Material Cost Optimization and Quality Control Improvement of Building Construction

Abstract

A building construction project cannot be completed successfully by only planning and scheduling. Project controlling that consists of project monitoring and updating, is the other critical stage. Construction materials cost and quality are becoming a severe problem in Ethiopia, especially in Ambo Town, where little consideration is given. The study tried to evaluate different factors affecting the materials cost and quality on building construction site, rank significant factors that are prevalently affecting the cost and quality of materials used for building projects from the point of view of the respondents and established a model to optimize materials cost and materials quality using multilinear regression method to come up with the model best fit. Purposive sampling was used, and 180 questionnaires were administered on building construction firms to analyze Material cost Optimization and quality control ImprovementCase Study of Ambo Town. To achieve this, a questionnaire survey containing factors affecting Building construction material cost and quality, which were identified by literature and Interview, was analyzed by using the Relative Importance Index, Pearson's correlation, and Multilinear regression model. Variables are statistically significant at 95% confidence level is assessed that all of the independent variables are positively related to Building Construction material cost and quality. The RII results showed that Material Wastage minimization (RII = 0.908), Proper workmanship (RII= 0.859), Sufficient Training of workers/staff (RII = 0.876), Proper forecasting of materials price in the market (RII = 0.850), Using suitable, safe and secure storage (RII = 0.886), Quality checking for each delivery of the aggregates to the site (RII = 0.889), Cutting of materials ( glass, tiles, plywood) (RII = 0.870) and Design changes (RII = 0.770) were ranked as the most significant among factors affecting building construction materials. Also, Two Multiple linear regression models were established for both dependent variables (material cost and material quality). The analysis results showed all independent variables were significant and had a strong positive relationship with dependent variables. The values of R2 for the developed equations indicated 0.771 for material cost, and 0.777 for material quality. Hence, the basic recommendations such as planning and monitoring construction activities and inspection and verification of materials were anticipated to be exercised by key role players of the construction industry.

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