning tree (REPTree) base classifier, namely RS-REPTree, was proposed to predict the LSCP. A total of 122 laboratory datasets were used and portioned into training (70%: 85 cases) and validation (30%: 37 cases) datasets for modeling and validation processes, respectively. The statistical metrics such as mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (R), and Taylor diagram were used to check the goodness-of-fit and performance of the proposed model. The capability of this model was assessed and compared with four state-of-the-art soft-computing benchmark algorithms, including artificial neural network (ANN), support vector machine (SVM), M5P, and REPTree, along with two empirical models, including the Florida Department of Transportation (FDOT) and Hydraulic Engineering Circular No. 18 (HEC-18). The findings showed that machine learning algorithms had the highest goodness-of-fit and prediction accuracy (0.885 < R < 0.945) in comparison to the other models. The results of sensitivity analysis by the proposed model indicated that pile cap location (Y) was a more sensitive factor for LSCP among other factors. The result also depicted that the RS-REPTree ensemble model (R = 0.945) could well enhance the prediction power of the REPTree base classifier (R = 0.885). Therefore, the proposed model can be useful as a promising technique to predict the LSCP.
format	Article
author	Bui, D. T. Shirzadi, A. Amini, A. Shahabi, H. Al-Ansari, N. Hamidi, S. Singh, S. K. Pham, B. T. Ahmad, B. B. Ghazvinei, P. T.
author_facet	Bui, D. T. Shirzadi, A. Amini, A. Shahabi, H. Al-Ansari, N. Hamidi, S. Singh, S. K. Pham, B. T. Ahmad, B. B. Ghazvinei, P. T.
author_sort	Bui, D. T.
title	A hybrid intelligence approach to enhance the prediction accuracy of local scour depth at complex bridge piers
title_short	A hybrid intelligence approach to enhance the prediction accuracy of local scour depth at complex bridge piers
title_full	A hybrid intelligence approach to enhance the prediction accuracy of local scour depth at complex bridge piers
title_fullStr	A hybrid intelligence approach to enhance the prediction accuracy of local scour depth at complex bridge piers
title_full_unstemmed	A hybrid intelligence approach to enhance the prediction accuracy of local scour depth at complex bridge piers
title_sort	hybrid intelligence approach to enhance the prediction accuracy of local scour depth at complex bridge piers
publisher	MDPI AG
publishDate	2020
url	http://eprints.utm.my/id/eprint/86878/1/DieuTienBui2020_AHybridIntelligenceApproach.pdf http://eprints.utm.my/id/eprint/86878/ https://dx.doi.org/10.3390/su12031063
_version_	1681489486691696640
score	13.211869

A hybrid intelligence approach to enhance the prediction accuracy of local scour depth at complex bridge piers

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