Preserving and interpreting the mining company office : landscape, space and technological change in the management of the copper industry

The purpose of this research is to examine the role of the mining company office in the management of the copper industry in Michigan’s Keweenaw Peninsula between 1901 and 1946. Two of the largest and most influential companies were examined – the Calumet & Hecla Mining Company and the Quincy Mining Company. Both companies operated for more than forty years under general managers who were arguably the most influential people in the management of each company. James MacNaughton, general manager at Calumet and Hecla, worked from 1901 through 1941; Charles Lawton, general manager at Quincy Mining Company, worked from 1905 through 1946. In this case, both of these managers were college-educated engineers and adopted scientific management techniques to operate their respective companies. This research focused on two main goals. The first goal of this project was to address the managerial changes in Michigan’s copper mining offices of the early twentieth century. This included the work of MacNaughton and Lawton, along with analysis of the office structures themselves and what changes occurred through time. The second goal of the project was to create a prototype virtual exhibit for use at the Quincy Mining Company office. A virtual exhibit will allow visitors the opportunity to visit the office virtually, experiencing the office as an office worker would have in the early twentieth century. To meet both goals, this project used various research materials, including archival sources, oral histories, and material culture to recreate the history of mining company management in the Copper Country.

Challenging masculinity in CSR disclosures:silencing of women’s voices in Tanzania’s mining industry

This paper presents a feminist analysis of corporate social responsibility (CSR) in a male-dominated industry within a developing country context. It seeks to raise awareness of the silencing of women’s voices in CSR reports produced by mining companies in Tanzania. Tanzania is one of the poorest countries in Africa, and women are often marginalised in employment and social policy considerations. Drawing on work by Hélène Cixous, a post-structuralist/radical feminist scholar, the paper challenges the masculinity of CSR discourses that have repeatedly masked the voices and concerns of ‘other’ marginalised social groups, notably women. Using interpretative ethnographic case studies, the paper provides much-needed empirical evidence to show how gender imbalances remain prevalent in the Tanzanian mining sector. This evidence draws attention to the dynamics faced by many women working in or living around mining areas in Tanzania. The paper argues that CSR, a discourse enmeshed with the patriarchal logic of the contemporary capitalist system, is entangled with tensions, class conflicts and struggles which need to be unpacked and acknowledged. The paper considers the possibility of policy reforms in order to promote gender balance in the Tanzanian mining sector and create a platform for women’s concerns to be voiced.

Improving the management of building life cycle : a data mining approach

The construction industry has adapted information technology in its processes in terms of computer aided design and drafting, construction documentation and maintenance. The data generated within the construction industry has become increasingly overwhelming. Data mining is a sophisticated data search capability that uses classification algorithms to discover patterns and correlations within a large volume of data. This paper presents the selection and application of data mining techniques on maintenance data of buildings. The results of applying such techniques and potential benefits of utilising their results to identify useful patterns of knowledge and correlations to support decision making of improving the management of building life cycle are presented and discussed.

Using data mining to improve building life cycle

The building life cycle process is complex and prone to fragmentation as it moves through its various stages. The number of participants, and the diversity, specialisation and isolation both in space and time of their activities, have dramatically increased over time. The data generated within the construction industry has become increasingly overwhelming. Most currently available computer tools for the building industry have offered productivity improvement in the transmission of graphical drawings and textual specifications, without addressing more fundamental changes in building life cycle management. Facility managers and building owners are primarily concerned with highlighting areas of existing or potential maintenance problems in order to be able to improve the building performance, satisfying occupants and minimising turnover especially the operational cost of maintenance. In doing so, they collect large amounts of data that is stored in the building’s maintenance database. The work described in this paper is targeted at adding value to the design and maintenance of buildings by turning maintenance data into information and knowledge. Data mining technology presents an opportunity to increase significantly the rate at which the volumes of data generated through the maintenance process can be turned into useful information. This can be done using classification algorithms to discover patterns and correlations within a large volume of data. This paper presents how and what data mining techniques can be applied on maintenance data of buildings to identify the impediments to better performance of building assets. It demonstrates what sorts of knowledge can be found in maintenance records. The benefits to the construction industry lie in turning passive data in databases into knowledge that can improve the efficiency of the maintenance process and of future designs that incorporate that maintenance knowledge.

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.

