Structural damage identification based on self-fitting ARMAX model and multi-sensor data fusion

Statistical time series methods have proven to be a promising technique in structural health monitoring, since it provides a direct form of data analysis and eliminates the requirement for domain transformation. Latest research in structural health monitoring presents a number of statistical models that have been successfully used to construct quantified models of vibration response signals. Although a majority of these studies present viable results, the aspects of practical implementation, statistical model construction and decision-making procedures are often vaguely defined or omitted from presented work. In this article, a comprehensive methodology is developed, which essentially utilizes an auto-regressive moving average with exogenous input model to create quantified model estimates of experimentally acquired response signals. An iterative self-fitting algorithm is proposed to construct and fit the auto-regressive moving average with exogenous input model, which is capable of integrally finding an optimum set of auto-regressive moving average with exogenous input model parameters. After creating a dataset of quantified response signals, an unlabelled response signal can be identified according to a 'closest-fit' available in the dataset. A unique averaging method is proposed and implemented for multi-sensor data fusion to decrease the margin of error with sensors, thus increasing the reliability of global damage identification. To demonstrate the effectiveness of the developed methodology, a steel frame structure subjected to various bolt-connection damage scenarios is tested. Damage identification results from the experimental study suggest that the proposed methodology can be employed as an efficient and functional damage identification tool. © The Author(s) 2014.

Contact lens fitting and training in a child and youth population

PURPOSE: To determine the ease with which children and youths without previous soft contact lens (SCL) experience were able to handle, care for, adapt and be fitted with SCLs. METHODS: 179 children aged 8-16 were recruited. Study visits included: screening and training visits, 1-week and 3-month follow-ups. During the training visit, the time taken to demonstrate proficiency in lens insertion and removal and care was recorded. A second training visit was scheduled if necessary. RESULTS: Nine children did not complete the screening visit and eight discontinued during the study. Of those eight, seven discontinued during the first week and one before the 3-month visit. Of those recruited, 90.5% (162/179) were successfully fitted and completed the study. A majority of children were dispensed with lenses at the first training visit (94.6%, 162/171). The mean training time for all children was 30min. There were no statistically significant differences in the number of lenses required to fit or instruction time by age group (p>0.05) or gender (p>0.05). Nine participants (5.3%, 9/171) required a second training visit with four still unable to handle lenses (2.3%, 4/171). By the 1-week visit 13.2% (22/167) of participants either lost or tore lenses, no subsequent lost or torn lenses occurred. No serious adverse events occurred during the study. CONCLUSION: Children and youths with no previous contact lens experience were easily fitted, able to successfully wear and care for lenses. The results of this study should encourage practitioners to recommend SCLs as a vision correction option.

Performance assessment of earth pipe cooling system for low energy buildings in a subtropical climate

Energy consumption in heating and cooling around the world has been a major contributor to global warming. Hence, many studies have been aimed at finding new techniques to save and control energy through energy efficient measures. Most of this energy is used in residential, agricultural and commercial buildings. It is therefore important to adopt energy efficiency measures in these buildings through new technologies and novel building designs. These new building designs can be developed by employing various passive cooling systems. Earth pipe cooling is one of these which can assist to save energy without using any customary mechanical units. This paper investigates the earth pipe cooling performance in a hot humid subtropical climate of Rockhampton, Australia. A thermal model is developed using ANSYS Fluent for measuring its performance. Impacts of air velocity, air temperature, relative humidity and soil temperature on room cooling performance are also assessed. A temperature reduction of around 2 °C was found for the system. This temperature reduction contributed to an energy saving of a maximum of 866.54 kW (8.82%) per year for a 27.23 m3 room.

Numerical modeling of vertical earth pipe cooling system for hot and humid subtropical climate

Energy crisis is one of the major problems facing the progress of human society. There are several energy-efficient technologies that can be applied to save energy and make a sustainable environment. Passive air cooling of earth pipe cooling technology is one of them to reduce the energy consumption for hot and humid subtropical climates. The technology works with a long buried pipe with one end for intake air and the other end for providing air cooled by soil to the desired space such as residential, agricultural, or industrial buildings. It can be an attractive economical alternative to conventional cooling since there are no compressors or any customary mechanical unit. This chapter reports the performance of a vertical earth pipe cooling system for a hot and humid subtropical climatic zone in Queensland, Australia. A series of buried pipes were installed in vertical arrangement in order to increase earth pipe cooling performance. To measure the performance of the system, a numerical model was developed and simulated using the CFD software Fluent in ANSYS 15.0. Data were collected from two modeled rooms built from two shipping containers and installed at the Sustainable Precinct at Central Queensland University, Rockhampton, Australia. The impact of air temperature and velocity on room cooling performance has also been assessed. A temperature reduction of 1.82 °C was observed in the room connected to the vertical earth pipe cooling system, which will save the energy cost for thermal cooling in buildings.

