The relationship between academic library usage and perceived personal performance in Kuwait

Academic libraries around the world often have to justify high maintenance costs. High maintenance costs of university libraries are often justified by the belief that regular use of an academic library improves the grades of students. However, this is a difficult statement to support, therefore demonstrating the link between library use and student outcomes is critical to ensuring that library investment continues. Questionnaires and interviews were conducted and the findings were analysed to derive users’ perceptions. The findings revealed interesting results regarding how users make use of the library and how users feel the library improves their personal performance. Overall, the perception of all three groups of the academic libraries within Kuwait is positive, however many users are dissatisfied with some academic library services. Students answered positively regarding their grades and use of the academic library. Academics and administrators were generally positive and offered an experienced insight into the quality of the library. This study offers the first perception based results in Kuwait. The inclusion of administrators’ perceptions is also novel in terms of the Gulf States. A refined model was designed based on the overall findings within the study. This model can be applied to any academic library, regardless of size or collection type. Based on findings, the researcher recommends taking the following points into consideration in order to improve library services and facilities for all users. Improvements could be made in the structure of library training courses and academic libraries should be providing flexible spaces for individuals and group study as well as social activities.

Utilising Reliability and Condition Monitoring Data for Asset Health Prognosis

The ability to forecast machinery health is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models which attempt to forecast machinery health based on condition data such as vibration measurements. This paper demonstrates how the population characteristics and condition monitoring data (both complete and suspended) of historical items can be integrated for training an intelligent agent to predict asset health multiple steps ahead. The model consists of a feed-forward neural network whose training targets are asset survival probabilities estimated using a variation of the Kaplan–Meier estimator and a degradation-based failure probability density function estimator. The trained network is capable of estimating the future survival probabilities when a series of asset condition readings are inputted. The output survival probabilities collectively form an estimated survival curve. Pump data from a pulp and paper mill were used for model validation and comparison. The results indicate that the proposed model can predict more accurately as well as further ahead than similar models which neglect population characteristics and suspended data. This work presents a compelling concept for longer-range fault prognosis utilising available information more fully and accurately.

Asset health prediction using the explicit hazard model

The ability to estimate the asset reliability and the probability of failure is critical to reducing maintenance costs, operation downtime, and safety hazards. Predicting the survival time and the probability of failure in future time is an indispensable requirement in prognostics and asset health management. In traditional reliability models, the lifetime of an asset is estimated using failure event data, alone; however, statistically sufficient failure event data are often difficult to attain in real-life situations due to poor data management, effective preventive maintenance, and the small population of identical assets in use. Condition indicators and operating environment indicators are two types of covariate data that are normally obtained in addition to failure event and suspended data. These data contain significant information about the state and health of an asset. Condition indicators reflect the level of degradation of assets while operating environment indicators accelerate or decelerate the lifetime of assets. When these data are available, an alternative approach to the traditional reliability analysis is the modelling of condition indicators and operating environment indicators and their failure-generating mechanisms using a covariate-based hazard model. The literature review indicates that a number of covariate-based hazard models have been developed. All of these existing covariate-based hazard models were developed based on the principle theory of the Proportional Hazard Model (PHM). However, most of these models have not attracted much attention in the field of machinery prognostics. Moreover, due to the prominence of PHM, attempts at developing alternative models, to some extent, have been stifled, although a number of alternative models to PHM have been suggested. The existing covariate-based hazard models neglect to fully utilise three types of asset health information (including failure event data (i.e. observed and/or suspended), condition data, and operating environment data) into a model to have more effective hazard and reliability predictions. In addition, current research shows that condition indicators and operating environment indicators have different characteristics and they are non-homogeneous covariate data. Condition indicators act as response variables (or dependent variables) whereas operating environment indicators act as explanatory variables (or independent variables). However, these non-homogenous covariate data were modelled in the same way for hazard prediction in the existing covariate-based hazard models. The related and yet more imperative question is how both of these indicators should be effectively modelled and integrated into the covariate-based hazard model. This work presents a new approach for addressing the aforementioned challenges. The new covariate-based hazard model, which termed as Explicit Hazard Model (EHM), explicitly and effectively incorporates all three available asset health information into the modelling of hazard and reliability predictions and also drives the relationship between actual asset health and condition measurements as well as operating environment measurements. The theoretical development of the model and its parameter estimation method are demonstrated in this work. EHM assumes that the baseline hazard is a function of the both time and condition indicators. Condition indicators provide information about the health condition of an asset; therefore they update and reform the baseline hazard of EHM according to the health state of asset at given time t. Some examples of condition indicators are the vibration of rotating machinery, the level of metal particles in engine oil analysis, and wear in a component, to name but a few. Operating environment indicators in this model are failure accelerators and/or decelerators that are included in the covariate function of EHM and may increase or decrease the value of the hazard from the baseline hazard. These indicators caused by the environment in which an asset operates, and that have not been explicitly identified by the condition indicators (e.g. Loads, environmental stresses, and other dynamically changing environment factors). While the effects of operating environment indicators could be nought in EHM; condition indicators could emerge because these indicators are observed and measured as long as an asset is operational and survived. EHM has several advantages over the existing covariate-based hazard models. One is this model utilises three different sources of asset health data (i.e. population characteristics, condition indicators, and operating environment indicators) to effectively predict hazard and reliability. Another is that EHM explicitly investigates the relationship between condition and operating environment indicators associated with the hazard of an asset. Furthermore, the proportionality assumption, which most of the covariate-based hazard models suffer from it, does not exist in EHM. According to the sample size of failure/suspension times, EHM is extended into two forms: semi-parametric and non-parametric. The semi-parametric EHM assumes a specified lifetime distribution (i.e. Weibull distribution) in the form of the baseline hazard. However, for more industry applications, due to sparse failure event data of assets, the analysis of such data often involves complex distributional shapes about which little is known. Therefore, to avoid the restrictive assumption of the semi-parametric EHM about assuming a specified lifetime distribution for failure event histories, the non-parametric EHM, which is a distribution free model, has been developed. The development of EHM into two forms is another merit of the model. A case study was conducted using laboratory experiment data to validate the practicality of the both semi-parametric and non-parametric EHMs. The performance of the newly-developed models is appraised using the comparison amongst the estimated results of these models and the other existing covariate-based hazard models. The comparison results demonstrated that both the semi-parametric and non-parametric EHMs outperform the existing covariate-based hazard models. Future research directions regarding to the new parameter estimation method in the case of time-dependent effects of covariates and missing data, application of EHM in both repairable and non-repairable systems using field data, and a decision support model in which linked to the estimated reliability results, are also identified.

