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Dynamic Web Twain 6 1 Cracking
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Officials admit that \"dynamic zero COVID\" involves some inconvenience and pain, but argue that dropping it would cause even worse trouble. That doesn't explain why the authorities failed to shift away from it earlier, or make use of the time it bought them to increase vaccination rates and prepare for the eventual reopening of the country.
Schematic illustration of rigorous risk assessment for a single structure and a defined response condition or limit state; a for each earthquake scenario, a suite of accelerograms is generated and used in dynamic analyses of a structural model, and b the results used to determine the rate at which damage occurs (Bommer and Crowley 2017)
The presence of layers of different stiffness in the near-surface site profile can have a profound effect on the surface motions, hence incorporating such local amplification effects is essential in any site-specific seismic hazard assessment. As noted in sub-Sect. 2.2.3, modern ground-motion prediction models always include a term for site amplification, usually expressed in terms of VS30. For an empirically constrained site amplification term, the frequency and amplitude characteristics of the VS30-dependence will correspond to an average site amplification of the recording sites contributing to the database from which the GMM was derived. The amplification factors for individual sites may differ appreciably from this average site effect as a result of different layering in the uppermost 30 m and to differences in the VS profiles at greater depth (Fig. 46). For a site-specific PSHA, therefore, it would be difficult to defend reliance on the generic amplification factors in the GMM or GMMs adopted for the study, even if this also include additional parameters such as Z1.0 or Z2.5. Site amplification effects can be modelled using measured site profiles and this is the only component of a GMC model for which the collection of new data to provide better constraint and to reduce epistemic uncertainty does not depend on the occurrence of new earthquakes. Borehole and non-invasive techniques can be used to measure VS profiles at the site and such measurements should be considered an indispensable part of any site-specific PSHA, as should site response analyses to determine the dynamic effect of the near-surface layers at the site.
An issue that was not always clearly recognised in this approach was the need to also capture correctly the AF associated with the VS profile below the rock horizon at which the hazard is calculated and where the dynamic inputs to the site response calculations are defined. If the site-specific VS profile is appreciably different from the profile implicit in the GMM used to predict the rock motions, there is an inconsistency for which an adjustment should be made (Williams and Abrahamson 2021; Fig. 47). In a number of site-specific PSHA studies, this has been addressed by making an adjustment for differences between both the GMM and target VS profiles and between the damping associated with these profiles, in order to obtain the rock hazard, before convolving this with the AFs obtained from SRA for the overlying layers. Such host-to-target VS-\\(\\kappa \\) adjustments (e.g., Al Atik et al. 2014) became part of standard practice in site-specific PSHA studies, especially at nuclear sites (e.g., Biro and Renault 2012; PNNL 2014; Bommer et al. 2015b; Tromans et al. 2019). The scheme for including such adjustments to obtain hazard estimates calibrated to the target rock profile and then convolving the rock hazard with the AFs for overlying layers is illustrated in Fig. 48.
From a logistical point of view, the Level 4 process is rather cumbersome and Level 3 studies have been shown to be considerably more agile. Moreover, the role of TFI is exceptionally demanding, considerably more so than that of the TI Leads or even the PTI in a Level 3 study. In my view, the Level 3 process offers two very significant advantages over Level 4, in addition to the points just noted. Firstly, if the final logic tree in a Level 4 is generated by simply combining the logic trees of the individual evaluator experts, then it can become enormous: in the PEGASOS project, the total number of branch combinations in the full logic tree was on the order of 1026. Such wildly dendritic logic trees pose enormous challenges from a computational perspective, but their size does not mean that they are more effectively capturing the epistemic uncertainty. Indeed, such an unwieldy model probably makes it more difficult to visualise the resulting distributions and inevitably limits the options for performing sensitivity analyses that can provide very valuable insights. The second advantage of Level 3 studies is the heightened degree of interaction among the evaluator experts. In a Level 4 study, there is ample opportunity for interaction among the experts including questions and technical challenges, but ultimately each expert is likely to feel responsibility for her or his own model, leaving the burden of robust technical challenge to the TFI. In a Level 3 study, where the experts are charged to collectively construct a model that they are all prepared to assume ownership of and to defend, the process of technical challenge and defence is envigorated. Provided the interactions among the experts take place in an environment of mutual respect and without dominance by any individual, the animated exchanges and lively debates that will usually ensue can add great value to the process. In this regard, however, it is important to populate the TI Teams with individuals with diverse viewpoints who are prepared to openly debate the technical issues to be resolved during the course of the project. If the majority of the TI Team members are selected from a single organisation, for example, this can result in a less dynamic process of technical challenge and defence, especially if one of the TI Team members, or indeed the TI Lead, is senior to the others within their organisation.
A question that often arises when undertaking a PSHA, is whether there is a way to ascertain that sufficient epistemic uncertainty has been captured. The required range of epistemic uncertainty cannot be measured, since the range of the epistemic uncertainty, by definition, lies beyond the available data. For individual components of the hazard input models, comparisons may be made with the epistemic uncertainty in other models. For example, for the GMC model, one might look at the range of epistemic uncertainty in the NGA-West models, as measured by the model-to-model variability (rather than their range of predicted values), and then make the inference that since these models were derived from a data-rich region, their uncertainty range should define the lower bound on uncertainty for the target region. However, there are many reasons why such an approach may not be straightforward. Firstly, the uncertainty defined by the NGA-West2 GMMs displays a trend of decreasing in the magnitude ranges where the data are sparser, although this is improved with application of the Al Atik and Youngs (2014) additional uncertainty penalty (Fig. 71). Secondly, the site-specific PSHA might be focused on a region that is much smaller than the state of California for which the NGA-West2 models were developed (using a dataset dominated by other regions in the upper range of magnitudes). The dynamic characterisation of the target site is also likely to be considerably better constrained than the site conditions at the recording stations contributing to the NGA-West2 database, for which just over half have VS30 values inferred from proxies rather than measured directly (Seyhan et al. 2014).
The vast diversity of landscapes found on Earth results from interplay between processes that break rock down, produce mobile regolith, and transport materials away. Mechanical weathering is fundamental to shaping landscapes, yet it is perhaps less understood at a mechanistic level than chemical weathering. Ubiquitous microfractures in rock propagate and grow through a slow process known as subcritical cracking that operates at the low applied stresses common in the near-surface. Subcritical cracking is the most likely explanation for the mechanical processes associated with thermal stress, ice lens growth, mineral alteration, and root growth. The long timescales over which critical zone architectures develop require an understanding of slow processes, such as subcritical cracking. 153554b96e
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