Accurate estimation of bearing capacity of stone columns reinforced: An investigation of different optimization algorithms

Fattahi, H; Ghaedi, H; Malekmahmoodi, F; Armaghani, DJ

Accurate estimation of bearing capacity of stone columns reinforced: An investigation of different optimization algorithms

Fattahi, H Ghaedi, H Malekmahmoodi, F Armaghani, DJ

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Publisher:: ELSEVIER SCIENCE INC
Publication Type:: Journal Article
Citation:: Structures, 2024, 64
Issue Date:: 2024-06-01

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Field	Value	Language
dc.contributor.author	Fattahi, H
dc.contributor.author	Ghaedi, H
dc.contributor.author	Malekmahmoodi, F
dc.contributor.author	Armaghani, DJ
dc.date.accessioned	2025-02-24T02:21:27Z
dc.date.available	2025-02-24T02:21:27Z
dc.date.issued	2024-06-01
dc.identifier.citation	Structures, 2024, 64
dc.identifier.issn	2352-0124
dc.identifier.issn	2352-0124
dc.identifier.uri	http://hdl.handle.net/10453/185276
dc.description.abstract	The precise estimation of bearing capacity (qrs) of stone columns reinforced with geogrid is crucial given the intricate nature of geotechnical materials and geological factors. However, the cost and complexity involved in determining qrs necessitate the use of a precise and consistent nonlinear equation suitable for diverse case studies. To address this, intelligent methods like nature-inspired optimization algorithms have emerged as effective solutions, enabling time and resource savings through accurate modeling. This research explores the utilization of two optimization algorithms, specifically Artificial Bee Colony (ABC) and Harmony Search (HS), for the estimation of qrs. Input parameters for modeling encompass the ratios of geogrid-reinforced layer diameter to footing diameter, GRSB and USB thickness to base diameter, unreinforced soft clay qrs, stone column length to diameter, and settlement to footing diameter. Finally, to assess the precision of the models, statistical indicators including Variance Account For (VAF), squared correlation coefficient (R2), mean absolute percentage error (MAPE), mean square error (MSE), and root mean square error (RMSE) were computed. According to the findings of this study, the accuracy achieved by employing smart methods using the ABC algorithm ranged from 0.981 to 0.989, with error rates ranging from 7.86 × 10−5 to 0.00883. Similarly, the accuracy of the HS algorithm was determined to be between 0.984 and 0.988, with error rates ranging from 3 × 10−5 to 0.00551. These results underscore the high accuracy of intelligent algorithms, offering a dependable means of determining qrs across various study areas while considering uncertainties.
dc.language	English
dc.publisher	ELSEVIER SCIENCE INC
dc.relation.ispartof	Structures
dc.relation.isbasedon	10.1016/j.istruc.2024.106519
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0905 Civil Engineering, 1202 Building
dc.subject.classification	4005 Civil engineering
dc.title	Accurate estimation of bearing capacity of stone columns reinforced: An investigation of different optimization algorithms
dc.type	Journal Article
utslib.citation.volume	64
utslib.for	0905 Civil Engineering
utslib.for	1202 Building
pubs.organisational-group	University of Technology Sydney
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/School of Civil and Environmental Engineering
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-02-24T02:21:25Z
pubs.publication-status	Published
pubs.volume	64

Abstract:

The precise estimation of bearing capacity (qrs) of stone columns reinforced with geogrid is crucial given the intricate nature of geotechnical materials and geological factors. However, the cost and complexity involved in determining qrs necessitate the use of a precise and consistent nonlinear equation suitable for diverse case studies. To address this, intelligent methods like nature-inspired optimization algorithms have emerged as effective solutions, enabling time and resource savings through accurate modeling. This research explores the utilization of two optimization algorithms, specifically Artificial Bee Colony (ABC) and Harmony Search (HS), for the estimation of qrs. Input parameters for modeling encompass the ratios of geogrid-reinforced layer diameter to footing diameter, GRSB and USB thickness to base diameter, unreinforced soft clay qrs, stone column length to diameter, and settlement to footing diameter. Finally, to assess the precision of the models, statistical indicators including Variance Account For (VAF), squared correlation coefficient (R2), mean absolute percentage error (MAPE), mean square error (MSE), and root mean square error (RMSE) were computed. According to the findings of this study, the accuracy achieved by employing smart methods using the ABC algorithm ranged from 0.981 to 0.989, with error rates ranging from 7.86 × 10−5 to 0.00883. Similarly, the accuracy of the HS algorithm was determined to be between 0.984 and 0.988, with error rates ranging from 3 × 10−5 to 0.00551. These results underscore the high accuracy of intelligent algorithms, offering a dependable means of determining qrs across various study areas while considering uncertainties.

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http://hdl.handle.net/10453/185276