Commercial vehicle pre-clearance programs : current issues and recommendations for potential implementation

The regulation of overweight trucks is of increasing importance. Quickly growing heavy vehicle volumes over-proportionally contribute to roadway damage. Raising maintenance costs and compromised road safety are also becoming a major concern to managing agencies. Minimizing pavement wear is done by regulating overloaded trucks on major highways at weigh stations. However, due to lengthy inspections and insufficient capacities, weigh stations tend to be inefficient. New practices, using Radio Frequency Identification (RFID) transponders and weigh-in-motion technologies, called preclearance programs, have been set up in a number of countries. The primary aim of this study is to investigate the current issues with regard to the implementation and operation of the preclearance program. The State of Queensland, Australia, is used as a case study. The investigation focuses on three aspects; the first emphasizes on identifying the need for improvement of the current regulation programs in Queensland. Second, the operators of existing preclearance programs are interviewed for their lessons-learned and the marketing strategies used for promoting their programs. The trucking companies in Queensland are interviewed for their experiences with the current weighing practices and attitudes toward the potential preclearance system. Finally, the estimated benefit of the preclearance program deployment in Queensland is analyzed. The penultimate part brings the former four parts together and provides the study findings and recommendations. The framework and study findings could be valuable inputs for other roadway agencies considering a similar preclearance program or looking to promote their existing ones.

Perceived obstacles to multi-storey timber-frame construction : an Australian study

The contemporary default materials for multi-storey buildings – namely concrete and steel – are all significant generators of carbon and the use of timber products provides a technically, economically and environmentally viable alternative. In particular, timber’s sustainability can drive increased use and subsequent evolution of the Blue economy as a new economic model. National research to date, however, indicates a resistance to the uptake of timber technologies in Australia. To investigate this further, a preliminary study involving a convenience sample of 15 experts was conducted to identify the main barriers involved in the use of timber frames in multi-storey buildings. A closed-ended questionnaire survey involving 74 experienced construction industry participants was then undertaken to rate the relative importance of the barriers. The survey confirmed the most significant barriers to be a perceived increase in maintenance costs and fire risk, together with a limited awareness of the emerging timber technologies available. It is expected that the results will benefit government and the timber industry, contributing to environmental improvement by developing strategies to increase the use of timber technologies in multi-storey buildings by countering perceived barriers in the Australian context.

