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Bee Hua, G (2008) The state of applications of quantitative analysis techniques to construction economics and management (1983 to 2006). Construction Management and Economics, 26(05), 485-97.
- Type: Journal Article
- Keywords: artificial intelligence; statistical method; quantitative analysis technique; bibliometrics; construction economics
- ISBN/ISSN: 0144-6193
- URL: https://doi.org/10.1080/01446190801998716
With increasing complexity of construction industry problems, researchers are experimenting with computationally rigorous techniques with the aim of seeking innovative solutions. In order to trace the applications of quantitative analysis techniques to research in the two fields of construction economics and construction management for both conventional and AI techniques, the methodology involves compiling all the relevant papers from the top two ranking construction management journals, namely, Construction Management and Economics and ASCE's Journal of Construction Engineering and Management. The period is from 1983 to 2006. The compiled papers are classified by field, area (or topic), technique applied and year of publication to enable time series and cross-sectional analyses of the data. Mainly, the results are depicted as trends when the patterns of distribution of the papers are plotted over time. The three findings are: (1) for construction economics, the overall increasing trend is higher for papers that have applied conventional techniques; (2) for construction management, there is a clear positive trend for papers that have applied AI techniques which starts from 1995; and (3) the areas (or topics) of construction management that have increasingly higher growth in the application of AI techniques are optimization of site operations and optimization of project time, cost and resources allocation. Two broad recommendations are made that relate to advancing the fields of construction economics and construction management with the view that researchers must better enable themselves to build tools that incorporate intelligence as innovative solutions for increasingly complex problems.