Rice husk ash as an indigenous construction material in building process, a statistical evaluation

Ndubuisi Micheal Odoanyanwu *, Ifeanyichukwu Hyginus Ivoke and Maduabuchi Vitalis Irouke

Department of Architecture, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria.
 
Research Article
Article DOI: 10.30574/gscaet.2021.1.1.0025
Publication history: 
Received on 01 March 2021; revised on 10 April 2021; accepted on 13 April 2021
 
Abstract: 
The research described the rice husk ash as an indigenous construction material used in building production process of the concrete design mix. The production process is analyzed statistically. The quality of concrete mixture is of inevitable concern to all stakeholders in the construction industry and on building production process in the zone when the climatic conditions of the zone are considered. The mix design ratio is investigated and all the prevailing construction/production practices are considered statistically to portray the experimental results in the system. The statistical tools applied in this research for clarity of the results are descriptive, normality, missing value analysis, process statistical summary and confidence estimation methods of statistics. The experimental matrix was designed using three level four factors. Twenty five (25) experimental runs was conducted the M-Estimator was used to obtain the missing value analysis, the estimate of the output parameter at each selected factor levels.  The results show that all the factors selected are fit for the experimental analysis. The factors in M-estimators show that the response (Slump) can be as low as 68.8924mm and as high as 145.5352mm. In descriptive statistic, the mean for the parameters: cement, water, fine husk, coarse aggregate and slump are 242.56 kg/m3, 6.00 kg/m3, 568.56 kg/m3, 111544 kg/m3 and 110.84mm respectively. The tools portray the necessary information in the data to understand what the data information for further experimental process analysis.
 
Keywords: 
Concrete; Estimators; Experimental Process; Statistics; Descriptive; Construction industry and climatic conditions
 
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