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Al-Ameen, H A (2012) Antitrust: the person-centred approach, Unpublished PhD Thesis, , Swansea University.

Beynon, K S (2005) Dispute resolution and access to justice, with particular reference to the construction industry in the United Kingdom, Unpublished PhD Thesis, Department of Law, Swansea University.

Dubey, K K (2001) A framework for analysing quality in the US homebuilding industry, Unpublished PhD Thesis, (?)Glamorgan Business School, Swansea University.

Farran, H H (1993) A study of the implementation and impact of turn-key management contracts in hospitals in the kingdom of Saudi Arabia, Unpublished PhD Thesis, , Swansea University.

Hamour, O A A R (1978) Project selection and planning in developing countries, Unpublished PhD Thesis, , Swansea University.

Massoudi, A R (1995) Utilization of plant condition monitoring with reference to the Iranian construction industry, Unpublished PhD Thesis, Department of Mechanical Engineering, Swansea University.

Salman, S H (1991) Planning and control in the small business: Case studies from construction industry, Unpublished PhD Thesis, , Swansea University.

Sandhaus, G (1998) Neural networks for cost estimating in project management, Unpublished PhD Thesis, , Swansea University.

  • Type: Thesis
  • Keywords: artificial intelligence; estimating; learning; neural network
  • ISBN/ISSN:
  • URL: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.638763
  • Abstract:
    The purpose of this thesis is to evaluate whether neural networks can be used for cost estimating in project management and if so, whether they lead to improved estimates. Chapter one gives a short introduction into the field of artificial intelligence and describes the structure and learning algorithm of a selection of some typical neural networks. Chapter two reviews literature regarding cost estimating in project management and differentiates between different methods of cost estimates. Chapter three describes the statistical problem of parametric cost estimates and addresses these problems with the help of neural networks. Chapter four and Chapter five give examples of how neural networks can be used to represent mathematical equations which are currently used for cost estimating in project management. Chapter six applies neural networks on real world data and compares its performance with one of the leading parametric cost estimating software tools. Chapter seven includes the discussion and conclusions of the findings throughout the thesis. The limitation and restriction of the implementation of neural networks are examined and the potentials for further research are suggested.