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Predicting the performance of traditional general contract projects: A neural network based approach

Several studies had shown that many project managers are facing difficulties in predicting the performance of Traditional General Contract (TGC) projects. This is due to the fact that there are many factors that affect TGC project success. This paper presents the TGS project success factors that hav...

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محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Mohamad Zin, Rosli, Mansur, S. A., Bakri, A., Tan, Caren Cai Loon
التنسيق: Conference or Workshop Item
اللغة:English
منشور في: 2006
الموضوعات:
TA Engineering (General). Civil engineering (General)
الوصول للمادة أونلاين:http://eprints.utm.my/id/eprint/549/1/RosliMohamadZin2006_Predictingtheperformanceoftraditionalgeneral.pdf
http://eprints.utm.my/id/eprint/549/
http://civil.utm.my/apsec2015/
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id my.utm.549
record_format eprints
spelling my.utm.5492017-08-23T00:31:17Z http://eprints.utm.my/id/eprint/549/ Predicting the performance of traditional general contract projects: A neural network based approach Mohamad Zin, Rosli Mansur, S. A. Bakri, A. Tan, Caren Cai Loon TA Engineering (General). Civil engineering (General) Several studies had shown that many project managers are facing difficulties in predicting the performance of Traditional General Contract (TGC) projects. This is due to the fact that there are many factors that affect TGC project success. This paper presents the TGS project success factors that have been identified. In addition, a model to predict the performance of TGC project based on time is also described. Through literature research, a total of forty-four factors affecting TGC project success had been established. The degree of importance for these factors was determined through questionnaire survey. The outcome of the survey formed a basis for the development of the project performance prediction model. The best model was found to be a multi-layer back-propagation neural network consists of eight input nodes, five hidden nodes and three output nodes. The model was tested by using data from nine new projects. The results showed that the mean error for this prediction model is relatively low. The model enables all parties involved in TGC projects to predict and ensure that their project performance is within the time constraints. 2006-09 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/549/1/RosliMohamadZin2006_Predictingtheperformanceoftraditionalgeneral.pdf Mohamad Zin, Rosli and Mansur, S. A. and Bakri, A. and Tan, Caren Cai Loon (2006) Predicting the performance of traditional general contract projects: A neural network based approach. In: 6th Asia-Pacific Structural Engineering and Construction Conference, 5-6 September 2006, Kuala Lumpur, Malaysia. http://civil.utm.my/apsec2015/
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Mohamad Zin, Rosli
Mansur, S. A.
Bakri, A.
Tan, Caren Cai Loon
Predicting the performance of traditional general contract projects: A neural network based approach
description Several studies had shown that many project managers are facing difficulties in predicting the performance of Traditional General Contract (TGC) projects. This is due to the fact that there are many factors that affect TGC project success. This paper presents the TGS project success factors that have been identified. In addition, a model to predict the performance of TGC project based on time is also described. Through literature research, a total of forty-four factors affecting TGC project success had been established. The degree of importance for these factors was determined through questionnaire survey. The outcome of the survey formed a basis for the development of the project performance prediction model. The best model was found to be a multi-layer back-propagation neural network consists of eight input nodes, five hidden nodes and three output nodes. The model was tested by using data from nine new projects. The results showed that the mean error for this prediction model is relatively low. The model enables all parties involved in TGC projects to predict and ensure that their project performance is within the time constraints.
format Conference or Workshop Item
author Mohamad Zin, Rosli
Mansur, S. A.
Bakri, A.
Tan, Caren Cai Loon
author_facet Mohamad Zin, Rosli
Mansur, S. A.
Bakri, A.
Tan, Caren Cai Loon
author_sort Mohamad Zin, Rosli
title Predicting the performance of traditional general contract projects: A neural network based approach
title_short Predicting the performance of traditional general contract projects: A neural network based approach
title_full Predicting the performance of traditional general contract projects: A neural network based approach
title_fullStr Predicting the performance of traditional general contract projects: A neural network based approach
title_full_unstemmed Predicting the performance of traditional general contract projects: A neural network based approach
title_sort predicting the performance of traditional general contract projects: a neural network based approach
publishDate 2006
url http://eprints.utm.my/id/eprint/549/1/RosliMohamadZin2006_Predictingtheperformanceoftraditionalgeneral.pdf
http://eprints.utm.my/id/eprint/549/
http://civil.utm.my/apsec2015/
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score 13.251813

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