Concrete pavements are essential to modern infrastructure, but their low tensile and flexural strengths can cause cracking and shrinkage. This study evaluates fiber reinforcement with steel and carbon fibers in various combinations to improve rigid pavement performance. Six concrete mixes were tested: a control mix with no fiber, a mix with 1% steel fiber (SF1%), a mix with 1% carbon fiber (CF1%), and three hybrid mixes with 1% fiber content: 0.75% steel /0.25% carbon fiber (SF0.75CF0.25), 0.25% steel /0.75% carbon fiber (SF0.25CF0.75), and 0.5% steel /0.5% carbon fiber ((SF0.5CF0.5). Laboratory experiments including compressive, flexural, and splitting tensile strength tests were conducted at 7, 28, and 90 days, while Finite Element Analysis (FEA) using ABAQUS software was developed to examine pavement behavior under repeated loading. The results revealed that at 90 days, the SF1% mix exhibited a 9.1% improved compressive strength and CF1% mix a 7.3% improved strength over the control mix. The SF1% mix increased flexural strength by 72.5% and the CF1% mix by 48.6%. Additionally, splitting tensile strength increased by 70% for the SF1% and 45.5% for the CF1%. The hybrid mixes improved compressive strength by 7.6%-8.5%, flexural strength by 59.7%-70.2%, and splitting tensile strength by 56%-67.8%. The finite element modeling showed that the control mix was displaced 15 mm under repeated loading, while the SF1% reduced displacement by 35% and the hybrid mixes by 30%. These findings indicated that SF1% exhibited the best mechanical properties. However, fiber reinforcement, whether used single or in hybrid combinations, improves concrete pavement mechanical performance and loading behavior, offering a promising way to infrastructure durability and service life.
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... Show MoreMultiple linear regressions are concerned with studying and analyzing the relationship between the dependent variable and a set of explanatory variables. From this relationship the values of variables are predicted. In this paper the multiple linear regression model and three covariates were studied in the presence of the problem of auto-correlation of errors when the random error distributed the distribution of exponential. Three methods were compared (general least squares, M robust, and Laplace robust method). We have employed the simulation studies and calculated the statistical standard mean squares error with sample sizes (15, 30, 60, 100). Further we applied the best method on the real experiment data representing the varieties of
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