Collapse pressure of sandwich pipes with strain-hardening cementitious composite - Part 2: A suitable prediction equation. (March 2020) - Yang, Jiankun; Estefen, Segen F.; Fu, Guangming; Paz, Claudio M.; Lourenço, Marcelo Igor
Abstract: A comprehensive study on the collapse pressure and post-buckling behaviour of a sandwich pipe (SP) with a core of strain-hardening cementitious composite (SHCC) was carried out in Part 1 of two companion papers. The results in the Part 1 paper show that an SP with an SHCC core has a different collapse mechanism from an SP with a polypropylene core. Because of its weak inter-layer adhesion and a relatively hard core, the collapse pressure and the characteristic response of an SP with an SHCC core are more influenced by its strongest layer than by the summed strength of all its layers. Since this behaviour has never been reported before, current prediction equations for the collapse pressure of SP systems cannot capture the special behaviour of an SP with an SHCC core. Therefore, utilising the available prediction equations for an SP with an SHCC core may lead to unreliable estimates. This Part 2 paper is dedicated to addressing the challenge by proposing a suitable prediction equation. Based on the extensive simulation results carried out by the numerical model verified by experiments in the Part 1 paper, supervised machine learning techniques were applied to support the regression of different equation forms, which come from three sources: (a) equation forms proposed by previous researchers, (b) equation forms found by the automatic machine learning software EUREQA, and (c) equation forms proposed by us. Further, the performances of the equation forms in predicting accurate results for the collapse pressure were compared. Based on the comparative performances and accuracy, an equation was recommended for the design of SPs under external pressure. Highlights: The accuracies of equation forms on prediction of collapse pressure of SHCC sandwich pipes have been evaluated. As large errors were revealed in previous prediction equations, they are not recommended for SHCC sandwich pipes. An extensive search area of prediction equations with better accuracy have been covered by applying machine learning. With an insightful understanding of the physical background, the proposed prediction equation provides the best accuracy among all the examined equations.