Seismic fragility of traction elevators

Second‐generation performance‐based earthquake engineering (PBEE‐2) requires a library of component fragility functions to estimate probabilistic damage to a wide variety of building components. The present work draws on a large body of (mostly) post‐earthquake reconnaissance and (some) post‐earthquake survey observations of traction elevators to create fragility functions useful in PBEE‐2. Two surveys provide detailed observations of 115 representative elevators at 12 hospitals shaken in the 1989 Loma Prieta and 1994 Northridge earthquakes and selected without regard to or foreknowledge of damage. Of these, 55 failed and 60 did not. Approximately half were installed after an important code change of 1972, so one can distinguish the performance of pre‐1973 and post‐1973 elevator construction. They experienced a range of strong motion: 22 with peak ground acceleration (PGA) < 0.25 g, 93 with 0.25 g < PGA < 0.85 g. The hospitals had elevator failure rates as low as 0% and as high as 100%. A third survey describes damage qualitatively for six sites with PGA ≤ 0.25 and per‐site failure rates of 0% to perhaps 30%. Fragility functions are offered where the damage state is the loss of functionality of the elevator. The elevators in these surveys exhibit a median capacity of PGA ≈ 0.35 g with a logarithmic standard deviation of 0.40. Capacity is modestly sensitive to whether the elevator was installed before or after 1973. Using building‐specific intensity measures such as S_a(T₁) does not improve the fragility functions. Copyright © 2015 John Wiley & Sons, Ltd.     

Towards evaluating cloud response to climate change using clustering technique identification of cloud regimes

Most of the discrepancies in the climate sensitivity of general circulation models (GCMs) are believed to be due to differences in cloud radiative feedback. Analysis of cloud response to climate change in different ‘regimes’ may offer a more detailed understanding of how the cloud response differs between GCMs. In which case, evaluation of simulated cloud regimes against observations in terms of both their cloud properties and frequency of occurrence will assist in assessing confidence in the cloud response to climate change in a particular GCM. In this study, we use a clustering technique on International Satellite Cloud Climatology Project (ISCCP) data and on ISCCP-like diagnostics from two versions of the Hadley Centre GCM to identify cloud regimes over four different geographical regions. The two versions of the model are evaluated against observational data and their cloud response to climate change compared within the cloud regime framework. It is found that cloud clusters produced by the more recent GCM, HadSM4, compare more favourably with observations than HadSM3. In response to climate change, although the net cloud response over particular regions is often different in the two models, in several instances the same basic processes may be seen to be operating. Overall, both changes in the frequency of occurrence of cloud regimes and changes in the properties (optical depth and cloud top height) of the cloud regimes contribute to the cloud response to climate change.     

Why simulate cities?

This paper consists of three parts. After an introduction that stresses the historical progression of modeling methods, the motivation for urban modeling and simulation is explored, and the terms defined. Next, a meta-review of the literature is conducted, partially in an attempt to show that urban models resemble, and indeed share many overlapping issues with models in parallel fields such as economics and ecology. Lastly, the specific lessons learned from the author’s fifteen-year experience developing and supporting a cellular urban growth and land use change model (SLEUTH) are shared, in the interest of making these issues generic to current and future modeling and simulation efforts. The conclusion stresses that future models face new computing power, new theoretical paradigms, vastly improved ways of visualizing simulations, and a rapidly changing audience for modeling and simulation.     

Quantifying the rate and depth dependence of bioturbation based on optically‐stimulated luminescence (OSL) dates and meteoric 10Be

Both the rate and the vertical distribution of soil disturbance modify soil properties such as porosity, particle size, chemical composition and age structure; all of which play an important role in a soil's biogeochemical functioning. Whereas rates of mixing have been previously quantified, the nature of bioturbation's depth dependence remains poorly constrained. Here we constrain, for the first time, the relationship between mixing rate and depth in a bioturbated soil in northeast Queensland, Australia using a novel method combining OSL (optically‐stimulated luminescence) ages and meteoric beryllium‐10 (¹⁰Be) inventories. We find that the best fit mixing rate decreases non‐linearly with increasing soil depth in this soil and the characteristic length scale of 0.28 m over which the mixing coefficient decays is comparable to reported rooting depth coefficients. In addition we show that estimates of surface mixing rates from OSL data are highly dependent on erosion rate and that erosion rate must be constrained if accurate mixing rates are to be quantified. We calculate surface diffusion‐like mixing coefficients of 1.8 × 10^?4 and 2.1 × 10^?4 m² yr^?1 for the studied soil for two different estimates of soil erosion. © 2014 The Authors. Earth Surface Processes and Landforms Published by John Wiley & Sons Ltd.     

Multibeam sonar backscatter data processing

Multibeam sonar systems now routinely record seafloor backscatter data, which are processed into backscatter mosaics and angular responses, both of which can assist in identifying seafloor types and morphology. Those data products are obtained from the multibeam sonar raw data files through a sequence of data processing stages that follows a basic plan, but the implementation of which varies greatly between sonar systems and software. In this article, we provide a comprehensive review of this backscatter data processing chain, with a focus on the variability in the possible implementation of each processing stage. Our objective for undertaking this task is twofold: (1) to provide an overview of backscatter data processing for the consideration of the general user and (2) to provide suggestions to multibeam sonar manufacturers, software providers and the operators of these systems and software for eventually reducing the lack of control, uncertainty and variability associated with current data processing implementations and the resulting backscatter data products. One such suggestion is the adoption of a nomenclature for increasingly refined levels of processing, akin to the nomenclature adopted for satellite remote-sensing data deliverables.