Methodology of CO2 emission evaluation in the life cycle of office building facades

The construction industry is one of the greatest sources of pollution because of the high level of energy consumption during its life cycle. In addition to using energy while constructing a building, several systems also use power while the building is operating, especially the air-conditioning system. Energy consumption for this system is related, among other issues, to external air temperature and the required internal temperature of the building. The facades are elements which present the highest level of ambient heat transfer from the outside to the inside of tall buildings. Thus, the type of facade has an influence on energy consumption during the building life cycle and, consequently, contributes to buildings' CO2 emissions, because these emissions are directly connected to energy consumption. Therefore, the aim is to help develop a methodology for evaluating CO2 emissions generated during the life cycle of office building facades. The results, based on the parameters used in this study, show that facades using structural glazing and uncolored glass emit the most CO2 throughout their life cycle, followed by brick facades covered with compound aluminum panels or ACM (Aluminum Composite Material), facades using structural glazing and reflective glass and brick facades with plaster coating. On the other hand, the typology of facade that emits less CO2 is brickwork and mortar because its thermal barrier is better than structural glazing facade and materials used to produce this facade are better than brickwork and ACM. Finally, an uncertainty analysis was conducted to verify the accuracy of the results attained. (C) 2011 Elsevier Inc. All rights reserved.

Fuzzy linguistic model for bioelectrical impedance vector analysis

Backgrounds Ea aims: The boundaries between the categories of body composition provided by vectorial analysis of bioimpedance are not well defined. In this paper, fuzzy sets theory was used for modeling such uncertainty. Methods: An Italian database with 179 cases 18-70 years was divided randomly into developing (n = 20) and testing samples (n = 159). From the 159 registries of the testing sample, 99 contributed with unequivocal diagnosis. Resistance/height and reactance/height were the input variables in the model. Output variables were the seven categories of body composition of vectorial analysis. For each case the linguistic model estimated the membership degree of each impedance category. To compare such results to the previously established diagnoses Kappa statistics was used. This demanded singling out one among the output set of seven categories of membership degrees. This procedure (defuzzification rule) established that the category with the highest membership degree should be the most likely category for the case. Results: The fuzzy model showed a good fit to the development sample. Excellent agreement was achieved between the defuzzified impedance diagnoses and the clinical diagnoses in the testing sample (Kappa = 0.85, p < 0.001). Conclusions: fuzzy linguistic model was found in good agreement with clinical diagnoses. If the whole model output is considered, information on to which extent each BIVA category is present does better advise clinical practice with an enlarged nosological framework and diverse therapeutic strategies. (C) 2012 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

A comparison of deterministic, reliability-based and risk-based structural optimization under uncertainty

In this paper, the effects of uncertainty and expected costs of failure on optimum structural design are investigated, by comparing three distinct formulations of structural optimization problems. Deterministic Design Optimization (DDO) allows one the find the shape or configuration of a structure that is optimum in terms of mechanics, but the formulation grossly neglects parameter uncertainty and its effects on structural safety. Reliability-based Design Optimization (RBDO) has emerged as an alternative to properly model the safety-under-uncertainty part of the problem. With RBDO, one can ensure that a minimum (and measurable) level of safety is achieved by the optimum structure. However, results are dependent on the failure probabilities used as constraints in the analysis. Risk optimization (RO) increases the scope of the problem by addressing the compromising goals of economy and safety. This is accomplished by quantifying the monetary consequences of failure, as well as the costs associated with construction, operation and maintenance. RO yields the optimum topology and the optimum point of balance between economy and safety. Results are compared for some example problems. The broader RO solution is found first, and optimum results are used as constraints in DDO and RBDO. Results show that even when optimum safety coefficients are used as constraints in DDO, the formulation leads to configurations which respect these design constraints, reduce manufacturing costs but increase total expected costs (including expected costs of failure). When (optimum) system failure probability is used as a constraint in RBDO, this solution also reduces manufacturing costs but by increasing total expected costs. This happens when the costs associated with different failure modes are distinct. Hence, a general equivalence between the formulations cannot be established. Optimum structural design considering expected costs of failure cannot be controlled solely by safety factors nor by failure probability constraints, but will depend on actual structural configuration. (c) 2011 Elsevier Ltd. All rights reserved.

Overview of the South American Biomass burning analysis (SAMBBA) field experiment.

Biomass burning represents one of the largest sources of particulate matter to the atmosphere, which results in a significant perturbation to the Earth’s radiative balance coupled with serious negative impacts on public health. Globally, biomass burning aerosols are thought to exert a small warming effect of 0.03 Wm-2, however the uncertainty is 4 times greater than the central estimate. On regional scales, the impact is substantially greater, particularly in areas such as the Amazon Basin where large, intense and frequent burning occurs on an annual basis for several months (usually from August-October). Furthermore, a growing number of people live within the Amazon region, which means that they are subject to the deleterious effects on their health from exposure to substantial volumes of polluted air. Initial results from the South American Biomass Burning Analysis (SAMBBA) field experiment, which took place during September and October 2012 over Brazil, are presented here. A suite of instrumentation was flown on-board the UK Facility for Airborne Atmospheric Measurement (FAAM) BAe-146 research aircraft and was supported by ground based measurements, with extensive measurements made in Porto Velho, Rondonia. The aircraft sampled a range of conditions with sampling of fresh biomass burning plumes, regional haze and elevated biomass burning layers within the free troposphere. The physical, chemical and optical properties of the aerosols across the region will be characterized in order to establish the impact of biomass burning on regional air quality, weather and climate.