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The overdesign of concrete mixtures and substandard concrete acceptance testing practices significantly impact the concrete industry's role in sustainable construction. This study evaluates the impact of overdesign on the sustainability of concrete and embodied carbon emissions at the national and project scales. In addition, this paper reviews quality results from a concrete producer survey; established industry standards and their role in acceptance testing in the building codes; the reliance on proper acceptance testing by the licensed design professional, building code official, and the project owner; and the carbon footprints that result from overdesign of concrete mixtures. In 2020, a field survey conducted on over 100 projects documented Pennsylvania's quality of field testing. Of those surveyed, only 15% of the projects met the testing criteria within the ASTM and building code requirements. As a result, the total overdesign-induced cement consumption is as large as 6.7% of the estimated cement used in the United States. [ABSTRACT FROM AUTHOR]

Purpose: Several efforts have attempted to incorporate the sources of uncertainty and variability into the life cycle assessment (LCA) of pavements. However, no method has been proposed that can simultaneously consider data quality, methodological choices, and variability in inputs and outputs without the need for complementary software. This study aims to develop and implement a flexible method that can be used in the LCA software to assess the effects of these sources on the conclusions.Methods: A Monte Carlo analysis was conducted and applied in a comparative LCA of pavements to assess the preferred scenario. The uncertainty of the results was first estimated by considering the data quality using the ecoinvent database. Moreover, the variabilities of the materials, construction methods, and repair stages of the pavement life cycle were included in the analysis by assigning continuous uniform probability distributions to each variable. Ultimately, the probability of methodological choices was modeled using uniform distributions and assigning a portion of the area of the distribution to each scenario. The individual and combined effects of these uncertainty and variability sources were assessed in a comparative LCA of asphalt and concrete pavements in a cold region such as Quebec (Canada).Results and discussion: The results of the Monte Carlo analysis show that the allocation choices can change the environmentally preferred scenario in four midpoint categories. These categories are significantly dominated by the crude oil supply chain. The variability in construction materials and methods can change the preferred scenario in the damage categories, namely, human health and global warming. Additionally, parameter uncertainty has a significant effect on the conclusion of the preferred scenario in ecosystem quality. The worst qualitative scores are given to the geographical uncertainty of the elementary flow that primarily contributes to this category (i.e., zinc). The simultaneous effect of the uncertainty and variability sources prevents the decision-maker from reaching a less uncertain conclusion about ecosystem quality, human health, and global warming effects.Conclusions: This study demonstrates that it is feasible to assess the cumulative effects of common uncertainty and variability sources using commercial LCA software, including Monte Carlo simulation. Based on the variability and uncertainty of the results, the identification of a certain conclusion is case specific at both the midpoint and endpoint levels. Increasing the quality of the inventory is one solution to decreasing the uncertainties related to human health, ecosystem quality, and global warming regarding pavement LCA. This improvement can be achieved by avoiding the adaptation of a similar process to match the considered process and using practical construction efficiencies and realistic construction materials. The effectiveness of these tasks must be assessed in future studies. It should be noted that these conclusions were determined regardless of the uncertainty in the characterization factors of the impact assessment method. [ABSTRACT FROM AUTHOR]