An evolutionary approach to formulate the compressive strength of roller compacted concrete pavement

Ashrafian, A; Gandomi, AH; Rezaie-Balf, M; Emadi, M

An evolutionary approach to formulate the compressive strength of roller compacted concrete pavement

Ashrafian, A Gandomi, AH Rezaie-Balf, M Emadi, M

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Measurement: Journal of the International Measurement Confederation, 2020, 152
Issue Date:: 2020-02-01

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Field	Value	Language
dc.contributor.author	Ashrafian, A
dc.contributor.author	Gandomi, AH
dc.contributor.author	Rezaie-Balf, M
dc.contributor.author	Emadi, M
dc.date.accessioned	2020-10-29T19:56:11Z
dc.date.available	2020-10-29T19:56:11Z
dc.date.issued	2020-02-01
dc.identifier.citation	Measurement: Journal of the International Measurement Confederation, 2020, 152
dc.identifier.issn	0263-2241
dc.identifier.issn	1873-412X
dc.identifier.uri	http://hdl.handle.net/10453/143596
dc.description.abstract	© 2019 Elsevier Ltd The construction and maintenance of roads pavement was a critical problem in the last years. Therefore, the use of roller-compacted concrete pavement (RCCP) in road problems is widespread. The compressive strength (fc) is the key characteristic of the RCCP caused to significant impact on the cost of production. In this study, an evolutionary-based algorithm named gene expression programming (GEP) is implemented to propose novel predictive formulas for the fc of RCCP. The fc is formulated based on important factor used in mixture proportion in three different combinations of dimensional form (coarse aggregate, fine aggregate, cement, pulverized fly ash, water, and binder), non-dimensional form (water to cement ratio, water to binder ratio, coarse to fine aggregate ratio and pulverized fly ash to binder ratio) and percentage form of input variables. A comprehensive and reliable database incorporating 235 experimental cases collected from several studies. Furthermore, mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (r), average absolute error (AAE), performance index (PI), and objective function (OBJ) as the internal standard statistical measures and external validation evaluated proposed GEP-based models. Uncertainty and parametric studies were carried out to verify the results. Moreover, sensitivity analysis to determine the importance of each predictor on fc of RCCP revealed that fine aggregate content and water to binder ratio is the most useful predictor in dimensional, non-dimensional and percentage forms, respectively. The proposed equation-based models are found to be simple, robustness and straightforward to utilize, and provide consequently new formulations for fc of RCCP.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Measurement: Journal of the International Measurement Confederation
dc.relation.isbasedon	10.1016/j.measurement.2019.107309
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0913 Mechanical Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.title	An evolutionary approach to formulate the compressive strength of roller compacted concrete pavement
dc.type	Journal Article
utslib.citation.volume	152
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-10-29T19:56:02Z
pubs.publication-status	Published
pubs.volume	152

Abstract:

© 2019 Elsevier Ltd The construction and maintenance of roads pavement was a critical problem in the last years. Therefore, the use of roller-compacted concrete pavement (RCCP) in road problems is widespread. The compressive strength (fc) is the key characteristic of the RCCP caused to significant impact on the cost of production. In this study, an evolutionary-based algorithm named gene expression programming (GEP) is implemented to propose novel predictive formulas for the fc of RCCP. The fc is formulated based on important factor used in mixture proportion in three different combinations of dimensional form (coarse aggregate, fine aggregate, cement, pulverized fly ash, water, and binder), non-dimensional form (water to cement ratio, water to binder ratio, coarse to fine aggregate ratio and pulverized fly ash to binder ratio) and percentage form of input variables. A comprehensive and reliable database incorporating 235 experimental cases collected from several studies. Furthermore, mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (r), average absolute error (AAE), performance index (PI), and objective function (OBJ) as the internal standard statistical measures and external validation evaluated proposed GEP-based models. Uncertainty and parametric studies were carried out to verify the results. Moreover, sensitivity analysis to determine the importance of each predictor on fc of RCCP revealed that fine aggregate content and water to binder ratio is the most useful predictor in dimensional, non-dimensional and percentage forms, respectively. The proposed equation-based models are found to be simple, robustness and straightforward to utilize, and provide consequently new formulations for fc of RCCP.

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http://hdl.handle.net/10453/143596