Ductile failure of structural metals is relevant to a wide range of engineering scenarios. Computational methods are employed to anticipate the critical conditions of failure, yet they sometimes provide inaccurate and misleading predictions. Challenge scenarios, such as the one presented in the current work, provide an opportunity to assess the blind, quantitative predictive ability of simulation methods against a previously unseen failure problem. Rather than evaluate the predictions of a single simulation approach, the Sandia Fracture Challenge relies on numerous volunteer teams with expertise in computational mechanics to apply a broad range of computational methods, numerical algorithms, and constitutive models to the challenge. This exercise is intended to evaluate the state of health of technologies available for failure prediction. In the first Sandia Fracture Challenge, a wide range of issues were raised in ductile failure modeling, including a lack of consistency in failure models, the importance of shear calibration data, and difficulties in quantifying the uncertainty of prediction [see Boyce et al. (Int J Fract 186:5–68, 2014) for details of these observations]. This second Sandia Fracture Challenge investigated the ductile rupture of a Ti–6Al–4V sheet under both quasi-static and modest-rate dynamic loading (failure in \(\sim \)0.1 s). Like the previous challenge, the sheet had an unusual arrangement of notches and holes that added geometric complexity and fostered a competition between tensile- and shear-dominated failure modes. The teams were asked to predict the fracture path and quantitative far-field failure metrics such as the peak force and displacement to cause crack initiation. Fourteen teams contributed blind predictions, and the experimental outcomes were quantified in three independent test labs. Additional shortcomings were revealed in this second challenge such as inconsistency in the application of appropriate boundary conditions, need for a thermomechanical treatment of the heat generation in the dynamic loading condition, and further difficulties in model calibration based on limited real-world engineering data. As with the prior challenge, this work not only documents the ‘state-of-the-art’ in computational failure prediction of ductile tearing scenarios, but also provides a detailed dataset for non-blind assessment of alternative methods.

High early strength (HES) portland cement concrete (PCC) is widely used in pavement preservation because it permits a quick reopening to traffic. However, these repairs often deteriorate faster than normal paving concrete, prompting a need to investigate the mechanisms leading to poor performance. In this study, six HES concrete mixtures with varying cement factors and accelerator dosages were subjected to isothermal calorimetry, strength testing, drying shrinkage, and various durability-related tests. The effects of the cement content and accelerator dosage on concrete strength and durability were then investigated. It is hypothesized that mixtures containing high cement contents and large dosages of accelerators will demonstrate poor performance. Testing results indicate that a target minimum compressive strength of 1,800 psi in 6 h, as required by certain state highway agencies, can be obtained using any of the studied mixtures, regardless of the cement content or accelerator dosage. Only one mixture successfully achieved all durability-related testing targets; however, only one of the mixtures catastrophically failed, and the remaining four mixtures all performed reasonably well. Thus, it cannot be concluded that HES portland cement concrete is inherently nondurable and that high cement content and accelerator dosage are incongruent with durable concrete. Possible explanations for the anecdotal poor field durability are explored.