An Analytical Failure Envelope for the Design of Textile Reinforced Concrete Shells. (August 2018) - Hawkins, Will; Orr, John; Ibell, Tim; Shepherd, Paul
Abstract: Shells have the potential to considerably reduce material consumption in buildings due to their high structural efficiency compared to equivalent structures acting in bending. Textile reinforced concrete (TRC) is a promising material for the construction of thin concrete shells due to its strength, geometric versatility, and durability. Existing design methods for TRC shells predicts the local capacity by linear interpolation between experimentally determined values of strength in pure tension, pure bending, and pure compression. This simplification leads to a significant underestimation of strength in combined bending and compression. Relying entirely on physical test results also effectively prohibits exploration and optimisation of the shell design. This paper proposes a new analytical design approach for TRC which is instead derived from the properties of the concrete and reinforcement, and for the first time captures the highly non-linear interaction between axial and bending forces. A series of pure tension, pure bending, and combined bending and compression tests were carried out on TRC specimens of 15 mm and 30 mm thickness. The predicted strengths were conservative under combined compression and bending but otherwise accurate. For the specimens tested, the proposed method increases the predicted strength by a factor of up to 3.7 compared to existing methods, whilst remaining conservative, and hence its use could lead to significant material savings and new applications for TRC shells. Graphical abstract: Highlights: The strength of TRC under combined axial and bending loads is investigated. An analytical failure envelope is proposed, derived from constituent material properties. This captures the observed non-linear relationship between axial and bending strength. The predicted strengths are accurate or moderately conservative across the envelope. The model gives greater accuracy and requires less experimental effort than current methods.