@article{(Open Science Index):https://publications.waset.org/pdf/10000298,
	  title     = {Construction Unit Rate Factor Modelling Using Neural Networks},
	  author    = {Balimu Mwiya and  Mundia Muya and  Chabota Kaliba and  Peter Mukalula},
	  country	= {},
	  institution	= {},
	  abstract     = {Factors affecting construction unit cost vary
depending on a country’s political, economic, social and
technological inclinations. Factors affecting construction costs have
been studied from various perspectives. Analysis of cost factors
requires an appreciation of a country’s practices. Identified cost
factors provide an indication of a country’s construction economic
strata. The purpose of this paper is to identify the essential factors
that affect unit cost estimation and their breakdown using artificial
neural networks. Twenty five (25) identified cost factors in road
construction were subjected to a questionnaire survey and employing
SPSS factor analysis the factors were reduced to eight. The 8 factors
were analysed using neural network (NN) to determine the
proportionate breakdown of the cost factors in a given construction
unit rate. NN predicted that political environment accounted 44% of
the unit rate followed by contractor capacity at 22% and financial
delays, project feasibility and overhead & profit each at 11%. Project
location, material availability and corruption perception index had
minimal impact on the unit cost from the training data provided.
Quantified cost factors can be incorporated in unit cost estimation
models (UCEM) to produce more accurate estimates. This can create
improvements in the cost estimation of infrastructure projects and
establish a benchmark standard to assist the process of alignment of
work practises and training of new staff, permitting the on-going
development of best practises in cost estimation to become more
effective.
},
	    journal   = {International Journal of Civil and Environmental Engineering},
	  volume    = {9},
	  number    = {1},
	  year      = {2015},
	  pages     = {29 - 34},
	  ee        = {https://publications.waset.org/pdf/10000298},
	  url   	= {https://publications.waset.org/vol/97},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 97, 2015},
	}