Effects of Elevated Temperatures and Fire on Bond Strength of Prestressed Steel and Carbon FRP Bars in High Performance Self-Consolidating Concrete

Novel structures are emerging utilizing high performance, self-consolidating, fibre-reinforced concrete (HPSCC) reinforced with high-strength, lightweight, and non-corroding prestressed reinforcement. One example of this is a new type of precast carbon fibre reinforced polymer (CFRP) pretensioned HPSCC panel intended as load-bearing panels for building envelopes. As for all load-bearing structural members in building applications, the performance of these members in fire must be understood before they can be used with confidence. In particular, the bond performance of CFRP prestressing reinforcement at elevated temperatures is not well known. This paper examines the fire performance of these new types of structural elements, placing particular emphasis on the bond performance of CFRP and steel wire prestressing reinforcement at elevated temperatures. The results of large scale fire tests and bond-pullout tests on CFRP and steel prestressing bars embedded in HPSCC cylinders are presented and discussed to shed light on the fire performance of these structural elements. An analytical model is proposed to determine the temperature at which first appearance of the cracking phenomenon and concrete cover failure occurred, in the large scale fire tests, due to incompatibility of thermal expansion between CFRP tendons and HPSCC. From this research it is proposed that temperatures in range of the glass transition temperature (Tg) of the CFRP tendon’s epoxy matrix are critical for the bond strength capacity of the CFRP tendons in reinforced or pretensioned concrete members

Earth observation-based disaggregation of exposure data for earthquake loss modelling

We use TanDEM-X and Sentinel-2 observations to disaggregate earthquake risk-related exposure data. We use the refined exposure data and model earthquake loss. Results for the city of Santiago de Chile show that earthquake risk has been underestimated before due to aggregated exposure data

Earth observation-based disaggregation of exposure data for earthquake loss modeling

We use TanDEM-X and Sentinel-2 observations to disaggregate earthquake risk-related exposure data. We use the refined exposure data and model earthquake loss. Results for the city of Santiago de Chile show that earthquake risk has been underestimated before due to aggregated exposure data

Benefits of global earth observation missions for disaggregation of exposure data and earthquake loss modeling: evidence from Santiago de Chile

Exposure is an essential component of risk models and describes elements that are endangered by a hazard and susceptible to damage. The associated vulnerability characterizes the likelihood of experiencing damage (which can translate into losses) at a certain level of hazard intensity. Frequently, the compilation of exposure information is the costliest component (in terms of time and labor) of risk assessment procedures. Existing models often describe exposure in an aggregated manner, e.g., by relying on statistical/census data for given administrative entities. Nowadays, earth observation techniques allow the collection of spatially continuous information for large geographic areas while enabling a high geometric and temporal resolution. Consequently, we exploit measurements from the earth observation missions TanDEM-X and Sentinel-2, which collect data on a global scale, to characterize the built environment in terms of constituting morphologic properties, namely built-up density and height. Subsequently, we use this information to constrain existing exposure data in a spatial disaggregation approach. Thereby, we establish dasymetric methods for disaggregation. The results are presented for the city of Santiago de Chile, which is prone to natural hazards such as earthquakes. We present loss estimations due to seismic ground shaking and corresponding sensitivity as a function of the resolution properties of the exposure data used in the model. The experimental results underline the benefits of deploying modern earth observation technologies for refined exposure mapping and related earthquake loss estimation with enhanced accuracy properties

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake

Behavior of hollow, rectangular concrete piers subjected to biaxial bending

textCivil, Architectural, and Environmental Engineerin

Automated building characterization for seismic risk assessment using street-level imagery and deep learning

Accurate seismic risk modeling requires knowledge of key structural characteristics of buildings. However, to date, the collection of such data is highly expensive in terms of labor, time and money and thus prohibitive for a spatially continuous large-area monitoring. This study quantitatively evaluates the potential of an automated and thus more efficient collection of vulnerability-related structural building characteristics based on Deep Con-volutional Neural Networks (DCNNs) and street-level imagery such as provided by Google Street View. The proposed approach involves a tailored hierarchical categorization workflow to structure the highly heteroge-neous street-level imagery in an application-oriented fashion. Thereupon, we use state-of-the-art DCNNs to explore the automated inference of Seismic Building Structural Types. These reflect the main-load bearing structure of a building, and thus its resistance to seismic forces. Additionally, we assess the independent retrieval of two key building structural parameters, i.e., the material of the lateral-load-resisting system and building height to investigate the applicability for a more generic structural characterization of buildings. Experimental results obtained for the earthquake-prone Chilean capital Santiago show accuracies beyond κ =0.81 for all addressed classification tasks. This underlines the potential of the proposed methodology for an efficient in-situ data collection on large spatial scales with the purpose of risk assessments related to earthquakes, but also other natural hazards (e.g., tsunamis, or floods)

Street-Level Imagery and Deep Learning for Characterization of Exposed Buildings

Knowledge on the key structural characteristics of exposed buildings is crucial for accurate risk modeling with regard to natural hazards. In risk assessment this information is used to interlink exposed buildings with specific representative vulnerability models and is thus a prerequisite to implement sound risk models. The acquisition of such data by conventional building surveys is usually highly expensive in terms of labor, time, and money. Institutional data bases such as census or tax assessor data provide alternative sources of information. Such data, however, are often inappropriate, out-of-date, or not available. Today, the large-area availability of systematically collected street-level data due to global initiatives such as Google Street View, among others, offers new possibilities for the collection of in-situ data. At the same time, developments in machine learning and computer vision – in deep learning in particular – show high accuracy in solving perceptual tasks in the image domain. Thereon, we explore the potential of an automatized and thus efficient collection of vulnerability related building characteristics. To this end, we elaborated a workflow where the inference of building characteristics (e.g., the seismic building structural type, the material of the lateral load resisting system or the building height) from geotagged street-level imagery is tasked to a custom-trained Deep Convolutional Neural Network. The approach is applied and evaluated for the earthquake-prone Chilean capital Santiago de Chile. Experimental results are presented and show high accuracy in the derivation of addressed target variables. This emphasizes the potential of the proposed methodology to contribute to large-area collection of in-situ information on exposed buildings