Circular fibre-reinforced polymer (FRP)-concrete-steel hybrid multitube concrete columns: Compressive behaviour

This paper presents a study on circular fibre-reinforced polymer (FRP)-concrete-steel hybrid multitube concrete columns (MTCCs), which consists of an outer FRP tube, a number of inner small steel tubes to form a steel wall and concrete filled in the remaining spaces. The advantages of MTCCs include excellent axial load and deformation capacities, ease of construction, elimination/mitigation of difficulties in transporting and installing large steel tubes, and possibility of optimising the arrangement of the small steel tubes to improve structural performance. A total of 7 pairs of MTCCs, 4 pairs of concrete-filled FRP tubes (CFFTs), 4 pairs of concrete-filled steel walls (CFSWs) and 3 pairs of concrete-filled steel tubes (CFSTs) were tested in the present study, with the investigated parameters covering the thickness of FRP tube, the number and type of steel inner tubes, the type of concrete and status of the steel inner tubes. The test results lead to an in-depth understanding of the behaviour of MTCCs under axial compression. Furthermore, a comparison between the test results and predictions by a model previously proposed by the authors shows that the model can reasonably well predict the axial load-strain behaviour of MTCCs but largely underestimates the ultimate strain of the specimen. This model may be used for conservative design, while further investigation is needed for the development of a more accurate model. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This study conducted tests on circular fibre-reinforced polymer-concrete-steel hybrid multitube concrete columns, finding that they have excellent axial load and deformation capacities, suitable for engineering structures. Comparison between test results and model predictions revealed that the existing model can reasonably predict axial load-strain behavior but largely underestimates ultimate strain. Further research may help in developing a more accurate model.