Optimization of performance index of self-sensing spray reactive powder concrete based on response surface methodology

Abstract

This study aims to develop spay reactive powder concrete (SRPC) with excellent mechanical and self-sensing properties by using carbon fiber (CF) as reinforcement material in combination with RPC base mix ratio. A Box–Behnken model was designed using the response surface method to study the influence of silica fume (SF), fly ash (FA), and CF on the compressive strength and strain sensitivity coefficient (SSC) of SRPC with different dosages. Variance regression was used to analyze the interaction of each factor on the index, and multi-index optimization technology was utilized to optimize the response scheme. Results show that the compressive strength is 107.272 MPa and SSC is 34.429 under optimized conditions of 18% SF, 17.097% FA, and 0.5% CF. The strain sensitivity coefficient of SRPC is 5–17 times higher than that of the conventional strain gauge used for monitoring concrete. SRPC was prepared by adjusting the dosage of the water reducer and stirring time at a 0.2 water–binder ratio.