Experimental and numerical investigations of recycled aggregate concrete-filled stainless steel tube stub columns under combined compression and bending

• Main Authors: Zhong, Yukai, Zhao, Ou, Young, Ben
• 其他作者: School of Civil and Environmental Engineering
• 格式: Article
• 語言: English
• 出版: 2022
• 主題: Engineering::Civil engineering; Design Codes; Eccentric Compression Tests
• 在線閱讀: https://hdl.handle.net/10356/163482

This paper reports experimental and numerical investigations into the behaviour and resistance of recycled aggregate concrete-filled stainless steel tube stub columns under combined compression and bending. Eccentric compression tests were conducted on twelve stub column specimens fabricated from austenitic stainless steel tubes with two cross-section sizes and concretes with three recycled coarse aggregate replacement ratios (0%, 35% and 70%). The test results, including the failure loads, load–mid-height lateral deflection curves, evolution of neutral axis and confinement effect, were discussed in detail. The testing programme was followed by a numerical modelling programme, where finite element models were developed and validated against the test results and then used to perform parametric studies to generate further numerical data over a wide range of cross-section dimensions and loading combinations. Given that there are no existing design codes for recycled aggregate concrete–stainless steel composite structures, the relevant codified design rules for natural aggregate concrete filled-carbon steel tube stub columns under combined compression and bending, as set out in the European code, American specification and Australian/New Zealand standard, were evaluated for their applicability to recycled aggregate concrete filled-stainless steel tube stub columns, based on the test and numerical data. The evaluation results revealed that the European code and Australian/New Zealand standard generally provide an acceptable level of design accuracy, while the American specification yields unduly conservative failure load predictions.