No description available.

Thesis (M. Sc.)--University of Hong Kong, 1978.

No description available.

Thesis (Ph.D.)(Civil and Biosystems Engineering)--University of Pretoria, 2005. / Includes bibliographical references. Includes bibliographical references. Available on the Internet via the World Wide Web.

M.Ing. (Engineering Management) / Please refer to full text to view abstract

No description available.

Heavy construction and mining general contractors record on a daily basis large amount of operational data. Nevertheless, this information is rarely used to enhance the knowledge and capabilities of the companies that spent great amount of money and resources recording it. This research presents different approaches on how to process this data to convert it in useful information. The prime goal of this analysis is to determine a suitable and convenient method to obtain and present historical productivities of key equipment, in order to provide a tool to aid estimating and generate reference information to support decision making. Data mining, artificial neural networks and summarization tools proved to assist effectively in the assessment of historical productivities and in the identification of the attributes that most influence the results. / Construction Engineering and Management

This dissertation analyzed data and the competitiveness model of the Taiwanese earthmoving equipment industry. Competitiveness in this industry is the most important factor impacting the struggle of businesses for existence. Thus, understanding factors affecting competitiveness is important for business decision-makers. / Thesis (PhD)--University of South Australia, 2008.

Thesis (PhD)--University of Stellenbosch, 2004. / Bibliography. Also available via the Internet.

The management of heavy construction equipment is a difficult task. Equipment managers are often called upon to make complex economic decisions involving the machines in their charge. These decisions include those concerning acquisitions, maintenance, repairs, rebuilds, replacements, and retirements. The equipment manager must also be able to forecast internal rental rates for their machinery. Repair and maintenance expenditures can have significant impacts on these economic decisions and forecasts. The purpose of this research was to identify a regression model that can adequately represent repair costs in terms of machine age in cumulative hours of use. The study was conducted using field data on 270 heavy construction machines from four different companies. Nineteen different linear and transformed non-linear models were evaluated. A second-order polynomial expression was selected as the best. It was demonstrated how this expression could be incorporated in the Cumulative Cost Model developed by Vorster where it can be used to identify optimum economic decisions. It was also demonstrated how equipment managers could form their own regression equations using standard spreadsheet and database software. / Ph. D.