Influence of Rice Straw Ash on Workability and Strength of Concrete

Abstract

Abstract Cement manufacturing is a major contributor to the waste of energy as about 3.4GJ of thermal energy is needed to produce 1 tonne of Portland cement, not to mention the emissions of greenhouse gases like carbon di oxide due to the calcination of limestone during Portland cement production. These emissions have caused a dramatic rise in global warming. There is a pressing need for innovative recycling technology that can reduce the negative effects of waste on ecosystems and people. Rice straws are the waste produced after the threshing of rice. The goal of this study is to investigate the influence of rice straw ash on workability and strength of concrete. The rice straw used was the straw of a specific rice variety locally called mass I gotten from Akpugo, Enugu, Nigeria. The rice straw was calcined by open burning to produce the rice straw ash (RSA). The materials used were preliminary tested to determine their physical properties and chemical composition. The rice straw ash was used to replace Ordinary Portland Cement (OPC) at 0, 5, 10, 15, 20 and 30% respectively. The workability and compressive strength test were done for all replacement levels. Using slump test, the concrete workability was evaluated. For this study, 72 cubes were cast, cured in water for 7, 14, 21, and 28days before been tested under compression. From the investigation, percentage increase in RSA replacement decreased the concrete workability. The sample containing 0%, 5%, and 10% RSA attained 9%, 6.1% and 2.2% more strength compared to the design strength at 28th day of curing. While the 15%, 20% and 30% attained 17.3%, 28.9% and 67.6% lower strength compared to the design strength at 28th day of curing. The experimental result was analysed using statistical product and service solution (SPSS) and excel spreadsheet regression (ESR). This study suggests that the optimum replacement level of rice straw ash from structural reinforced grade 25 concrete point of view is 10%. The predictive models were tested and found to be adequate.