Academic Journal
Direct Integration of Hybrid ANN Models into FORM/SORM for Reliability Analysis of Pile Foundations.
| Τίτλος: | Direct Integration of Hybrid ANN Models into FORM/SORM for Reliability Analysis of Pile Foundations. |
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| Συγγραφείς: | M, Karthikeyan, Kumar, Manish |
| Πηγή: | Transportation Infrastructure Geotechnology; Aug2026, Vol. 13 Issue 6, p1-25, 25p |
| Θεματικοί όροι: | Artificial neural networks, Imperialist competitive algorithm, Particle swarm optimization, Building foundations, Structural frames, Engineering reliability theory, Ant algorithms |
| Περίληψη: | Assessment of pile bearing capacity reliability is critical for foundation safety; however, incorporating advanced predictive models into reliability frameworks remains limited. This study focused on integrating hybrid ANN-predicted bearing capacity into the limit-state function for first- and second-order reliability methods (FORM and SORM). Hybrid-ANN models were developed using Imperialistic Competitive Algorithm (ICA), Ant Colony Optimization, Antlion Optimization (ALO), and Particle Swarm Optimization. The ANN-ICA model demonstrated superior convergence with lower Root Mean Square Error (RMSE) in both training (0.0088) and testing (0.0111) phases. The hybrid ANN-ALO model exhibited faster convergence but poorer predictive performance, with RMSE values of 0.0339 in training and 0.0329 in testing. The comprehensive measure (COM) was used to rank models based on multiple performance criteria. ANN-ICA ranked first with the lowest COM (0.034). The piles exhibited a moderate reliability level under an average loading of 500kN, with a reliability index (β) of 3.911 and 5.33 for 300 mm and 400 mm piles, respectively, using FORM, and 3.803 and 5.244 using SORM. Based on the Mutual Information sensitivity analysis, soil parameters exerted greater influence on the model output (33.43%, 20.51%, and 16.64% for unit weight, friction angle, and undrained cohesion, respectively). [ABSTRACT FROM AUTHOR] |
| Copyright of Transportation Infrastructure Geotechnology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Direct Integration of Hybrid ANN Models into FORM/SORM for Reliability Analysis of Pile Foundations. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22M%2C+Karthikeyan%22">M, Karthikeyan</searchLink><br /><searchLink fieldCode="AR" term="%22Kumar%2C+Manish%22">Kumar, Manish</searchLink> – Name: TitleSource Label: Source Group: Src Data: Transportation Infrastructure Geotechnology; Aug2026, Vol. 13 Issue 6, p1-25, 25p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Imperialist+competitive+algorithm%22">Imperialist competitive algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Building+foundations%22">Building foundations</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+frames%22">Structural frames</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+reliability+theory%22">Engineering reliability theory</searchLink><br /><searchLink fieldCode="DE" term="%22Ant+algorithms%22">Ant algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Assessment of pile bearing capacity reliability is critical for foundation safety; however, incorporating advanced predictive models into reliability frameworks remains limited. This study focused on integrating hybrid ANN-predicted bearing capacity into the limit-state function for first- and second-order reliability methods (FORM and SORM). Hybrid-ANN models were developed using Imperialistic Competitive Algorithm (ICA), Ant Colony Optimization, Antlion Optimization (ALO), and Particle Swarm Optimization. The ANN-ICA model demonstrated superior convergence with lower Root Mean Square Error (RMSE) in both training (0.0088) and testing (0.0111) phases. The hybrid ANN-ALO model exhibited faster convergence but poorer predictive performance, with RMSE values of 0.0339 in training and 0.0329 in testing. The comprehensive measure (COM) was used to rank models based on multiple performance criteria. ANN-ICA ranked first with the lowest COM (0.034). The piles exhibited a moderate reliability level under an average loading of 500kN, with a reliability index (β) of 3.911 and 5.33 for 300 mm and 400 mm piles, respectively, using FORM, and 3.803 and 5.244 using SORM. Based on the Mutual Information sensitivity analysis, soil parameters exerted greater influence on the model output (33.43%, 20.51%, and 16.64% for unit weight, friction angle, and undrained cohesion, respectively). [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Transportation Infrastructure Geotechnology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s40515-026-00944-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 1 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Imperialist competitive algorithm Type: general – SubjectFull: Particle swarm optimization Type: general – SubjectFull: Building foundations Type: general – SubjectFull: Structural frames Type: general – SubjectFull: Engineering reliability theory Type: general – SubjectFull: Ant algorithms Type: general Titles: – TitleFull: Direct Integration of Hybrid ANN Models into FORM/SORM for Reliability Analysis of Pile Foundations. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: M, Karthikeyan – PersonEntity: Name: NameFull: Kumar, Manish IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 21967202 Numbering: – Type: volume Value: 13 – Type: issue Value: 6 Titles: – TitleFull: Transportation Infrastructure Geotechnology Type: main |
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