Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Direct Integration of Hybrid ANN Models into FORM/SORM for Reliability Analysis of Pile Foundations. |
| 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] |
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| Βάση Δεδομένων: |
Complementary Index |