Imperfection Sensitivity Detection in Pultruded Columns Using Machine Learning and Synthetic Data

Abstract

Experimental and theoretical solutions have shown that imperfections in wide-flanged structural columns may reduce the failure load of the column by as much as 30% with respect to that of a perfect column. Therefore, the early detection and prevention of such imperfections, which would likely reduce the load capacity of a structure, are critical for avoiding catastrophic failure. In the present article, we show how machine learning may be used to detect imperfection sensitivity in pultruded columns using observable column deformations occurring at loads as low as 30% of the design load. Abaqus simulations were used to capture the behavior of such columns of various lengths under service load. The deformations found from the simulations were used to train the machine learning algorithm. Similar deformations could be easily collected from in-service columns using inexpensive instrumentation. With over 3000 test cases, 95% accuracy in the correct detection of imperfection sensitivity was found. We anticipate that the proposed machine learning pipeline will enhance structural health monitoring, providing timely warning for potentially compromised structures.

Keywords

• structural health monitoring
• machine learning
• buckling
• imperfection sensitivity
• failure prevention