Integrated approach for diagnostics and prognostics of HP LNG pump based on health state probability estimation

Effective machine fault prognostic technologies can lead to elimination of unscheduled downtime and increase machine useful life and consequently lead to reduction of maintenance costs as well as prevention of human casualties in real engineering asset management. This paper presents a technique for accurate assessment of the remnant life of machines based on health state probability estimation technique and historical failure knowledge embedded in the closed loop diagnostic and prognostic system. To estimate a discrete machine degradation state which can represent the complex nature of machine degradation effectively, the proposed prognostic model employed a classification algorithm which can use a number of damage sensitive features compared to conventional time series analysis techniques for accurate long-term prediction. To validate the feasibility of the proposed model, the five different level data of typical four faults from High Pressure Liquefied Natural Gas (HP-LNG) pumps were used for the comparison of intelligent diagnostic test using five different classification algorithms. In addition, two sets of impeller-rub data were analysed and employed to predict the remnant life of pump based on estimation of health state probability using the Support Vector Machine (SVM) classifier. The results obtained were very encouraging and showed that the proposed prognostics system has the potential to be used as an estimation tool for machine remnant life prediction in real life industrial applications.

Improving boiler performance with modified boiler control systems

For timely processing of the crop, sugar factories need boiler stations that can reliably produce steam when fired with fuel of variable quality. The control systems installed on most sugar factory boilers have changed little in the last thirty years and in some cases the default control system response to changes in fuel and/or fuel quality is not correct and operator intervention is required to prevent factory stoppages or reductions in crushing rate caused by poor combustion. Some factories have recently modified their boiler control systems for improved combustion performance and reduced maintenance costs. This paper describes testing carried out to evaluate some of these control system modifications and identifies boiler control system changes that can be applied more widely in the sugar industry.

BIM-based lifecycle planning and specifications for sustainable cities of the future: A conceptual approach

Annually, several million tonnes of waste are produced from reworks, demolition, and use of substandard materials. Building Information Modelling (BIM), a digital representation of facilities and their constituent data, is a viable means of addressing some concerns about the impacts of these processes. BIM functionalities can be extended and combined with rich building information from specifications and product libraries, for efficient, streamlined design and construction. This paper conceptualises a framework for BIM-knowledge transfer from advanced economies for adaptation and use in urban development works in developing nations using the Sydney Down Under and Lagos Eko Atlantic projects as reference points. We present a scenario that highlights BIM-based lifecycle planning/specifications as agents of sustainable construction (in terms of cost and time) crucial to the quality of as-built data from early on in city development. We show how, through the use of BIM, city planners in developing nations can avoid high, retrospective (and sometimes wasteful) maintenance costs and leapfrog infrastructure management standards of advanced economies. Finally, this paper illustrates how BIM can address concerns about economic sustainability during city development in developing countries by enriching model objects with specification information sourced from a product library.

Resolving the conflicts of sustainable world heritage landscapes in cities : fully open or limited access for visitors?

World Heritage Landscapes (WHLs) are receiving increased attention from researchers, urban planners, managers, and policy makers and many heritage values and resources are becoming irreversibly lost. This phenomenon is especially prominent for WHLs located in cities, where greater development opportunities are involved. Decision making for sustainable urban landscape planning, conservation and management of WHLs often takes place from an economic perspective, especially in developing countries. This, together with the uncertain source of funding to cover WHL operating and maintenance costs, has resulted in many urban managers seeking private sector funding either in the form of visitor access fees or leasing part of the site for high-rental facilities such as five star hotels, clubs and expensive restaurants. For the former, this can result in low-income urban citizens being unable to afford the access fees and hence contradicting the principle of equal access for all; while, for the latter, the principle of open access for all is equally violated. To resolve this conflict, a game model is developed to determine how urban managers should allocate WHL spaces to maximize the combination of economic, social and ecological benefits and cultural values. A case study is provided of the Hangzhou's West Lake Scenic Area, a WHL located at the centre of Hangzhou city, in which several high-rental facilities have recently been closed down by the local authorities due to charges of elitism and misuse of public funds by government officials. The result shows that the best solution is to lease a small space with high rents and leave the remainder of the site to the public. This solution is likely to be applicable only in cities with a strong economy.

