Analysis of acoustic emission data for accurate damage assessment for structural health monitoring applications

Structural health monitoring (SHM) refers to the procedure used to assess the condition of structures so that their performance can be monitored and any damage can be detected early. Early detection of damage and appropriate retrofitting will aid in preventing failure of the structure and save money spent on maintenance or replacement and ensure the structure operates safely and efficiently during its whole intended life. Though visual inspection and other techniques such as vibration based ones are available for SHM of structures such as bridges, the use of acoustic emission (AE) technique is an attractive option and is increasing in use. AE waves are high frequency stress waves generated by rapid release of energy from localised sources within a material, such as crack initiation and growth. AE technique involves recording these waves by means of sensors attached on the surface and then analysing the signals to extract information about the nature of the source. High sensitivity to crack growth, ability to locate source, passive nature (no need to supply energy from outside, but energy from damage source itself is utilised) and possibility to perform real time monitoring (detecting crack as it occurs or grows) are some of the attractive features of AE technique. In spite of these advantages, challenges still exist in using AE technique for monitoring applications, especially in the area of analysis of recorded AE data, as large volumes of data are usually generated during monitoring. The need for effective data analysis can be linked with three main aims of monitoring: (a) accurately locating the source of damage; (b) identifying and discriminating signals from different sources of acoustic emission and (c) quantifying the level of damage of AE source for severity assessment. In AE technique, the location of the emission source is usually calculated using the times of arrival and velocities of the AE signals recorded by a number of sensors. But complications arise as AE waves can travel in a structure in a number of different modes that have different velocities and frequencies. Hence, to accurately locate a source it is necessary to identify the modes recorded by the sensors. This study has proposed and tested the use of time-frequency analysis tools such as short time Fourier transform to identify the modes and the use of the velocities of these modes to achieve very accurate results. Further, this study has explored the possibility of reducing the number of sensors needed for data capture by using the velocities of modes captured by a single sensor for source localization. A major problem in practical use of AE technique is the presence of sources of AE other than crack related, such as rubbing and impacts between different components of a structure. These spurious AE signals often mask the signals from the crack activity; hence discrimination of signals to identify the sources is very important. This work developed a model that uses different signal processing tools such as cross-correlation, magnitude squared coherence and energy distribution in different frequency bands as well as modal analysis (comparing amplitudes of identified modes) for accurately differentiating signals from different simulated AE sources. Quantification tools to assess the severity of the damage sources are highly desirable in practical applications. Though different damage quantification methods have been proposed in AE technique, not all have achieved universal approval or have been approved as suitable for all situations. The b-value analysis, which involves the study of distribution of amplitudes of AE signals, and its modified form (known as improved b-value analysis), was investigated for suitability for damage quantification purposes in ductile materials such as steel. This was found to give encouraging results for analysis of data from laboratory, thereby extending the possibility of its use for real life structures. By addressing these primary issues, it is believed that this thesis has helped improve the effectiveness of AE technique for structural health monitoring of civil infrastructures such as bridges.

Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals

Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.

Developing a creative cluster in a post-industrial city : the Creative Industries Development Service (CIDS) and Manchester

This article takes the establishment and demise of Manchester’s Creative Industries Development Service (CIDS) as an exemplary case study for the ways in which creative industry policy has intersected with urban economic policy over the last decade. The authors argue that the creative industries required specific kinds of economic development agencies that would be able to act as “intermediaries” between the distinct languages of policymakers and “creatives.” They discuss the tensions inherent in such an approach and how CIDS attempted to manage them and suggest that the main reason for the demise of the CIDS was the domination of the “economic” over the “cultural” logic, both of which are present within the creative industries policy discourse.

Analysing seasonal data

Many common diseases, such as the flu and cardiovascular disease, increase markedly in winter and dip in summer. These seasonal patterns have been part of life for millennia and were first noted in ancient Greece by both Hippocrates and Herodotus. Recent interest has focused on climate change, and the concern that seasons will become more extreme with harsher winter and summer weather. We describe a set of R functions designed to model seasonal patterns in disease. We illustrate some simple descriptive and graphical methods, a more complex method that is able to model non-stationary patterns, and the case–crossover for controlling for seasonal confounding.

Sharing environmental data : the role of an information policy framework and copyright licensing

Advances in information and communication technologies have brought about an information revolution, leading to fundamental changes in the way that information is collected or generated, shared and distributed. The importance of establishing systems in which research findings can be readily made available to and used by other researchers has long been recognized in international scientific collaborations. If the data access principles adopted by international scientific collaborations are to be effectively implemented they must be supported by the national policies and laws in place in the countries in which participating researchers are operating.

