Risk-aware buckling design of functionally graded porous beams via machinelearning surrogates and reliability analysis.
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| Title: | Risk-aware buckling design of functionally graded porous beams via machinelearning surrogates and reliability analysis. |
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| Authors: | Pitta, Satyasaibaba1 satyasaibabapitta@gmail.com, Ginka, Ranga Janardhana2, Banavathu, Balakrishna1 |
| Source: | International Journal of Automotive & Mechanical Engineering. Mar2026, Vol. 23 Issue 1, p13367-13392. 26p. |
| Subjects: | Structural reliability, Machine learning, Engineering reliability theory, Mechanical buckling, Monte Carlo method, Shear (Mechanics), Design techniques, Girders |
| Abstract: | Functionally graded porous beams offer high stiffness-to-weight ratios, but their buckling strength is sensitive to induced porosity variability. Designers, therefore, need tools that are both fast and explicitly risk-aware. This study develops and validates an interpretable methodology that combines higher-order shear deformation theory, machine learning surrogates, and structural reliability analysis to support buckling design of functionally graded porous beams. Deterministic buckling responses are first generated using a higher-order shear deformation theory for two boundary conditions (simply supported and clamped-clamped), two slenderness ratios (L/h=10 and 40), geometric controls (taper and width), porosity indices 0 |
| Copyright of International Journal of Automotive & Mechanical Engineering is the property of Universiti Malaysia Pahang, Faculty of Mechanical Engineering 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.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193881061 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Risk-aware buckling design of functionally graded porous beams via machinelearning surrogates and reliability analysis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Pitta%2C+Satyasaibaba%22">Pitta, Satyasaibaba</searchLink><relatesTo>1</relatesTo><i> satyasaibabapitta@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Ginka%2C+Ranga+Janardhana%22">Ginka, Ranga Janardhana</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Banavathu%2C+Balakrishna%22">Banavathu, Balakrishna</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Automotive+%26+Mechanical+Engineering%22">International Journal of Automotive & Mechanical Engineering</searchLink>. Mar2026, Vol. 23 Issue 1, p13367-13392. 26p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Structural+reliability%22">Structural reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+reliability+theory%22">Engineering reliability theory</searchLink><br /><searchLink fieldCode="DE" term="%22Mechanical+buckling%22">Mechanical buckling</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22Shear+%28Mechanics%29%22">Shear (Mechanics)</searchLink><br /><searchLink fieldCode="DE" term="%22Design+techniques%22">Design techniques</searchLink><br /><searchLink fieldCode="DE" term="%22Girders%22">Girders</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Functionally graded porous beams offer high stiffness-to-weight ratios, but their buckling strength is sensitive to induced porosity variability. Designers, therefore, need tools that are both fast and explicitly risk-aware. This study develops and validates an interpretable methodology that combines higher-order shear deformation theory, machine learning surrogates, and structural reliability analysis to support buckling design of functionally graded porous beams. Deterministic buckling responses are first generated using a higher-order shear deformation theory for two boundary conditions (simply supported and clamped-clamped), two slenderness ratios (L/h=10 and 40), geometric controls (taper and width), porosity indices 0<a<0.3, and two porosity patterns (Regular and Uneven). A gradient-boosted tree surrogate is then trained on the log-transformed dimensionless critical buckling load using five-fold crossvalidation. Manufacturing variability in porosity is propagated through the surrogate via Monte Carlo simulation, and a companion First-order Reliability Method (FORM) in the porosity dimension provides efficient estimates of failure probability. Risk is summarized through domain-wise mean buckling capacity and Conditional Value-at-Risk (CVaR) at the 5% level (CVaR5%), from which risk-return frontiers and "knee" designs distinguish risk-neutral from risk-averse choices. The surrogate generalizes strongly (out-of-fold log coefficient of determination, R2log=0.94; mean absolute error, MAE=2.7; mean absolute percentage error, MAPE=9.5%), while FORM tracks Monte Carlo with near-perfect concordance (average R2=0.9997; MAE(Pf)=0.0005; MAPE=0.33%), with small, interpretable deviations confined to slender, simply supported beams with uneven porosity. Across all regions, clamped-clamped, regular, and thick beams systematically dominate both mean and lower tail performance. Knee designs typically incur only 1-2% loss in mean capacity while retaining 95-99% of the best attainable CvaR, providing a reliable basis for compact, risk-aware buckling design guidance. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Automotive & Mechanical Engineering is the property of Universiti Malaysia Pahang, Faculty of Mechanical Engineering 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: 10.15282/ijame.23.1.2026.16.1014 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 13367 Subjects: – SubjectFull: Structural reliability Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Engineering reliability theory Type: general – SubjectFull: Mechanical buckling Type: general – SubjectFull: Monte Carlo method Type: general – SubjectFull: Shear (Mechanics) Type: general – SubjectFull: Design techniques Type: general – SubjectFull: Girders Type: general Titles: – TitleFull: Risk-aware buckling design of functionally graded porous beams via machinelearning surrogates and reliability analysis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pitta, Satyasaibaba – PersonEntity: Name: NameFull: Ginka, Ranga Janardhana – PersonEntity: Name: NameFull: Banavathu, Balakrishna IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 22298649 Numbering: – Type: volume Value: 23 – Type: issue Value: 1 Titles: – TitleFull: International Journal of Automotive & Mechanical Engineering Type: main |
| ResultId | 1 |
