Development and assessment of damage‐to‐loss models for moment‐frame reinforced concrete buildings

The assessment of earthquake loss often requires the definition of a relation between a measure of damage and a quantity of loss, usually achieved through the employment of a damage‐to‐loss model. These models are frequently characterized by a large variability, which inevitably increases the uncertainty in the vulnerability assessment and earthquake loss estimation. This study provides an insight on the development of damage‐to‐loss functions for moment‐frame reinforced concrete buildings through an analytical methodology. Tri‐dimensional finite element models of existing reinforced concrete buildings were subjected to a number of ground motion records compatible with the seismicity in the region of interest, through nonlinear dynamic analysis. These results were used to assess, for a number of damage states, the probability distribution of loss ratio, taking into consideration member damage and different repair techniques, as well as to derive sets of fragility functions. Then, a vulnerability model (in terms of the ratio of cost of repair to cost of replacement, conditional on the level of ground shaking intensity) was derived and compared with the vulnerability functions obtained through the combination of various damage‐to‐loss models with the set of fragility functions developed herein. In order to provide realistic estimates of economic losses due to seismic action, a comprehensive study on repair costs using current Portuguese market values was also carried out. The results of this study highlight important issues in the derivation of vulnerability functions, which are a fundamental component for an adequate seismic risk assessment. © 2015 The Authors. Earthquake Engineering & Structural Dynamics published by John Wiley & Sons Ltd.     

A relocatable ocean model in support of environmental emergencies

During the Costa Concordia emergency case, regional, subregional, and relocatable ocean models have been used together with the oil spill model, MEDSLIK-II, to provide ocean currents forecasts, possible oil spill scenarios, and drifters trajectories simulations. The models results together with the evaluation of their performances are presented in this paper. In particular, we focused this work on the implementation of the Interactive Relocatable Nested Ocean Model (IRENOM), based on the Harvard Ocean Prediction System (HOPS), for the Costa Concordia emergency and on its validation using drifters released in the area of the accident. It is shown that thanks to the capability of improving easily and quickly its configuration, the IRENOM results are of greater accuracy than the results achieved using regional or subregional model products. The model topography, and to the initialization procedures, and the horizontal resolution are the key model settings to be configured. Furthermore, the IRENOM currents and the MEDSLIK-II simulated trajectories showed to be sensitive to the spatial resolution of the meteorological fields used, providing higher prediction skills with higher resolution wind forcing.     

K-feldspar IRSL dating of a Pleistocene river terrace staircase sequence of the Lower Tejo River (Portugal,western Iberia)

We present the results of K-feldspar IRSL dating of the four lower terraces (T3–T6) of the Portuguese Tejo River, in the Arripiado-Chamusca area. Terrace correlation was based upon: a) analysis of aerial photographs, geomorphological mapping and field topographic survey; b) sedimentology of the deposits; and c) luminescence dating. Sediment sampled for luminescence dating gave unusually high dose rates, of between 3.4 and 6.2 Gy/ka and, as a result, quartz OSL was often found to be in saturation. We therefore used the IRSL signal from K-feldspar as the principal luminescence technique. The K-feldspar age results support sometimes complex geomorphic correlations, as fluvial terraces have been vertically displaced by faults (known from previous studies). Integration of these new ages with those obtained previously in the more upstream reaches of the Tejo River in Portugal indicates that the corrected K-feldspar IRSL ages are stratigraphically and geomorphologically consistent over a distance of 120 km along the Tejo valley. However, we are sceptical of the accuracy of the K-feldspar ages of samples from the T3 and T4 terraces (with uncorrected D_e values >500 Gy). In these cases the Dose Rate Correction (DRC) model puts the natural signals close to luminescence saturation, giving a minimum corrected D_e of about 1000 Gy, and thus minimum terrace ages; this may even be true for those doses >200 Gy. Luminescence dating results suggest that: T3 is older than 300 ka, probably ca. 420–360 ka (～Marine Isotope Stage [MIS]11); T4 is ca. 340–150 ka (～MIS9-6); T5 is 136–75 ka (～MIS5); T6 is 60–30 ka (MIS3); an aeolian sand unit that blankets T6 and some of the older terraces is 30–≥12 ka. Collectively, the luminescence ages seem to indicate that regional river downcutting events may be coincident with periods of low sea level (associated, respectively, with the MIS10, MIS6, MIS4 and MIS2).     

Benthic biotope index for classifying habitats in the Sado Estuary: Portugal

An integration of sediment physical, chemical, biological, and toxicity data is necessary for a meaningful interpretation of the complex sediment conditions in the marine environment. Assessment of benthic community is a vital component for that interpretation, yet their evaluation is complex and requires a large expenditure of time and funds. Thus, there is a need for new tools that are less expensive and more understandable for managers. This paper presents a benthic biotope index to predict from physical and chemical variables the occurrence of macrobenthic habitats. Parameters such as sediment type, organic matter, depth, and hydrodynamic parameters were selected, through a discriminant analysis, to compute the index. Other authors have used multivariate methods to determine the benthic biotopes for Sado Estuary. The index proved to be a valid tool to classify and assess the spatial patterns of benthic habitat and to synthesize stress biotope gradients.     