Researcher attitudes to data sharing : cultural change requires better motivations

While undertaking the ANDS RDA Gold Standard Record Exemplars project, research data sharing was discussed with many QUT researchers. Our experiences provided rich insight into researcher attitudes towards their data and the sharing of such data. Generally, we found traditional altruistic motivations for research data sharing did not inspire researchers, but an explanation of the more achievement-oriented benefits were more compelling.

Identifying familiarity to facilitate intuitive interaction for older adults

Almost every nation on the planet is experiencing increases in both the number and proportion of older adults. Research has shown that older adults use technology less intuitively than younger adults, and have more difficulty with using products effectively. With an ever-increasing population of older adults, it is necessary to understand why they often struggle to use technology, which is becoming more and more important in day to day living. Intuitive use of products is grounded in familiarity and prior experience. The aims of this research were twofold: (i) to examine the differences in familiarity between younger and older adults, to see if this could explain the difficulties faced by some older adults; (ii) to develop investigational methods to assist designers in identifying familiarity in prospective users. Two empirical studies were conducted. The first experiment was conducted in the field with 32 participants, divided across four age groups (18 – 44, 45 – 59, 60 – 74, and 75+). This experiment was conducted in the participants’ homes, with a product they were familiar with. Familiarity was measured through the analysis of data collected through interviews, observation and retrospective protocol. The results of this study show that the youngest group demonstrated significantly higher levels of familiarity with products they own than the 60 – 74 and the 75+ age groups. There were no significant differences between the 18 – 44 age group and the 45 – 59 age group and there were also no significant differences between the three oldest age groups. The second experiment was conducted with 32 participants, across the same four age groups. Four everyday products were used in this experiment. The results of Experiment 2 show that, with previously unused products, younger adults demonstrate significantly higher levels of familiarity than the three older age groups. The three oldest age groups had no significant differences between them. The results of these two studies show that younger adults are more familiar with contemporary products than older adults. They also demonstrate that in terms of familiarity, older adults do not differ significantly as they get older. The results also show that the 45 – 59 age group demonstrate higher levels of familiarity with products they have owned, in comparison with those they have not. The two older age groups did not demonstrate such differences. This suggests that interacting with products over time increases familiarity more for middle-aged adults than for older adults. As a result of this research, a method that can be used by designers to identify potential users’ product familiarity has been identified. This method is easy to use, quick, low cost, highly mobile, flexible, and allows for easy data collection and analysis. A tool has been designed that assists designers and researchers to use the method. Designers can use the knowledge gained from this tool, and integrate it into the design process, resulting in more intuitive products. Such products may lead to improvements in the quality of life of older adults, as a result of improved societal integration, better health management, and more widespread use of communications technology.

VIVO as an Institutional Data Registry

The Queensland University of Technology (QUT) in Brisbane, Australia, is involved in a number of projects funded by the Australian National Data Service (ANDS). Currently, QUT is working on a project (Metadata Stores Project) that uses open source VIVO software to aid in the storage and management of metadata relating to data sets created/managed by the QUT research community. The registry (called QUT Research Data Finder) will support the sharing and reuse of research datasets, within and external to QUT. QUT uses VIVO for both the display and the editing of research metadata.

Maintenance test section survey : data collection and analysis

A Maintenance Test Section Survey (MTSS) was conducted as part of a Peer State Review of the Texas Maintenance Program conducted October 5–7, 2010. The purpose of the MTSS was to conduct a field review of 34 highway test sections and obtain participants’ opinions about pavement, roadside, and maintenance conditions. The goal was to cross reference or benchmark TxDOT’s maintenance practices based on practices used by selected peer states. Representatives from six peer states (California, Georgia, Kansas, Missouri, North Carolina, and Washington) were invited to Austin to attend a 3-day Peer State Review of TxDOT Maintenance Practices Workshop and to participate in a field survey of a number of pre-selected one-mile roadway sections. It should be emphasized that the objective of the survey was not to evaluate and grade or score TxDOT’s road network but rather to determine whether the selected roadway sections met acceptable standards of service as perceived by Directors of Maintenance or senior maintenance managers from the peer states...

