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Akintoye, A and Fitzgerald, E (2000) A survey of current cost estimating practices in the UK. Construction Management and Economics, 18(02), 161-72.
Austin, S A, Baldwin, A N, Baizhan, L and Waskett, P (2000) Analytical design planning technique (ADePT): a dependency structure matrix tool to schedule the building design process. Construction Management and Economics, 18(02), 173-82.
Bresnen, M and Marshall, N (2000) Partnering in construction: a critical review of issues, problems and dilemmas. Construction Management and Economics, 18(02), 229-37.
Dainty, A R J, Bagilhole, B M and Neale, R H (2000) A grounded theory of women's career under-achievement in large UK construction companies. Construction Management and Economics, 18(02), 239-50.
Goh, B-H (2000) Evaluating the performance of combining neural networks and genetic algorithms to forecast construction demand: the case of the Singapore residential sector. Construction Management and Economics, 18(02), 209-17.
- Type: Journal Article
- Keywords: accuracy; construction demand; forecasting; genetic algorithms; neural networks
- ISBN/ISSN: 0144-6193
- URL: https://doi.org/10.1080/014461900370834
- Abstract:
In recent years, forecasting demand for residential construction in Singapore has become more vital, since it is widely perceived that the next trough of the real estate cycle is approaching. This paper evaluates the use of a combination of neural networks (NNs) and genetic algorithms (GAs) to forecast residential construction demand in Singapore. Successful applications of NNs, especially in solving complex non-linear problems, have since stimulated interest in exploring the capabilities of other biological-based methods such as GAs, and in exploiting the synergy of these two techniques to create more problem-solving power. In the study, a basic NN model is used as a benchmark to gauge the performance of the combined NN-GA model. A relative measure of forecasting accuracy, known as the mean absolute percentage error (MAPE), is used for the comparison. The models are checked also for internal validity by allowing each to be trained twice and having a set of forecasts generated after each training. Both models are found to produce accurate forecasts, because their MAPE values consistently fall within the acceptable limit of 10%. However, the combined model out-performs the basis model remarkably by reducing the average MAPE from about 6% to a mere 1%. For each model, the marginal difference in the MAPE values (i.e., 0.5% for the NN model and 0.06% for the NN-GA model) of its two forecasts indicates consistency in performance, hence establishing internal validity as well. The findings reinforce the reliability of using NNs to model construction demand and reveal the benefit of combining NNs and GAs to produce more accurate models.
Love, P E D and Li, H (2000) Overcoming the problems associated with quality certification. Construction Management and Economics, 18(02), 139-49.
Pietroforte, R, Bon, R and Gregori, T (2000) Regional development and construction in Italy: an input-output analysis, 1959-1992. Construction Management and Economics, 18(02), 151-9.
Smith, S D, Wood, G S and Gould, M (2000) A new earthworks estimating methodology. Construction Management and Economics, 18(02), 219-28.
Sobotka, A (2000) Simulation modelling for logistics re-engineering in the construction company. Construction Management and Economics, 18(02), 183-95.
Wang, S Q, Tiong, R L K, Ting, S K and Ashley, D (2000) Evaluation and management of foreign exchange and revenue risks in China's BOT projects. Construction Management and Economics, 18(02), 197-207.
Wong, K-C and Walker, A (2000) Property rights implications of public-private joint ventures. Construction Management and Economics, 18(02), 131-8.