Lu, M (2000) Productivity studies using advanced ANN models. PhD thesis, University of Alberta, Canada.
Abstract
Estimating labor productivity is one of the most difficult aspects of preparing an estimate, or a control budget based on the estimate for labor-intensive activities in construction. The primary objective of research is developing artificial neural network or ANN based estimating tools to offer estimators valuable information about labor productivity in bidding new jobs. In conjunction with a major Canadian industrial contractor, the thesis research presents case studies on the theoretical basis and practical considerations for measuring and analyzing labor productivity in industrial construction. Two important activities of process piping were investigated: pipe installation in the field and spool fabrication in the fabrication shop. Emerging computer modeling techniques such as data warehouses and ANN were researched from an academic perspective and implemented in industry to meet the challenges in productivity studies. The thesis research has addressed: (1) how to quantify labor productivity in industrial construction from a contractor's point of view; (2) how to measure actual labor productivity in industrial construction based upon on-site control practices; and (3) how to utilize ANN to analyze the variability of actual labor production rates and the sensitivity of identified influencing factors. Using actual data, the proposed ANN models were proven to be effective in both risk analysis and sensitivity analysis of construction labor productivity. The developed data warehouses and ANN-based decision-support tools have been implemented or are in the process of implementation at the involved company. The final results of the research not only assist estimators to improve the accuracy of estimating labor production rates for studied activities in bidding new jobs, but also offer the management a precise and integrated view of corporate productivity information spanning across many business divisions. The experience and lessons learned from the successful, productive and mutually beneficial collaboration between academia and industry in the thesis research will potentially benefit other university-industry joint research projects in the future.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Abourizk, S M |
| Uncontrolled Keywords: | accuracy; artificial neural network; bidding; collaboration; estimating; fabrication; productivity; risk analysis; estimator; neural network; risk analysis; case studies |
| Index terms: | construction labour, labour productivity, academia, industrial construction, estimate, implementation, variability, fabrication, computer modelling, influencing factor, case study, productivity, collaboration, estimator, lessons learned, risk analysis, estimating, bidding, sensitivity analysis, neural network, accuracy, artificial neural network |
| Subjects: | profession, risk assessment, data collection methods, modelling and simulation, contractual arrangements, bidding, artificial intelligence, financial and cost management, management, manufacturing engineering, professional development, educational institutions, building construction, statistical analysis, environmental hazards |
| Topics: | Roles and Professions, Cost Management, Business Strategy, Research Practice, Information Management, Organizational Design, Site Management, Digital Applications, Risk Management, Procurement, Sustainability, Engineering Principles, Education |
| Descriptive scope: | 4 PCEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here