This paper presents the experimental results of steel plates strengthened with carbon fiber reinforced polymer (CFRP) sheets under tensile load. The number of CFRP layers (ranging from one to four), strengthening schemes (single-sided and double-sided bonding), and temperatures (ranging from 25 to 120 °C) were investigated. Results showed that the number of CFRP layers and strengthening schemes had insignificant effects on failure modes of specimens. The failure modes were dominated by the degradation of resin matrix at temperatures lower than T_g + 10 or 20 °C, where T_g is the glass transition temperature, and were dominated by the volume decrease of resin matrix at temperatures above that. Through bonding CFRP sheets, the ultimate load and post-elastic stiffness of specimens were significantly increased. However, the increase in the number of CFRP layers also led to the decrease in strengthening and stiffening efficiency. The double-sided strengthened specimens showed more preferable mechanical properties than the single-sided strengthened specimens. As temperature increased, significant decreases in ultimate load and post-elastic stiffness were observed. Analytical modeling to predict the mechanical properties at ambient and elevated temperatures were conducted, respectively. The modeling results were verified by the test data.

Current guidelines stipulate a sample size of five for a tensile coupon test of fiber reinforced polymer (FRP) composites based on the assumption of a normal distribution and a sample coefficient of variation (COV) of 0.058. Increasing studies have validated that a Weibull distribution is more appropriate in characterizing the tensile properties of FRP. However, few efforts have been devoted to sample size evaluation based on a Weibull distribution. It is not clear if the Weibull distribution will result in a more conservative sample size value. In addition, the COV of FRP’s properties can vary from 5% to 15% in practice. In this study, the sample size based on a two-parameter Weibull distribution is compared with that based on a normal distribution. It is revealed that the Weibull distribution results in almost the same sample size as the normal distribution, which means that the sample size based on a normal distribution is applicable. For coupons with COVs varying from 0.05 to 0.20, the sample sizes range from less than 10 to more than 60. The use of only five coupons will lead to a prediction error of material property between 6.2% and 24.8% for COVs varying from 0.05 to 0.20.