Tên bài báo:

Hybrid Artificial Intelligence Approaches for Predicting Buckling Damage of Steel Columns Under Axial Compression
Tác giả:
Lý Hải Bằng
Tham gia cùng:
Phạm Thái Bình
Phạm Tuấn Anh
Nguyễn Duy Hưng
Tạp chí:
Năm xuất bản:
Từ trang 1 đến trang 18
Lĩnh vực:
Kỹ thuật xây dựng công trình giao thông
Phạm vi:
Quốc tế

Tóm tắt:

This study aims to investigate the prediction of critical buckling load of steel columns using two hybrid Artificial Intelligence (AI) models such as Adaptive Neuro-Fuzzy Inference System optimized by Genetic Algorithm (ANFIS-GA) and Adaptive Neuro-Fuzzy Inference System optimized by Particle Swarm Optimization (ANFIS-PSO). For this purpose, a total number of 57 experimental buckling tests of novel high strength steel Y-section columns were collected from the available literature to generate the dataset for training and validating the two proposed AI models. Quality assessment criteria such as coefficient of determination (R 2 ), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used to validate and evaluate the performance of the prediction models. Results showed that both ANFIS-GA and ANFIS-PSO had a strong ability in predicting the buckling load of steel columns, but ANFIS-PSO (R 2 = 0.929, RMSE = 60.522 and MAE = 44.044) was slightly better than ANFIS-GA (R 2 = 0.916, RMSE = 65.371 and MAE = 48.588). The two models were also robust even with the presence of input variability, as investigated via Monte Carlo simulations. This study showed that the hybrid AI techniques could help constructing an efficient numerical tool for buckling analysis.

Từ khóa:

buckling behavior Adaptive Neuro-Fuzzy Inference System Particle Swarm Optimization Genetic Algorithm steel column
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