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Title: Tender price modelling : artificial neural networks and regression techniques
Author: Elhag, Taha Mahmoud Salih.
ISNI:       0000 0001 3444 0897
Awarding Body: University of Liverpool
Current Institution: University of Liverpool
Date of Award: 2004
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Cost modelling in construction is the art and science of developing a reliable and effective estimation of the tender price of a project. Cost estimation is an experiencebased task, which involves evaluations of unknown circumstances and complex relationships of cost-influencing factors. Researchers argue that cost model developments lack rigour and consistent conceptual framework within which the performance of different models may be compared and evaluated. This study analyses construction cost models by classifying them into three groups according to the techniques used. These include deterministic models (regression analysis); probabilistic models (Monte Carlo simulation); and artificial intelligence models (neural networks). This research investigates the development of two methodologies for tender price estimation of buildings utilising neural computing and regression techniques. The emphasis is to provide clients and practitioners with a reliable tool, which would offer trustworthy advice and prediction of tender prices at an early stage of a construction project. The analysis in this research is based upon a data set of 230 office projects, newly constructed in the UK between 1983 and 1997. The cost data of these buildings consists of tender prices and 13 other cost influencing factors. The data extracted using the Building Cost Information Service (BCIS) database of the Royal Institution of Chartered Surveyors (RICS). Questionnaire survey and interviews were adopted to identify, evaluate and rank cost significant factors according to their degree of influence on tender prices. The practitioners involved in this stage were UK based quantity surveyors. Some of these cost variables formulate the basis for developing the tender estimation models. Cluster analysis was conducted to categorise the data set into more homogeneous project groups based upon the cost variables. The hypothesis is that developing estimation models using project categories would yield better performance and more efficient models. Self-Organising Maps (SOM), a type of neural networks, is used for the cluster analysis. Seventeen neural networks and thirteen regression models are developed for tender price estimation using different parameters and cost factors. The performance and efficiency of these models are analysed and compared before and after the cluster analysis of the data set. On the other hand, sensitivity analysis is conducted by developing fifty-five models to evaluate the effectiveness of different combinationso f network parameterso n the accuracyo f tenderp rice estimation. The research findings indicate that, when the whole data set of 230 office projects is used, both methodologies produced low accuracy and failed to map the relationship between the tender price and the selected influencing cost factors. On the contrary, after clustering the data set into coherent groups using Kohonen neural networks, the performance of both RA and ANN models increased dramatically, with many estimation accuracies above 80% and 90%, which is highly satisfactory for tender price estimation at an early stage of a project. The outcomes imply that: (a) clustering the projects into homogeneous categories is significant and key for model performance and accuracy; (b) after cluster analysis there is no significant difference in the performance of RA and ANN models, although the RA outperformed the ANN in some models. The results also reveal that for both methodologies the accuracy of the estimation models that utilised two cost factors (project area and duration) outperformed the estimation models that used 13 cost factors, which is an indication that area and duration are the most dominant cost determinant variables.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available