![]() ![]() Because there are more weak interfaces in RAC, the cracks would generate at these interfaces with the development of the corrosion layer, thus leading to the timely release of the tensile stress in the concrete cover. It can be observed that the critical crack width at the concrete surface cracking decreases with the increase of the RA replacement percentage in the concrete mixture. In this work, the values of the critical crack widths were 0.092, 0.089, 0.064, and 0.041 mm, respectively, for the R000, R033, R067, and R100 specimens listed in Table 8.5. A nonzero crack width should be introduced when predicting the steel corrosion at surface cracking. Therefore, it is inaccurate to predict the steel corrosion at concrete surfacing cracking by allowing the surface crack width to equal 0. However, based on the statement in Sections 8.3.2 and 8.4.1, the crack width cannot be 0 at the time of concrete surface cracking it maintains the same value along the radial direction in the concrete cover. In previous studies, the crack width was normally defined as 0 when calculating the critical steel corrosion at surface cracking of the concrete cover. 8.4.2 W c: Critical Crack Width at Concrete Outer Surface Cracking Similar results were obtained for specimens R033, R067, and R100 (ie, the crack widths calculated from Eqs. This result indicates that the crack width at the moment of concrete surface cracking, W c, remains constant along the radial direction at the moment of concrete surface cracking. (8.6) and (8.7), 0.092 mm is calculated from both equations. Substituting the critical corrosion layer thickness at the surface cracks of the concrete cover (ie, 0.0316 mm for R000) into Eqs. Therefore, the equation which expresses the forecast model (95% confidence) can be expressed as follows: ![]() On the basis of the data available, the latter limits will in turn make the OCRA multiplier factor (4.2) oscillate between 3.2 (minimum value) and 5.2 (maximum value). If the regression equation shown previously is being used as a predictive model (in this way the OCRA index becomes a forecast index of collective risk for a given exposed population to contract WMSDs) the confidence limits (95%) within which the forecast may oscillate must be considered. ![]() This datum is obviously different from the alternative one which is used: the prevalence of individuals affected by WMSDs (one or more). exposed individuals stands for the prevalence of single upper limb occupational pathologies calculated on the number of exposed individuals. This regression equation is calculated without the constant (e.g., if OCRA is 0, then there are no WMSDs), and starting from the data examined until this moment, it has an R 2 of 0.89, and extremely high statistical significance (p < 0.00001). Where : Y = n ° ⋅ WMSDs n ° ⋅ exposed individuals ⋅ 100 X = OCRA index ![]()
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