Assessing wet crashes for the skid resistance management of road assets

Australia, road crash trauma costs the nation A$15 billion annually whilst the US estimates an economic impact of around US$ 230 billion on its network. Worldwide economic cost of road crashes is estimated to be around US$ 518 billion each year. Road accidents occur due to a number of factors including driver behaviour, geometric alignment, vehicle characteristics, environmental impacts, and the type and condition of the road surfacing. Skid resistance is considered one of the most important road surface characteristics because it has a direct effect on traffic safety. In 2005, Austroads (the Association of Australian and New Zealand Road Transport and Traffic Authorities) published a guideline for the management of skid resistance and Queensland Department of Main Roads (QDMR) developed a skid resistance management plan (SRMP). The current QDMR strategy is based on rationale analytical methodology supported by field inspection with related asset management decision tools. The Austroads’s guideline and QDMR's skid resistance management plan have prompted QDMR to review its skid resistance management practice. As a result, a joint research project involving QDMR, Queensland University of Technology (QUT) and the Corporative Research Centre for Integrated Engineering Asset Management (CRC CIEAM) was formed. The research project aims at investigating whether there is significant relationship between road crashes and skid resistance on Queensland’s road networks. If there is, the current skid resistance management practice of QDMR will be reviewed and appropriate skid resistance investigatory levels will be recommended. This paper presents analysis results in assessing the relationship between wet crashes and skid resistance on Queensland roads. Attributes considered in the analysis include surface types, annual average daily traffic (AADT), speed and seal age.

Development of an online condition monitoring system for slow speed machinery

One of the main challenges of slow speed machinery condition monitoring is that the energy generated from an incipient defect is too weak to be detected by traditional vibration measurements due to its low impact energy. Acoustic emission (AE) measurement is an alternative for this as it has the ability to detect crack initiations or rubbing between moving surfaces. However, AE measurement requires high sampling frequency and consequently huge amount of data are obtained to be processed. It also requires expensive hardware to capture those data, storage and involves signal processing techniques to retrieve valuable information on the state of the machine. AE signal has been utilised for early detection of defects in bearings and gears. This paper presents an online condition monitoring (CM) system for slow speed machinery, which attempts to overcome those challenges. The system incorporates relevant signal processing techniques for slow speed CM which include noise removal techniques to enhance the signal-to-noise and peak-holding down sampling to reduce the burden of massive data handling. The analysis software works under Labview environment, which enables online remote control of data acquisition, real-time analysis, offline analysis and diagnostic trending. The system has been fully implemented on a site machine and contributing significantly to improve the maintenance efficiency and provide a safer and reliable operation.

Using data mining on road asset management data in analysing road crashes

Road safety is a major concern worldwide. Road safety will improve as road conditions and their effects on crashes are continually investigated. This paper proposes to use the capability of data mining to include the greater set of road variables for all available crashes with skid resistance values across the Queensland state main road network in order to understand the relationships among crash, traffic and road variables. This paper presents a data mining based methodology for the road asset management data to find out the various road properties that contribute unduly to crashes. The models demonstrate high levels of accuracy in predicting crashes in roads when various road properties are included. This paper presents the findings of these models to show the relationships among skid resistance, crashes, crash characteristics and other road characteristics such as seal type, seal age, road type, texture depth, lane count, pavement width, rutting, speed limit, traffic rates intersections, traffic signage and road design and so on.

Relationship management implementation in the Australian construction industry from the contractor's perspective

Many studies carried out in relation to construction procurement methods reveal evidence of a need to change of culture and attitude in the construction industry. This culture change would transition from traditional adversarial relationships to cooperative and collaborative relationships. Relational contracting approaches, such as partnering and relationship management, are business strategies whereby client, commercial participants’ and stakeholders’ objectives are aligned for mutual benefit. The efficacy of relationship management in the client and contractor groups is proven and well documented. However, the industry has a slow implementation of relational contracting down the value chain. This paper reports the findings of an empirical study which examined the practices and prerequisites for relationship management implementation success and for supply chain engagement to develop. Questionnaire survey, interviews and case studies were conducted with Australian contracting organisations in this study. The study reveals that the adaption of relational contracting approach in the supply chain is found to be limited and contractors still prefer to keep suppliers and subcontractors at arm’s length. Findings also show that the degree of match and mismatch between organizational structuring and organizational process is found to have an impact on staff’s commitment level and performance effectiveness.

