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Ahmed, S M, Ahmad, R and Saram, D D D (1999) Risk management trends in the Hong Kong construction industry: a comparison of contractors and owners perceptions. Engineering, Construction and Architectural Management, 6(03), 225–34.
Boussabaine, A H, Thomas, R and Elhag, T M S (1999) Modelling cost-flow forecasting for water pipeline projects using neural networks. Engineering, Construction and Architectural Management, 6(03), 213–24.
Dissanayaka, S M and Kumaraswamy, M M (1999) Evaluation of factors affecting time and cost performance in Hong Kong building projects. Engineering, Construction and Architectural Management, 6(03), 287–98.
Gunner, J and Skitmore, M R (1999) Pre-bid building price forecasting accuracy: price intensity theory. Engineering, Construction and Architectural Management, 6(03), 267–75.
Jaafari, A and Manivong, K (1999) The need for life-cycle integration of project processes. Engineering, Construction and Architectural Management, 6(03), 235–55.
Kaka, A P (1999) The development of a benchmark model that uses historical data for monitoring the progress of current construction projects. Engineering, Construction and Architectural Management, 6(03), 256–66.
Kartam, N A (1999) Design/construction integration: issues and illustrative prototype. Engineering, Construction and Architectural Management, 6(03), 299–314.
Khosrowshahi, F (1999) Neural network model for contractors' pre-qualification for local authority projects. Engineering, Construction and Architectural Management, 6(03), 315–28.
- Type: Journal Article
- Keywords:
- ISBN/ISSN: 0969-9988
- URL: http://www.blackwell-synergy.com/links/doi/10.1046/j.1365-232x.1999.00115.x/abs
- Abstract:
The way in which clients or their consultants undertake to select firms to tender for a given project is a highly complex process and can be very problematic. This is also true for public authorities as, for them, 'compulsory competitive tendering' is a relatively new concept. Despite its importance, contractors' pre-qualification is often based on heuristic techniques combining experience, judgement and intuition of the decision-makers. This, primarily, stems from the fact that pre-qualification is not an exact science. For any project, the right choice of the contractor is one of the most important decisions that the client has to make. Therefore, it is envisaged that the development of an effective decision-support model for contractor pre-qualification can yield significant benefits to the client. By implication, such a model can also be of considerable use to contractors: a model of this nature is an effective marketing tool for contractors to enhance their chances of success to obtain new work. To this end, this work offers a decision-support model that predicts whether or not a contractor should be selected for tendering projects. The focus is on local authorities because, in the absence of a viable universal selection system, there are significant variations in the way they conduct pre-qualification. The model is based on the use of artificial neural networks (ANN) and uses data relating to 42 local authorities (clients). With the aid of a questionnaire and a scaling system, the pre-qualification attributes that are considered to be important by clients are identified. The survey indicates significant variations in the level of importance given to different attributes. Statistical methods are adopted to generate additional data representing disqualified instances. Following a pre-processing exercise, the data form the basis of the input and output layers for training the neural-net model. An independent set of data is subjected to a similar pre-processing for testing the model. Tests reveal that the model has a highly satisfactory predictive accuracy and that the ANN technique is a viable tool for the prediction of success or failure of the contractor to qualify to tender for local authority projects.
Pasquire, C (1999) The implications of environmental issues on UK construction management. Engineering, Construction and Architectural Management, 6(03), 276–86.