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Barrett, P S and Barrett, L C (2003) Research as a kaleidoscope on practice. Construction Management and Economics, 21(07), 755-66.
Cho, Y (2003) The organizational boundaries of housebuilding firms in Korea. Construction Management and Economics, 21(07), 671-80.
Dainty, A R J and Edwards, D J (2003) The UK building education recruitment crisis: a call for action. Construction Management and Economics, 21(07), 767-75.
Kaka, A P and Lewis, J (2003) Development of a company-level dynamic cash flow forecasting model (DYCAFF). Construction Management and Economics, 21(07), 693-705.
Perttula, P, Merjama, J, Kiurula, M and Laitinen, H (2003) Accidents in materials handling at construction sites. Construction Management and Economics, 21(07), 729-36.
Phua, F T T and Rowlinson, S (2003) Cultural differences as an explanatory variable for adversarial attitudes in the construction industry: the case of Hong Kong. Construction Management and Economics, 21(07), 777-85.
Pongpeng, J and Liston, J (2003) A multicriteria model's survey: state of the art and some necessary capabilities of future models. Construction Management and Economics, 21(07), 665-70.
Shohet, I M (2003) Building evaluation methodology for setting maintenance priorities in hospital buildings. Construction Management and Economics, 21(07), 681-92.
Su, C-K, Lin, C-Y and Wang, M-T (2003) Taiwanese construction sector in a growing 'maturity' economy, 1964-1999. Construction Management and Economics, 21(07), 719-28.
Tam, C M, Fung, I W H, Yeung, T C L and Tung, K C F (2003) Relationship between construction safety signs and symbols recognition and characteristics of construction personnel. Construction Management and Economics, 21(07), 745-53.
Wanous, M, Boussabaine, A H and Lewis, J (2003) A neural network bid/no bid model: the case for contractors in Syria. Construction Management and Economics, 21(07), 737-44.
- Type: Journal Article
- Keywords: ANN; ANN bidding model; 'bid/no bid' criteria; construction; Syria
- ISBN/ISSN: 0144-6193
- URL: https://doi.org/10.1080/0144619032000093323
- Abstract:
Despite the crucial importance of the ’bid/no bid’ decision in the construction industry, it has been given little attention by researchers. This paper describes the development and testing of a novel bid/no bid model using the artificial neural network (ANN) technique. A back-propagation network consisting of an input buffer with 18 input nodes, two hidden layers and one output node was developed. This model is based on the findings of a formal questionnaire through which key factors that affect the ’bid/no bid’ decision were identified and ranked according to their importance to contractors operating in Syria. Data on 157 real-life bidding situations in Syria were used in training. The model was tested on another 20 new projects. The model wrongly predicted the actual bid/no bid decision only in two projects (10%) of the test sample. This demonstrates a high accuracy of the proposed model and the viability of neural network as a powerful tool for modelling the bid/no bid decision-making process. The model offers a simple and easy-to-use tool to help contractors consider the most influential bidding variables and to improve the consistency of the bid/ no bid decision-making process. Although the model is based on data from the Syrian construction industry, the methodology would suggest a much broader geographical applicability of the ANN technique on bid/no bid decisions.
Zhi, M, Hua, G B, Wang, S Q and Ofori, G (2003) Total factor productivity growth accounting in the construction industry of Singapore. Construction Management and Economics, 21(07), 707-18.