Integrated model of horizontal earth pipe cooling system for a hot humid climate

Energy efficiency of a building has become a major requirement since the building sector produces 40%-50% of the global greenhouse gas emissions. This can be achieved by improving building’s performance through energy savings, by adopting energy efficient technologies and reducing CO2 emissions. There exist several technologies with less or no environmental impact that can be used to reduce energy consumption of the buildings. Earth pipe cooling system is one of them, which works with a long buried pipe with one end for intake air and the other end for providing air cooled by soil to the building. It is an approach for cooling a room in a passive process without using any habitual mechanical unit. The paper investigates the thermal performance of a horizontal earth pipe cooling system in a hot and humid subtropical climatic zone in Queensland, Australia. An integrated numerical model for the horizontal earth pipe cooling system and the room (or building) was developed using ANSYS Fluent to measure the thermal performance of the system. The impact of air temperature, soil temperature, air velocity and relative humidity on room cooling performance has also been assessed. As the soil temperature was below the outdoor minimum temperature during the peak warming hours of the day, it worked as an effective heat sink to cool the room. Both experimental and numerical results showed a temperature reduction of 1.11oC in the room utilizing horizontal earth pipe cooling system which will assist to save the energy cost in the buildings.

Parametric study on thermal performance of horizontal earth pipe cooling system in summer

Rational use of energy and its associated greenhouse gas emissions has become a key issue for a sustainable environment and economy. A substantial amount of energy is consumed by today's buildings which are accountable for about 40% of the global energy consumption. There are on-going researches in order to overcome these and find new techniques through energy efficient measures. Passive air cooling of earth pipe cooling technique is one of those which can save energy in buildings with no greenhouse gas emissions. The performance of the earth pipe cooling system is mainly affected by the parameters, namely air velocity, pipe length, pipe diameter, pipe material, and pipe depth. This paper investigates the impact of these parameters on thermal performance of the horizontal earth pipe cooling system in a hot humid subtropical climate at Rockhampton, Australia. For the parametric investigation, a thermal model was developed for the horizontal earth pipe cooling system using the simulation program, FLUENT 15.0. Results showed a significant effect for air velocity, pipe length, and pipe diameter on the earth pipe cooling performance, where the pipe length dominated the other parameters.

Fitting aggregation functions to data: part I-linearization and regularization

The use of supervised learning techniques for fitting weights and/or generator functions of weighted quasi-arithmetic means – a special class of idempotent and nondecreasing aggregation functions – to empirical data has already been considered in a number of papers. Nevertheless, there are still some important issues that have not been discussed in the literature yet. In the first part of this two-part contribution we deal with the concept of regularization, a quite standard technique from machine learning applied so as to increase the fit quality on test and validation data samples. Due to the constraints on the weighting vector, it turns out that quite different methods can be used in the current framework, as compared to regression models. Moreover, it is worth noting that so far fitting weighted quasi-arithmetic means to empirical data has only been performed approximately, via the so-called linearization technique. In this paper we consider exact solutions to such special optimization tasks and indicate cases where linearization leads to much worse solutions.

Fitting aggregation functions to data: Part II - idempotization

The use of supervised learning techniques for fitting weights and/or generator functions of weighted quasi-arithmetic means – a special class of idempotent and nondecreasing aggregation functions – to empirical data has already been considered in a number of papers. Nevertheless, there are still some important issues that have not been discussed in the literature yet. In the second part of this two-part contribution we deal with a quite common situation in which we have inputs coming from different sources, describing a similar phenomenon, but which have not been properly normalized. In such a case, idempotent and nondecreasing functions cannot be used to aggregate them unless proper preprocessing is performed. The proposed idempotization method, based on the notion of B-splines, allows for an automatic calibration of independent variables. The introduced technique is applied in an R source code plagiarism detection system.