Barriers and drivers of new public-private infrastructure: sewer mining

The rate of water reform in Australia is gathering pace with Federal and State initiatives promoting a more integrated approach to water management. This approach encompasses a more competitive environment and a greater role for the private sector. There is a growing recognition of the importance of water recycling in these initiatives and the need to provide opportunities for its development. In March 2008 the Productivity Commission published its discussion paper on urban water reform (Productivity Commission, 2008). The paper cited inadequate institutional arrangements for the management of Australian urban water resources and noted the benefits to be gained from a comprehensive public review of urban water management. This development can be supported through the promotion of a sewer mining industry. This industry, offers flexible and innovative solutions to water recycling demands in a variety of situations and structures. In addition it has the capability of satisfying government competition and private sector policy initiatives.

Industry Partners

AIMM stands for 'Agents for Improved Maintenance Management.' The AIMM system is a prototype tool that has developed the state of the art life cycle modelling of buildings through the linking of a 3D model with maintenance data to allow both the facility manager and the designer to gain access to building maintenance information and knowledge that is currently inaccessible. AIMM integrates data mining agents into the maintenance process to produce timely data for the facility manager on the effects of different maintenance regimes.

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.

A role mining inspired approach to representing user behaviour in ERP systems

Despite all attempts to prevent fraud, it continues to be a major threat to industry and government. Traditionally, organizations have focused on fraud prevention rather than detection, to combat fraud. In this paper we present a role mining inspired approach to represent user behaviour in Enterprise Resource Planning (ERP) systems, primarily aimed at detecting opportunities to commit fraud or potentially suspicious activities. We have adapted an approach which uses set theory to create transaction profiles based on analysis of user activity records. Based on these transaction profiles, we propose a set of (1) anomaly types to detect potentially suspicious user behaviour and (2) scenarios to identify inadequate segregation of duties in an ERP environment. In addition, we present two algorithms to construct a directed acyclic graph to represent relationships between transaction profiles. Experiments were conducted using a real dataset obtained from a teaching environment and a demonstration dataset, both using SAP R/3, presently the most predominant ERP system. The results of this empirical research demonstrate the effectiveness of the proposed approach.

Effective pattern taxonomy mining in text documents

Many data mining techniques have been proposed for mining useful patterns in databases. However, how to effectively utilize discovered patterns is still an open research issue, especially in the domain of text mining. Most existing methods adopt term-based approaches. However, they all suffer from the problems of polysemy and synonymy. This paper presents an innovative technique, pattern taxonomy mining, to improve the effectiveness of using discovered patterns for finding useful information. Substantial experiments on RCV1 demonstrate that the proposed solution achieves encouraging performance.

Transaction mining for fraud detection in ERP Systems

Despite all attempts to prevent fraud, it continues to be a major threat to industry and government. Traditionally, organizations have focused on fraud prevention rather than detection, to combat fraud. In this paper we present a role mining inspired approach to represent user behaviour in Enterprise Resource Planning (ERP) systems, primarily aimed at detecting opportunities to commit fraud or potentially suspicious activities. We have adapted an approach which uses set theory to create transaction profiles based on analysis of user activity records. Based on these transaction profiles, we propose a set of (1) anomaly types to detect potentially suspicious user behaviour, and (2) scenarios to identify inadequate segregation of duties in an ERP environment. In addition, we present two algorithms to construct a directed acyclic graph to represent relationships between transaction profiles. Experiments were conducted using a real dataset obtained from a teaching environment and a demonstration dataset, both using SAP R/3, presently the predominant ERP system. The results of this empirical research demonstrate the effectiveness of the proposed approach.

Dragline automation - a decade of development : shared autonomy for improving mining equipment productivity

Draglines are massive machines commonly used in surface mining to strip overburden, revealing the targeted minerals for extraction. Automating some or all of the phases of operation of these machines offers the potential for significant productivity and maintenance benefits. The mining industry has a history of slow uptake of automation systems due to the challenges contained in the harsh, complex, three-dimensional (3D), dynamically changing mine operating environment. Robotics as a discipline is finally starting to gain acceptance as a technology with the potential to assist mining operations. This article examines the evolution of robotic technologies applied to draglines in the form of machine embedded intelligent systems. Results from this work include a production trial in which 250,000 tons of material was moved autonomously, experiments demonstrating steps towards full autonomy, and teleexcavation experiments in which a dragline in Australia was tasked by an operator in the United States.

Mining agreements : negotiated frameworks in the Australian minerals sector

Discusses the role of negotiated frameworks as a regulatory mechanism in the development of Australia's premier industry of the 20th century.