Estimating the loading condition of a diesel engine using instantaneous angular speed analysis

Continuing monitoring of diesel engine performance is critical for early detection of fault developments in the engine before they materialize and become a functional failure. Instantaneous crank angular speed (IAS) analysis is one of a few non intrusive condition monitoring techniques that can be utilized for such tasks. In this experimental study, IAS analysis was employed to estimate the loading condition of a 4-stroke 4-cylinder diesel engine in a laboratory condition. It was shown that IAS analysis can provide useful information about engine speed variation caused by the changing piston momentum and crankshaft acceleration during the engine combustion process. It was also found that the major order component of the IAS spectrum directly associated with the engine firing frequency (at twice the mean shaft revolution speed) can be utilized to estimate the engine loading condition regardless of whether the engine is operating at normal running conditions or in a simulated faulty injector case. The amplitude of this order component follows a clear exponential curve as the loading condition changes. A mathematical relationship was established for the estimation of the engine power output based on the amplitude of the major order component of the measured IAS spectrum.

Estimating the loading condition of a diesel engine using instantaneous angular speed analysis

Continuing monitoring of diesel engine performance is critical for early detection of fault developments in the engine before they materialize and become a functional failure. Instantaneous crank angular speed (IAS) analysis is one of a few non intrusive condition monitoring techniques that can be utilized for such tasks. In this experimental study, IAS analysis was employed to estimate the loading condition of a 4-stroke 4-cylinder diesel engine in a laboratory condition. It was shown that IAS analysis can provide useful information about engine speed variation caused by the changing piston momentum and crankshaft acceleration during the engine combustion process. It was also found that the major order component of the IAS spectrum directly associated with the engine firing frequency (at twice the mean shaft revolution speed) can be utilized to estimate the engine loading condition regardless of whether the engine is operating at normal running conditions or in a simulated faulty injector case. The amplitude of this order component follows a clear exponential curve as the loading condition changes. A mathematical relationship was established for the estimation of the engine power output based on the amplitude of the major order component of the measured IAS spectrum.

Industry needs and course content in project management

The new competitive international business environment is characterised by constant change and uncertainty. This is particularly true in project-oriented industries such as construction where subcontracting and competitive tendering add new dimensions to an already uncertain working environment. Many management writers and practitioners argue that the changing business environment and the speed required to design, develop and market products and services will lead to increasing use of project management in the future. This means that project management skills will become a competitive weapon for those individuals and firms that properly develop them.

A practical signal processing approach for condition monitoring of low speed machinery using Peak-Hold-Down-Sample algorithm

A simple and effective down-sample algorithm, Peak-Hold-Down-Sample (PHDS) algorithm is developed in this paper to enable a rapid and efficient data transfer in remote condition monitoring applications. The algorithm is particularly useful for high frequency Condition Monitoring (CM) techniques, and for low speed machine applications since the combination of the high sampling frequency and low rotating speed will generally lead to large unwieldy data size. The effectiveness of the algorithm was evaluated and tested on four sets of data in the study. One set of the data was extracted from the condition monitoring signal of a practical industry application. Another set of data was acquired from a low speed machine test rig in the laboratory. The other two sets of data were computer simulated bearing defect signals having either a single or multiple bearing defects. The results disclose that the PHDS algorithm can substantially reduce the size of data while preserving the critical bearing defect information for all the data sets used in this work even when a large down-sample ratio was used (i.e., 500 times down-sampled). In contrast, the down-sample process using existing normal down-sample technique in signal processing eliminates the useful and critical information such as bearing defect frequencies in a signal when the same down-sample ratio was employed. Noise and artificial frequency components were also induced by the normal down-sample technique, thus limits its usefulness for machine condition monitoring applications.

