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Numerous models for crack width prediction in reinforced concrete members have been proposed. These models differ in accuracy, simplicity, and the extent of empiricism. Recently, new versions of Eurocode 2 (2023) and Model Code (2020) were adopted. The current study investigates the accuracy of mean and maximum short-term crack width predictions by these models. An extended database of 1352 experimental points of 274 flexural members was collected from 21 test programs. The comparative analysis also included the design codes Eurocode 2 (2004), Model Code (2010), and the models by ACI and Oh and Kang (1987). In addition, the comparison considers the recently proposed Pure Shear Model. The latter model expressed via a simple formula that was derived analytically and includes a single empirical parameter. The statistical analysis has revealed major differences in accuracy and bias of the models considered.

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Accuracy of Crack Width Predictions for Reinforced Concrete Bending Members by State-of-the-Art Models, Including EC2 2023 and Model Code 2020

  • 会议论文

  • 发表日期：

    2025年

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  • Karolis Sakalauskas,
  • Dorian Borosnyoi-Crawley,
  • Gintaris Kaklauskas

摘要

Numerous models for crack width prediction in reinforced concrete members have been proposed. These models differ in accuracy, simplicity, and the extent of empiricism. Recently, new versions of Eurocode 2 (2023) and Model Code (2020) were adopted. The current study investigates the accuracy of mean and maximum short-term crack width predictions by these models. An extended database of 1352 experimental points of 274 flexural members was collected from 21 test programs. The comparative analysis also included the design codes Eurocode 2 (2004), Model Code (2010), and the models by ACI and Oh and Kang (1987). In addition, the comparison considers the recently proposed Pure Shear Model. The latter model expressed via a simple formula that was derived analytically and includes a single empirical parameter. The statistical analysis has revealed major differences in accuracy and bias of the models considered.