Explicit finite element modelling of bridge girder bearing pedestals

Bridge girder bearings rest on pedestals to transfer the loading safely to the pier headstock. In spite of the existence of industry guidelines, due to construction complexities, such guidelines are often overlooked. Further, there is paucity of research on the performance of pedestals, although their failure could cause exorbitant maintenance costs. Although reinforced concrete pedestals are recommended in the industry design guidelines, unreinforced concrete and/ or epoxy glue pedestals are provided due to construction issues; such pedestals fail within a very short period of service. With a view to understanding the response of pedestals subject to monotonic loading, a three-dimensional nonlinear explicit finite element micro-model of unreinforced and reinforced concrete pedestals has been developed. Contact and material nonlinearity have been accounted for in the model. It is shown that the unreinforced concrete pedestals suffer from localised edge stress singularities, the failure of which was comparable to those in the field. The reinforced concrete pedestals, on the other hand, distribute the loading without edge stress singularity, again conforming to the field experience.

Remaining useful life prediction of rotating equipment using covariate-based hazard models : industry applications

The ability to estimate the expected Remaining Useful Life (RUL) is critical to reduce maintenance costs, operational downtime and safety hazards. In most industries, reliability analysis is based on the Reliability Centred Maintenance (RCM) and lifetime distribution models. In these models, the lifetime of an asset is estimated using failure time data; however, statistically sufficient failure time data are often difficult to attain in practice due to the fixed time-based replacement and the small population of identical assets. When condition indicator data are available in addition to failure time data, one of the alternate approaches to the traditional reliability models is the Condition-Based Maintenance (CBM). The covariate-based hazard modelling is one of CBM approaches. There are a number of covariate-based hazard models; however, little study has been conducted to evaluate the performance of these models in asset life prediction using various condition indicators and data availability. This paper reviews two covariate-based hazard models, Proportional Hazard Model (PHM) and Proportional Covariate Model (PCM). To assess these models’ performance, the expected RUL is compared to the actual RUL. Outcomes demonstrate that both models achieve convincingly good results in RUL prediction; however, PCM has smaller absolute prediction error. In addition, PHM shows over-smoothing tendency compared to PCM in sudden changes of condition data. Moreover, the case studies show PCM is not being biased in the case of small sample size.

Understanding pressures on the future of roads: A sustainable built environment National Research Centre Discussion Paper

According to the Australian Government, when combined with expected population growth and internal migration, expected changes in temperature and rainfall are expected to increase road maintenance costs by over 30 percent by 2100. This presents a significant future economic risk, in response, this paper will discuss the potential for roads to improve their resilience to the impacts of climate change and other key pressures. The paper will also highlight how such measures can inform state and national main road infrastructure planning and reduce future associated risks and costs.

The future of roads: How road agencies are facing a conflicted future

Although road construction and use provides significant economic and social benefits, its environmental impact is of growing concern. Roads are one of the greatest greenhouse gas contributors, both directly through fossil energy consumed in mining, transporting, earthworks and paving work, and through the emissions from road use by vehicles. Further,according to the Australian Government, when combined with expected population growth and internal migration,expected changes in temperature and rainfall are expected to increase road maintenance costs. This discussion paper will outline opportunities within the Australian context for reducing environmental and carbon pressure from road building, and provide a framework for considering the potential pressures that will affect the resilience of roads to the impacts of climate change and oil vulnerability.

The future of roads: Reducing environmental pressures and the management of carbon

Although road construction and use provides significant economic and social benefits, its environmental impact is of growing concern. Roads are one of the greatest greenhouse gas contributors, both directly through fossil energy consumed in mining, transporting, earthworks and paving work, plus the emissions from road use by vehicles. Further, according to the Australian Government, when combined with forecast population growth, internal migration and changes in temperature and rainfall, these are expected to increase road maintenance costs. This discussion paper outlines opportunities within the Australian context for reducing environmental and carbon pressure from road building, and provides a framework for considering the potential future pressures that will affect the resilience of roads to the impacts of climate change and oil vulnerability. Seven strategic areas are outlined for further investigation, including a guide to carbon management for road agencies covering planning, funding, procurement, delivery and maintenance of roads.

Milling innovations at Rocky Point

This paper reviews the innovations that have been introduced in the milling train at Rocky Point mill since 2001 and provides some operational, performance and maintenance comparisons of the technologies in use. The decision to install BHEM mills in the #2 and #3 mill positions to complement the six-roll mills in the #1 and #4 mill positions has proven a good one. Satisfactory performance is being obtained by these mills while maintenance costs are significantly less. Very good #1 mill extraction and final bagasse moisture content are being achieved. The innovation of using Hägglunds hydraulic drives at higher speed…