Pandemic (H1N1) Influenza 2009 and Australian emergency departments : implications for policy, practice and pandemic preparedness

Objective: To describe the reported impact of Pandemic (H1N1) 2009 on EDs, so as to inform future pandemic policy, planning and response management. Methods: This study comprised an issue and theme analysis of publicly accessible literature, data from jurisdictional health departments, and data obtained from two electronic surveys of ED directors and ED staff. The issues identified formed the basis of policy analysis and evaluation. Results: Pandemic (H1N1) 2009 had a significant impact on EDs with presentation for patients with ‘influenza-like illness’ up to three times that of the same time in previous years. Staff reported a range of issues, including poor awareness of pandemic plans, patient and family aggression, chaotic information flow to themselves and the public, heightened stress related to increased workloads and lower levels of staffing due to illness, family care duties and redeployment of staff to flu clinics. Staff identified considerable discomfort associated with prolonged times wearing personal protective equipment. Staff believed that the care of non-flu patients was compromised during the pandemic as a result of overwork, distraction from core business and the difficulties associated with accommodating infectious patients in an environment that was not conducive. Conclusions: This paper describes the breadth of the impact of pandemics on ED operations. It identifies a need to address a range of industrial, management and procedural issues. In particular, there is a need for a single authoritative source of information, the re-engineering of EDs to accommodate infectious patients and organizational changes to enable rapid deployment of alternative sources of care.

Energy-efficient virtual machine placement in data centers by genetic algorithm

Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches to the virtual machine placement problem consider the energy consumption by physical machines in a data center only, but do not consider the energy consumption in communication network in the data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement in order to make the data center more energy-efficient. In this paper, we propose a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both the servers and the communication network in the data center. Experimental results show that the genetic algorithm performs well when tackling test problems of different kinds, and scales up well when the problem size increases.

The lived world of older urban Australians: relating everyday living to GPS tracking data

Neighbourhood like the concept of liveability is usually measured by either subjective indicators using surveys of residents’ perceptions or by objective means using secondary data or relative weights for objective indicators of the urban environment. Rarely, have objective and subjective indicators been related to one another in order to understand what constitutes a liveable urban neighbourhood both spatially and behaviourally. This paper explores the use of qualitative (diaries, in-depth interviews) and quantitative (Global Positioning Systems, Geographical Information Systems mapping) liveability research data to examine the perceptions and behaviour of 12 older residents living in six high density urban areas of Brisbane. Older urban Australians are one of the two principal groups highly attracted to high density urban living. The strength of the relationship between the qualitative and quantitative measures was examined. Results of the research indicate a weak relationship between subjective and objective indicators. Linking the two methods (quantitative and qualitative) is important in obtaining a greater understanding of human behaviour and the lived world of older urban Australians and in providing a wider picture of the urban neighbourhood.

A sequential Monte Carlo approach to the sequential design for discriminating between rival continuous data models

Here we present a sequential Monte Carlo approach to Bayesian sequential design for the incorporation of model uncertainty. The methodology is demonstrated through the development and implementation of two model discrimination utilities; mutual information and total separation, but it can also be applied more generally if one has different experimental aims. A sequential Monte Carlo algorithm is run for each rival model (in parallel), and provides a convenient estimate of the marginal likelihood (of each model) given the data, which can be used for model comparison and in the evaluation of utility functions. A major benefit of this approach is that it requires very little problem specific tuning and is also computationally efficient when compared to full Markov chain Monte Carlo approaches. This research is motivated by applications in drug development and chemical engineering.

Verifying a Student Engagement, Success and Retention Maturity Model (SESR-MM): A reality check of the model using data derived from three universities