The North Atlantic nutrient stream

Western boundary currents are the locus of intense nutrient transport, or nutrient streams. The largest fraction of this transport takes place in the upper-thermocline layers, between the surface layers (where speed reaches a maximum) and the nutrient bearing strata of the subtropical gyres (where nutrient concentration is maximum). The core of the nutrient stream of the North Atlantic subtropical gyre is located slightly offshore the Gulf Stream, its density coordinate centered on the 26.5–27.3_–band, approximately constant along the axis of the stream. During late spring and summer the nutrient stream reaches the surface seasonal mixed layer at the outcropping of this isopycnal band. We argue that this must be a principal factor sustaining the seasonal high productivity of the subpolar North Atlantic Ocean. Additionally, we investigate the possibility of intermittent shear-induced diapycnal mixing in the upper-thermocline layers of the Gulf Stream, induced by frontogenesis taking place during some phase of the meanders. Here we illustrate that diapycnal mixing has a maximum at the location of the nutrient stream, being associated to observed nutrient anomalies. We suggest that diapycnal mixing associated to the passage of steep meanders brings nutrients from the nutrient stream to the shallow photic layers, and sustains intermittent (day-to-week) patchy (10–100 km) productivity over the stream itself.     

Using Empirical Mode Decomposition to Filter Out Non-turbulent Contributions to Air–Sea Fluxes

A methodology based on Empirical mode decomposition (EMD) was used to filter out non-turbulent motions from measurements of atmospheric turbulence over the sea, aimed at reducing their contribution to eddy-covariance (EC) estimates of turbulent fluxes. The proposed methodology has two main objectives: (1) to provide more robust estimates of the fluxes of momentum, heat and CO\(_2\); and (2) to reduce the number of flux intervals rejected due to non-stationarity criteria when using traditional EC data processing techniques. The method was applied to measurements from a 28-day cruise (HALOCAST 2010) in the Eastern Pacific region. Empirical mode decomposition was applied to 4-h long time series data and used to determine the cospectral gap time scale, \(T_\mathrm{{gap}}\). Intrinsic modes of oscillation with characteristic periods longer than the gap scale due to non-turbulent motions were assumed and filtered out. Turbulent fluxes were then calculated for sub-intervals of length \(T_\mathrm{{gap}}\) from the filtered 4-h time series. In the HALOCAST data, the gap scale was successfully identified in 89% of the 4-h periods and had a mean of 37 s. The EMD approach resulted in the rejection of 11% of the flux intervals, which was much less than the 68% rejected when using standard filtering methods based on data non-stationarity. For momentum and sensible heat fluxes, the averaged difference in flux magnitude between the traditional and EMD approaches was small (3 and 1%, respectively). For the CO\(_2\) flux, the magnitude of EMD flux estimates was on average 16% less than fluxes estimated from linear detrended 10-min time series. These results provide evidence that the EMD method can be used to reduce the effects of non-turbulent correlations from flux estimates.     

Identification of the affected areas by mass movement through a physically based model of landslide hazard combined with an empirical model of debris flow

In tropical areas, mass movements are common phenomena, especially during periods of heavy rainfall, which frequently take place in the summer season. These phenomena have caused loss of life and serious damage to infrastructure and properties. The most prominent of these phenomena are landslides that can produce debris flows. Thus, this article aims at determining affected areas using a model to predict landslide prone areas (SHALSTAB) combined with an empirical model designed to define the debris flow travel distance and area of deposition. The methodology of this work consists of the following steps: (a) elaboration of a digital elevation model (DEM), (b) application of the deterministic SHALSTAB model to locate the landslide prone areas, (c) identification of the debris flow travel distance and area of deposition, and (d) mapping of the affected areas (landslides and debris flows). This work was developed in an area in which many mass movements occurred after intense rainfall during the summer season (February 1996) in the state of Rio de Janeiro, southeast Brazil. All of the scars produced by that event were mapped, allowing for validation of the applied models. The model results show that the mapped landslide locations can adequately be simulated by the model.     

Toponym matching through deep neural networks

Toponym matching, i.e. pairing strings that represent the same real-world location, is a fundamental problemfor several practical applications. The current state-of-the-art relies on string similarity metrics, either specifically developed for matching place names or integrated within methods that combine multiple metrics. However, these methods all rely on common sub-strings in order to establish similarity, and they do not effectively capture the character replacements involved in toponym changes due to transliterations or to changes in language and culture over time. In this article, we present a novel matching approach, leveraging a deep neural network to classify pairs of toponyms as either matching or nonmatching. The proposed network architecture uses recurrent nodes to build representations from the sequences of bytes that correspond to the strings that are to be matched. These representations are then combined and passed to feed-forward nodes, finally leading to a classification decision. We present the results of a wide-ranging evaluation on the performance of the proposed method, using a large dataset collected from the GeoNames gazetteer. These results show that the proposed method can significantly outperform individual similarity metrics from previous studies, as well as previous methods based on supervised machine learning for combining multiple metrics.     

Concentration of Ciprofloxacin in Brazilian Hospital Effluent and Preliminary Risk Assessment: A Case Study

This study was carried out to evaluate the occurrence of the fluoroquinolone antimicrobial agent ciprofloxacin (CIP) in the effluent of the Hospital of the Federal University of Santa Maria (HUSM). Measured environmental concentrations (MECs) of CIP in the hospital wastewater, both before (P1) and after (P2) cesspit/filter system treatment (CFTS), were determined by means of solid phase extraction and reversed‐phase liquid chromatography with fluorescence detection (LC‐FLD) and reversed‐phase liquid chromatography with mass spectrometric detection (LC‐MS/MS). The MECs (n = 7 daily composed samples) were 19 to 155 μg L^–1 (average: 54 ± 21 μg L^–1) and 32 to 99 μg L^–1 (average: 65 ± 45 μg L^–1) in P1 and P2, respectively. No relevant removal was observed from P1 to P2. In a worst case scenario, the final effluent was regarded as MECs of surface water. These MECs were generally 5 to 20,000‐fold higher than what was previously known. If the present data is drawn on to form a model of the situation in developing countries, the picture provides a first rough indication that the environmental risk associated with the use and emission of pharmaceuticals into the environment in developing countries might be higher than in developed countries.