A primer in public policy for the construction industry

The purpose of this document is to introduce non-specialists to the discipline and practice of public policy, particularly in relation to the construction sector in Australia. In order to do this, a brief overview of Australia’s government structure, and some of the main approaches to public policy analysis are outlined. Reference to construction related examples are provided to ensure issues discussed are relevant and understandable to construction professionals. Government is a significant player in the construction industry, and has multiple roles: adjudicator, regulator, constructor, purchaser and client of construction projects. Moreover there are many spheres of government that are typically engaged in construction projects at multiple stages. The machinery of government can be difficult to understand, even for long term public servants. Demystifying the processes within government can help to improve communication and therefore performance in the industry. A better understanding of how policy-making and government policies affect the construction industry will enhance communication and assist construction professionals and academics to understand and work with government. Additionally the document will provide an opportunity to demonstrate the relevance of policy analysis to inquiries of construction policies and regulation.

Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure 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 for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

Safety impacts of alcohol and other drugs in the construction industry : a research methodology

Anecdotal evidence from the infrastructure and building sectors highlights issues of drugs and alcohol and its association with safety risk on construction sites. Operating machinery and mobile equipment, proximity to live traffic together with congested sites, electrical equipment and operating at heights conspire to accentuate the potential adverse impact of drugs and alcohol in the workplace. While most Australian jurisdictions have identified this as a critical safety issue, information is limited regarding the prevalence of alcohol and other drugs in the workplace and there is limited evidential guidance regarding how to effectively and efficiently address such an issue. No known study has scientifically evaluated the relationship between the use of drugs and alcohol and safety impacts in construction, and there has been only limited adoption of nationally coordinated strategies, supported by employers and employees to render it socially unacceptable to arrive at a construction workplace with impaired judgement from drugs and alcohol. A nationally consistent collaborative approach across the construction workforce - involving employers and employees; clients; unions; contractors and sub-contractors is required to engender a cultural change in the construction workforce – in a similar manner to the on-going initiative in securing a cultural change to drink-driving in our society where peer intervention and support is encouraged. This study has four key objectives. Firstly, using the standard World Health Organisation AUDIT, a national qualitative and quantitative assessment of the use of drugs and alcohol will be carried out. This will build upon similar studies carried out in the Australian energy and mining sectors. Secondly, the development of an appropriate industry policy will adopt a non-punitive and rehabilitative approach developed in consultation with employers and employees across the infrastructure and building sectors, with the aim it be adopted nationally for adoption at the construction workplace. Thirdly, an industry-specific cultural change management program will be developed through a nationally collaborative approach to reducing the risk of impaired performance on construction sites and increasing workers’ commitment to drugs and alcohol safety. Finally, an implementation plan will be developed from data gathered from both managers and construction employees. Such an approach stands to benefit not only occupational health and safety, through a greater understanding of the safety impacts of alcohol and other drugs at work, but also alcohol and drug use as a wider community health issue. This paper will provide an overview of the background and significance of the study as well as outlining the proposed methodology that will be used to evaluate the safety impacts of alcohol and other drugs in the construction industry.

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.

Gender, sexuality, and rurality in the mining industry

Sexuality is a subject that has been, at best, marginal in the significant body of literature that has examined gender and mining in contemporary Western nations. This is despite the fact that academics have circled, if not almost bumped into the topic in closely related discussions of hegemonic masculinity and mining work, and of patriarchal familial relations and mining communities. This scholarship has documented what has been and remains women’s primary relationship to mining—that is, as a “mining wife.” How patriarchal relations are manifest in and emerge from this state of affairs has been critiqued with research on the gendered implications of housing arrangements in mining towns, the division of household labor, changing shift-work mining rosters, and the gendered consequences of strikes and mine closures (Williams 1981; Gibson 1992; Gibson-Graham 1996; Rhodes 2005; McDonald, Mayes, and Pini 2012). Despite the centrality of the heterosexual relationship—and indeed heteronormativity—to these discussions, scholars of gender and mining have had little to say on the subject of sexuality. In response to this lacuna, this chapter takes an exploratory lens to the subject of sexuality and the mining industry. We approach the task from the perspective that the mining industry is gendered as masculine. That is, definitions of mining mobilize around masculinized notions of physicality, technical competence with machinery, and strength, as well as emphasize the harshness and dirtiness of the work (Mayes and Pini 2010).

Which industry is greener? An empirical study of nine industries in OECD countries

This study analyzed the relationship between the CO2 emissions of different industries and economic growth in OECD countries from 1970 to 2005. We tested an environmental Kuznets curve (EKC) hypothesis and found that total CO2 emissions from nine industries show an N-shaped trend instead of an inverted U or monotonic increasing trend with increasing income. The EKC hypothesis for sector-level CO2 emissions was supported in the (1) paper, pulp, and printing industry; (2) wood and wood products industry; and (3) construction industry. We also found that emissions from coal and oil increase with economic growth in the steel and construction industries. In addition, the non-metallic minerals, machinery, and transport equipment industries tend to have increased emissions from oil and electricity with economic growth. Finally, the EKC turning point and the relationship between GDP per capita and sectoral CO2 emissions differ among industries according to the fuel type used. Therefore, environmental policies for CO2 reduction must consider these differences in industrial characteristics. © 2013 Elsevier Ltd.

Vendors' perspectives of coordination in the information technology offshore outsourcing industry : An exploratory study from the Philippines

This study investigates how offshore information technology (IT) service providers (vendors) coordinate work with their clients (employers) in order to succeed in the global IT offshore outsourcing industry. We reviewed literature on coordination studies, interviewed offshore service providers in the Philippines, and used thematic analysis to analyse coordination practices from the point of view of these individual vendors in a newly industrialized country. We used Olson and Olson's framework on 'collaboration at a distance' as a lens to structure the results. The study provides an understanding of vendors' individual attitudes towards the coordination of distributed work and draws attention to how differences in power affect the work situation of vendors, and by implication all stakeholders. We offer this insight as a way to enhance existing CSCW frameworks, by imbuing them with the perspective of non-equal relationships. The study found that vendors were generally able to produce outputs that satisfy their clients, however these results were only achieved because individuals were willing to take risks and make sacrifices in their personal lives. The relationship was further characterised by a complex interplay between the client's control of the overall work arrangements and the vendors' ability to establish a level of autonomy in their work practices and their flexible use of coordination tools.