Developing a model for construction contractors pre-qualification in the Gaza Strip and West Bank
El Sawalhi, NIH 2007, Developing a model for construction contractors pre-qualification in the Gaza Strip and West Bank , PhD thesis, Salford : University of Salford.
Restricted to Repository staff only until 03 October 2014.
Download (15MB) | Request a copy
Contractor pre-qualification is characterised as a multi-criteria problem with uncertain inputs. The criteria used for pre-qualification includes qualitative and quantitative information. Owing to the nature of pre-qualification, which depends on subjective judgements of construction professionals, it becomes an art rather than a science. Two approaches are found in the literature to model the contractor's pre-qualification criteria; Linear and non-linear models. The main aim of this research is to offer a rational method for contractor prequalification that enables to pre-qualify the contractors who are able to achieve the client's objectives. The main question guiding the research is how to be sure that the selected contractor is able to achieve the client's objectives. It is believed that there is an indirect relationship between the contractor's attributes and the contractor's ability to achieve the client's objectives. The time, cost and quality overruns of a project have been used as indicators to measure the contractor's ability to achieve client's objectives. To achieve this aim, the methodologies used included literature review, questionnaires, surveys, and hypothetical and real-life case studies. This work suggested improvements to the previous contractor pre-qualification models by using a hybrid model, combining the merits of Analytical Hierarchy Process (AHP), Neural Networks (NN) and Genetic Algorithms (GA) in one consolidated model called the Genetic Neural Network (GNN) model. AHP was used to establish relative weights of the contractor's pre-qualification criteria; NN was used as the main processing tool to find a relationship between the contractor's attributes and his performance. The GA was used to select the appropriate topology of the network. The data collected from questionnaires 1 and 2 were utilized to establish relative weights of contractors attributes. Hypothetical and real-life case studies from executed projects in the Gaza Strip and West Bank were collected through structured questionnaires. The actual evaluation of the contractor's attributes and the actual performance of the contractor in these projects in terms of overrun of time, cost and quality were collected. The weighted attributes were used as inputs to the GNN model. The corresponding time, cost, and quality overruns for the same case were fed as outputs to the GNN model in a supervised learning back propagation neural network. The adopted training and testing processes to develop a trained model are presented. The accuracy of the model was investigated using Average Absolute Error *^ (AAE), Mean Square Error (MSE) and correlation co-efficient (R ). The factors: AAE; MSE; and R2 showed a very good accuracy when comparing model prediction with actual real-life cases. The results revealed that there is a satisfactory relationship between the contractor attributes and the corresponding performance in terms of contractor's deviation from the client objectives. The GNN model is able to predict future contractor performance in terms of time, cost, and quality overruns. Therefore, the evolved model is able to predict the contractor performance. Key words: Pre-qualification, Contractors, Neural Networks, Genetic Algorithm, Model, Contractor Performance.
|Item Type:||Thesis (PhD)|
|Schools:||Colleges and Schools > College of Science & Technology > School of the Built Environment > Research Centre for Education in the Built Environment (RCEBE)|
Colleges and Schools > College of Science & Technology > School of the Built Environment
|Depositing User:||Institutional Repository|
|Date Deposited:||03 Oct 2012 14:34|
|Last Modified:||19 Feb 2014 13:19|
Actions (login required)
|Edit record (repository staff only)|