Abstracts – Browse Results
Click on the titles below to expand the information about each abstract.
Viewing 8 results ...
Anson, M, Tang, S L and Ying, K C (2002) Measurement of the performance of ready mixed concreting resources as data for system simulation. Construction Management and Economics, 20(03), 237-50.
Chang, C-Y and Ive, G (2002) Rethinking the multi-attribute utility approach based procurement route selection technique. Construction Management and Economics, 20(03), 275-84.
El-Rayes, K, Ramanthan, R and Moselhi, O (2002) An object-oriented model for planning and control of housing construction. Construction Management and Economics, 20(03), 201-10.
Kirkham, R J, Boussabaine, A H and Awwad, B H (2002) Probability distributions of facilities management costs for whole life cycle costing in acute care NHS hospital buildings. Construction Management and Economics, 20(03), 251-61.
Lingard, H (2002) The effect of first aid training on Australian construction workers' occupational health and safety knowledge and motivation to avoid work-related injury or illness. Construction Management and Economics, 20(03), 263-73.
Ng, S T, Luu, D T, Chen, S E and Lam, K C (2002) Fuzzy membership functions of procurement selection criteria. Construction Management and Economics, 20(03), 285-96.
Williams, T P (2002) Predicting completed project cost bidding data. Construction Management and Economics, 20(03), 225-35.
- Type: Journal Article
- Keywords: bidding; project costs; neural networks; regression analysis
- ISBN/ISSN: 0144-6193
- URL: https://doi.org/10.1080/01446190110112838
- Abstract:
Neural network and regression models have been developed to predict the completed cost of competitively bid highway projects constructed by the New Jersey Department of Transportation. Bid information was studied for inclusion as inputs to the models. Data studied included the low bid, median bid, standard deviation of the bids, expected project duration and the number of bids. A natural log transformation of the data was found to improve the linear relationship between the low bid and completed cost. The stepwise regression procedure was applied, and yielded the best performing predictive model. This regression model used only the natural log of the low bid as independent variable to predict the natural log of the completed cost. Radial basis neural networks were also constructed to predict the final cost. The best performing regres4 sion model produced superior predictions to the best performing neural network model. Hybrid models that used a regression model prediction as an input to a neural network were also studied and were found to also produce reasonable predictions. The calculated models produced good predictions of the completed project cost, but were found to be deficient in predicting very large cost increases. Simple models using the natural log of the low bid as input produced the best results. From the analysis it may be concluded that additional information about the variability of the bids submitted does not provide useful information for predicting the final project outcome.
Yasamis, F, Arditi, D and Mohammadi, J (2002) Assessing contractor quality performance. Construction Management and Economics, 20(03), 211-23.