Australian higher education institutions (HEIs) have entered a new phase of regulation and accreditation which includes performance-based funding relating to the participation and retention of students from social and cultural groups previously underrepresented in higher education. However, in addressing these priorities, it is critical that HEIs do not further disadvantage students from certain groups by identifying them for attention because of their social or cultural backgrounds, circumstances which are largely beyond the control of students. In response, many HEIs are focusing effort on university-wide approaches to enhancing the student experience because such approaches will enhance the engagement, success and retention of all students, and in doing so, particularly benefit those students who come from underrepresented groups. Measuring and benchmarking student experiences and engagement that arise from these efforts is well supported by extensive collections of student experience survey data. However no comparable instrument exists that measures the capability of institutions to influence and/or enhance student experiences where capability is an indication of how well an organisational process does what it is designed to do (Rosemann & de Bruin, 2005). We have proposed that the concept of a maturity model (Marshall, 2010; Paulk, 1999) may be useful as a way of assessing the capability of HEIs to provide and implement student engagement, success and retention activities and we are currently articulating a Student Engagement, Success and Retention Maturity Model (SESR-MM), (Clarke, Nelson & Stoodley, 2012; Nelson, Clarke & Stoodley, 2012). Our research aims to address the current gap by facilitating the development of an SESR-MM instrument that aims (i) to enable institutions to assess the capability of their current student engagement and retention programs and strategies to influence and respond to student experiences within the institution; and (ii) to provide institutions with the opportunity to understand various practices across the sector with a view to further improving programs and practices relevant to their context. Our research extends the generational approach which has been useful in considering the evolutionary nature of the first year experience (FYE) (Wilson, 2009). Three generations have been identified and explored: First generation approaches that focus on co-curricular strategies (e.g. orientation and peer programs); Second generation approaches that focus on curriculum (e.g. pedagogy, curriculum design, and learning and teaching practice); and third generation approaches—also referred to as transition pedagogy—that focus on the production of an institution-wide integrated holistic intentional blend of curricular and co-curricular activities (Kift, Nelson & Clarke, 2010). Our research also moves beyond assessments of students’ experiences to focus on assessing institutional processes and their capability to influence student engagement. In essence, we propose to develop and use the maturity model concept to produce an instrument that will indicate the capability of HEIs to manage and improve student engagement, success and retention programs and strategies. The issues explored in this workshop are (i) whether the maturity model concept can be usefully applied to provide a measure of institutional capability for SESR; (ii) whether the SESR-MM can be used to assess the maturity of a particular set of institutional practices; and (iii) whether a collective assessment of an institution’s SESR capabilities can provide an indication of the maturity of the institution’s SESR activities. The workshop will be approached in three stages. Firstly, participants will be introduced to the key characteristics of maturity models, followed by a discussion of the SESR-MM and the processes involved in its development. Secondly, participants will be provided with resources to facilitate the development of a maturity model and an assessment instrument for a range of institutional processes and related practices. In the final stage of the workshop, participants will “assess” the capability of these practices to provide a collective assessment of the maturity of these processes. References Australian Council for Educational Research. (n.d.). Australasian Survey of Student Engagement. Retrieved from http://www.acer.edu.au/research/ausse/background Clarke, J., Nelson, K., & Stoodley, I. (2012, July). The Maturity Model concept as framework for assessing the capability of higher education institutions to address student engagement, success and retention: New horizon or false dawn? A Nuts & Bolts presentation at the 15th International Conference on the First Year in Higher Education, “New Horizons,” Brisbane, Australia. Department of Education, Employment and Workplace Relations. (n.d.). The University Experience Survey. Advancing quality in higher education information sheet. Retrieved from http://www.deewr.gov.au/HigherEducation/Policy/Documents/University_Experience_Survey.pdf Kift, S., Nelson, K., & Clarke, J. (2010) Transition pedagogy - a third generation approach to FYE: A case study of policy and practice for the higher education sector. The International Journal of the First Year in Higher Education, 1(1), pp. 1-20. Marshall, S. (2010). A quality framework for continuous improvement of e-Learning: The e-Learning Maturity Model. Journal of Distance Education, 24(1), 143-166. Nelson, K., Clarke, J., & Stoodley, I. (2012). An exploration of the Maturity Model concept as a vehicle for higher education institutions to assess their capability to address student engagement. A work in progress. Submitted for publication. Paulk, M. (1999). Using the Software CMM with good judgment, ASQ Software Quality Professional, 1(3), 19-29. Wilson, K. (2009, June–July). The impact of institutional, programmatic and personal interventions on an effective and sustainable first-year student experience. Keynote address presented at the 12th Pacific Rim First Year in Higher Education Conference, “Preparing for Tomorrow Today: The First Year as Foundation,” Townsville, Australia. Retrieved from http://www.fyhe.com.au/past_papers/papers09/ppts/Keithia_Wilson_paper.pdf

Does occupational aggregation matter in determining the size of the gender wage gap?

The traditional decomposition of the gender wage gap distinguishes between a component attributable to gender differences in productivity-related characteristics and a residual component that is often taken as a measure of discrimination. This study of data from the 1989 Canadian Labour Market Activity Survey shows that when occupation is treated as a productivity-related characteristic, the proportion of the gender wage gap labeled explained increases with the number of occupational classifications distinguished. However, on the basis of evidence that occupational differences reflect the presence of barriers faced by women attempting to enter male-dominated occupations, the authors conclude that occupation should not be treated as a productivity-related characteristic; and in a decomposition of the gender wage gap that treats occupation as endogenously determined, they find that the level of occupational aggregation has little effect on the size of the "explained" component of the gap.