Project cost prediction model using principal component regression for public building projects in Nigeria
Major problem in Nigeria construction industry is that building contracts are completed at sums much higher than estimated cost, hence the need to develop predictive cost model that capture factors affecting project cost using principal components regression, through set objectives: to identify f...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit UKM
2010
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| Online Access: | http://journalarticle.ukm.my/2515/ http://journalarticle.ukm.my/2515/ http://journalarticle.ukm.my/2515/1/paper01.pdf |
| Summary: | Major problem in Nigeria construction industry is that building contracts are completed at
sums much higher than estimated cost, hence the need to develop predictive cost model
that capture factors affecting project cost using principal components regression, through
set objectives: to identify factors contributing to project cost; examine the importance of the
factors and develop cost predictive model. Literature review on the study indicated that
nature of clients, professional involved in a project and their decision regarding design,
function, duration, technology and implementation have significant effect on the overall
project cost. Data for the study are obtained through random sampling of public building
projects completed in Nigeria after 1995. The study identifies six most significant factors to
project cost among the design related variables as: Level of design complexity; level of
construction complexity; level of technological advancement; percentage of repetitive
element; presence of special issues and scope of work. Three factors among time/cost
related factors as Importance for project to be delivered; time allowed by the client and his
representative for bid evaluation; need for the project to be completed. Client, consultant
and contractor’s experience on similar project; adequacy of contractor’s plants and
equipments are most significant among project parties experience related factors. The
selected factors were used for cost predictive model. |
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