Local on-ramp queue management strategy with mainline speed recovery

Ramp metering is an effective motorway control tool beneficial for mainline traffic, but the long on-ramp queues created interfere with surface traffic profoundly. This study deals with the conflict between mainline benefits and thecosts of on-ramp and surface traffic. A novel local on-ramp queue management strategy with mainline speed recovery is proposed. Microscopic simulation is used to test the new strategy and compare it with other strategies. Simulation results reveal that the ramp metering with queue management strategy provides a good balance between the mainline and on-ramp performances.

A new look at the restrictions on the speed and magnitude of train loads for bridge management

In current bridge management systems (BMSs), load and speed restrictions are applied on unhealthy bridges to keep the structure safe and serviceable for as long as possible. But the question is, whether applying these restrictions will always decrease the internal forces in critical components of the bridge and enhance the safety of the unhealthy bridges. To find the answer, this paper for the first time in literature, looks into the design aspects through studying the changes in demand by capacity ratios of the critical components of a bridge under the train loads. For this purpose, a structural model of a simply supported bridge, whose dynamic behaviour is similar to a group of real railway bridges, is developed. Demand by capacity ratios of the critical components of the bridge are calculated, to identify their sensitivity to increase of speed and magnitude of live load. The outcomes of this study are very significant as they show that, on the contrary to what is expected, by applying restriction on speed, the demand by capacity ratio of components may increase and make the bridge unsafe for carrying live load. Suggestions are made to solve the problem.

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.