Anomaly diagnosis in ceramic claddings by thermography - A review

With the increasing importance given to building rehabilitation comes the need to create simple, fast and non-destructive testing methods (NDT) to identify problems and for anomaly diagnosis. Ceramic tiles are one of the most typical kinds of exterior wall cladding in several countries; the earliest known examples are Egyptian dating from 4000 BC. This type of building facade coating, though being quite often used in due to its aesthetic and architectural characteristics, is one of the most complex that can be applied given the several parts from which it is composed; hence, it is also one of the most difficult to correctly diagnose with expeditious methods. The detachment of ceramic wall tiles is probably the most common and difficult to identify anomaly associated with this kind of cladding and it is also definitely the one that can compromise security the most. Thus, it is necessary to study a process of inspection more efficient and economic than the currently used which often consist in semi-destructive methods (the most common is the pull off test), that can only be used in a small part of the building at a time, allowing some assumptions of what can the rest of the cladding be like. Infrared thermography (IRT) is a NDT with a wide variety of applications in building inspection that is becoming commonly used to identify anomalies related with thermal variations in the inspected surfaces. Few authors have studied the application of IRT in anomalies associated with ceramic claddings claiming that the presence of air or water beneath the superficial layer will influence the heat transfer in a way that can be detected in both a qualitative and a quantitative way by the thermal camera, providing information about the state of the wall in a much broad area per trial than other methods commonly used nowadays. This article intends to present a review of the state of art of this NDT and its potentiality in becoming a more efficient way to diagnose anomalies in ceramic wall claddings.

A comparative review of risk based portfolio allocations: An empirical study throughout rising yields

Since the financial crisis, risk based portfolio allocations have gained a great deal in popularity. This increase in popularity is primarily due to the fact that they make no assumptions as to the expected return of the assets in the portfolio. These portfolios implicitly put risk management at the heart of asset allocation and thus their recent appeal. This paper will serve as a comparison of four well-known risk based portfolio allocation methods; minimum variance, maximum diversification, inverse volatility and equally weighted risk contribution. Empirical backtests will be performed throughout rising interest rate periods from 1953 to 2015. Additionally, I will compare these portfolios to more simple allocation methods, such as equally weighted and a 60/40 asset-allocation mix. This paper will help to answer the question if these portfolios can survive in a rising interest rate environment.

Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.