Optimization of Concrete Mix Design Using Fractional Factorial Design and Response Surface Methodology

The study addresses the critical issue of optimizing construction materials to enhance structural integrity, minimize cracks, and prevent building collapses in Nigeria. It utilizes fractional factorial design and response surface methodology (RSM) to systematically determine the optimal parameters for concrete mix design to achieve maximum compressive strength. The key factors studied included standard sand size, curing time, water-cement ratio, type of cement, and stone gravel size. Minitab software was employed for its powerful statistical analysis capabilities, enabling efficient execution of fractional factorial design and response surface methods to analyse the data. The analysis revealed that the type of cement, water-cement ratio, and standard sand size significantly influenced compressive strength. The optimal conditions identified were: standard sand size of 20 mm, water-cement ratio of 0.5, BUA cement type, stone gravel size of 20 mm, and curing time of 28 days, achieving a predicted compressive strength of 44.175 MPa. The model demonstrated high reliability with an R-squared value of 99.50%. The findings offer valuable insights for enhancing the quality of building materials in the construction industry. By applying the optimized parameters, stakeholders can significantly improve structural integrity, reduce building failures, and ensure longer-lasting, safer constructions. This can lead to more durable infrastructure in Nigeria, addressing critical issues related to building safety and material performance.

Authors

Institutions

Publication Details

Journal
Asian Journal of Probability and Statistics
Published
2024-11-20
DOI
https://doi.org/10.9734/ajpas/2024/v26i11677
Citations
1
Primary Topic
Construction Engineering and Safety
Type
article
Field-Weighted Citation Impact
0.26
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimization of Concrete Mix Design Using Fractional Factorial Design and Response Surface Methodology

Ratna Musa, N.S. Dauran
1 citations
Asian Journal of Probability and Statistics
Construction Engineering and Safety
0.26
article

Optimization of Concrete Mix Design Using Fractional Factorial Design and Response Surface Methodology

Ratna Musa, N.S. Dauran
article en
1 citations

Abstract

The study addresses the critical issue of optimizing construction materials to enhance structural integrity, minimize cracks, and prevent building collapses in Nigeria. It utilizes fractional factorial design and response surface methodology (RSM) to systematically determine the optimal parameters for concrete mix design to achieve maximum compressive strength. The key factors studied included standard sand size, curing time, water-cement ratio, type of cement, and stone gravel size. Minitab software was employed for its powerful statistical analysis capabilities, enabling efficient execution of fractional factorial design and response surface methods to analyse the data. The analysis revealed that the type of cement, water-cement ratio, and standard sand size significantly influenced compressive strength. The optimal conditions identified were: standard sand size of 20 mm, water-cement ratio of 0.5, BUA cement type, stone gravel size of 20 mm, and curing time of 28 days, achieving a predicted compressive strength of 44.175 MPa. The model demonstrated high reliability with an R-squared value of 99.50%. The findings offer valuable insights for enhancing the quality of building materials in the construction industry. By applying the optimized parameters, stakeholders can significantly improve structural integrity, reduce building failures, and ensure longer-lasting, safer constructions. This can lead to more durable infrastructure in Nigeria, addressing critical issues related to building safety and material performance.

Asian Journal of Probability and StatisticsVol. 26(11)
Usmanu Danfodiyo University (NG)
Industry, innovation and infrastructure
Openalex Percentile: Top 43%
Construction Engineering and Safety
0.26
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.