Susceptibility of the critical structural components of railway bridges to the changes in train speed

Modern trains with different axle configurations, speeds and loads are used in railway networks. As a result, one of the most important questions of the mangers involved in bridge managements systems (BMS) is how these changes affect the structural behavior of the critical components of the railway bridges. Although researchers have conducted, many investigations on the dynamic effects of the moving loads on bridges, the influence of the changes in the speed of the train on the demand by capacity ratios of the different critical components of the bridge have not yet been properly studied. This study is important, because different components with different capacities and roles for carrying loads in the structure may be affected differently. To investigate the above phenomenon in this research, a structural model of a simply supported bridge is developed. It will be verified that the dynamic behavior of this bridge is similar to a group of railway bridges in Australia. Demand by capacity ratios of the critical components of the bridge, when it is subjected to a train load with different speeds will be calculated. The results show that the effect of increase or decrease of speed should not be underestimated. The outcome is very significant as it is contrary to what is currently expected, i.e. by reducing the speed of the train, the demand by capacity ratio of components may increase and make the bridge unsafe for carrying live load.

BIM : enabling sustainability and asset management through knowledge management

Building Information Modeling (BIM) is the use of virtual building information models to develop building design solutions and design documentation and to analyse construction processes. Recent advances in IT have enabled advanced knowledge management, which in turn facilitates sustainability and improves asset management in the civil construction industry. There are several important qualifiers and some disadvantages of the current suite of technologies. This paper outlines the benefits, enablers, and barriers associated with BIM and makes suggestions about how these issues may be addressed. The paper highlights the advantages of BIM, particularly the increased utility and speed, enhanced fault finding in all construction phases, and enhanced collaborations and visualisation of data. The paper additionally identifies a range of issues concerning the implementation of BIM as follows: IP, liability, risks, and contracts and the authenticity of users. Implementing BIM requires investment in new technology, skills training, and development of new ways of collaboration and Trade Practices concerns. However, when these challenges are overcome, BIM as a new information technology promises a new level of collaborative engineering knowledge management, designed to facilitate sustainability and asset management issues in design, construction, asset management practices, and eventually decommissioning for the civil engineering industry.

Исследования и испытания алгоритмов автоматической пилотажно-навигационной системы малогабаритного беспилотного летательного аппарата = Research and testing of automatic navigation management system’s algorithms for small unmanned aerial vehicle

В статье представлено развитие принципа построения автоматической пилотажно-навигационной системы (АПНС) для беспилотного летательного аппарата (БЛА). Принцип заключается в синтезе комплексных систем управления БПЛА не только на основе использования алгоритмов БИНС, но и алгоритмов, объединяющих в себе решение задач формирования и отработки сформированной траектории резервированной системой управления и навигации. Приведены результаты аналитического исследования и данные летных экспериментов разработанных алгоритмов АПНС БЛА, обеспечивающих дополнительное резервирование алгоритмов навигации и наделяющих БЛА новым функциональной способностью по выходу в заданную точку пространства с заданной скоростью в заданный момент времени с учетом атмосферных ветровых возмущений. Предложена и испытана методика идентификации параметров воздушной атмосферы: направления и скорости W ветра. Данные летных испытаний полученного решения задачи терминальной навигации демонстрируют устойчивую работу синтезированных алгоритмов управления в различных метеоусловиях. The article presents a progress in principle of development of automatic navigation management system (ANMS) for small unmanned aerial vehicle (UAV). The principle defines a development of integrated control systems for UAV based on tight coupling of strap down inertial navigation system algorithms and algorithms of redundant flight management system to form and control flight trajectory. The results of the research and flight testing of the developed ANMS UAV algorithms are presented. The system demonstrates advanced functional redundancy of UAV guidance. The system enables new UAV capability to perform autonomous multidimensional navigation along waypoints with controlled speed and time of arrival taking into account wind. The paper describes the technique for real-time identification of atmosphere parameters such as wind direction and wind speed. The flight test results demonstrate robustness of the algorithms in diverse meteorological conditions.

Acceptability-based QoE management for user-centric mobile video delivery : a field study evaluation

Effective Quality of Experience (QoE) management for mobile video delivery – to optimize overall user experience while adapting to heterogeneous use contexts – is still a big challenge to date. This paper proposes a mobile video delivery system to emphasize the use of acceptability as the main indicator of QoE to manage the end-to-end factors in delivering mobile video services. The first contribution is a novel framework for user-centric mobile video system that is based on acceptability-based QoE (A-QoE) prediction models, which were derived from comprehensive subjective studies. The second contribution is results from a field study that evaluates the user experience of the proposed system during realistic usage circumstances, addressing the impacts of perceived video quality, loading speed, interest in content, viewing locations, network bandwidth, display devices, and different video coding approaches, including region-of-interest (ROI) enhancement and center zooming

Fault detection of wind turbine drivetrain utilizing power-speed characteristics

Wind energy, being the fastest growing renewable energy source in the present world, requires a large number of wind turbines to transform wind energy into electricity. One factor driving the cost of this energy is the reliable operation of these turbines. Therefore, it is a growing requirement within the wind farm community, to monitor the operation of the wind turbines on a continuous basis so that a possible fault can be detected ahead of time. As the wind turbine operates in an environment of constantly changing wind speed, it is a challenging task to design a fault detection technique which can accommodate the stochastic operational behavior of the turbines. Addressing this issue, this paper proposes a novel fault detection criterion which is robust against operational uncertainty, as well as having the ability to quantify severity level specifically of the drivetrain abnormality within an operating wind turbine. A benchmark model of wind turbine has been utilized to simulate drivetrain fault condition and effectiveness of the proposed technique has been tested accordingly. From the simulation result it can be concluded that the proposed criterion exhibits consistent performance for drivetrain faults for varying wind speed and has linear relationship with the fault severity level.

Pull the plug or take the plunge: Multiple opportunities and the speed of venturing decisions in the Australian mining industry

Effectively capturing opportunities requires rapid decision-making. We investigate the speed of opportunity evaluation decisions by focusing on firms' venture termination and venture advancement decisions. Experience, standard operating procedures, and confidence allow firms to make opportunity evaluation decisions faster; we propose that a firm's attentional orientation, as reflected in its project portfolio, limits the number of domains in which these speed-enhancing mechanisms can be developed. Hence firms' decision speed is likely to vary between different types of decisions. Using unique data on 3,269 mineral exploration ventures in the Australian mining industry, we find that firms with a higher degree of attention toward earlier-stage exploration activities are quicker to abandon potential opportunities in early development but slower to do so later, and that such firms are also slower to advance on potential opportunities at all stages compared to firms that focus their attention differently. Market dynamism moderates these relationships, but only with regard to initial evaluation decisions. Our study extends research on decision speed by showing that firms are not necessarily fast or slow regarding all the decisions they make, and by offering an opportunity evaluation framework that recognizes that decision makers can, in fact often do, pursue multiple potential opportunities simultaneously.

Network security metrics and performance for healthcare systems management

While enhanced cybersecurity options, mainly based around cryptographic functions, are needed overall speed and performance of a healthcare network may take priority in many circumstances. As such the overall security and performance metrics of those cryptographic functions in their embedded context needs to be understood. Understanding those metrics has been the main aim of this research activity. This research reports on an implementation of one network security technology, Internet Protocol Security (IPSec), to assess security performance. This research simulates sensitive healthcare information being transferred over networks, and then measures data delivery times with selected security parameters for various communication scenarios on Linux-based and Windows-based systems. Based on our test results, this research has revealed a number of network security metrics that need to be considered when designing and managing network security for healthcare-specific or non-healthcare-specific systems from security, performance and manageability perspectives. This research proposes practical recommendations based on the test results for the effective selection of network security controls to achieve an appropriate balance between network security and performance