Developing a Model for Effective Management of Building Construction Projects

1. Alex Kalume
Sustainable Materials Research and Technology Centre (SMARTEC)
Jomo Kenyatta University of Agriculture and Technology
P.O. Box 62000-00100, Nairobi, Kenya
Corresponding e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

2. Stephen Diang’a
School of Architecture and Building Sciences (SABS)
Jomo Kenyatta University of Agriculture and Technology
P.O. Box 62000-00100, Nairobi, Kenya
E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.


Abstract
The construction industry is very important to the economy in terms of employment and wealth but it is faced with numerous challenges which necessitate the need of taking measures to improve the management of construction projects. Building projects are normally realized by inputs from different experts in their areas of specialization. The many parties involved in the construction process shows how difficult it is to manage the process to a successful completion. Majority of projects are not completed within the agreed time due to unforeseen circumstances not included at the estimating stage.

The study focused on developing tools for effective management of building construction projects in Kenya, as a means of helping to bring about improvements in project executions. Specifically, this is realized by developing regression models for realistic estimate of contract period. 
A survey of construction practitioners in Mombasa was conducted. The sample consists of architects, engineers, contractors and quantity surveyors. Information was obtained on past building projects performance in terms of time and determination of the degree of influence of various factors at every stage of a building project. Multiple regression technique was used in the analysis of data for the study.
Findings include regression models for realistic estimation of project time for residential houses, institutional buildings (education), industrial (factories), Hotel/motel and commercial (offices). These are: Commercial office – private, Final time = (0.04*initicost) + (1.308*initime) + (-0.005*floarea) + (-4.786*noflrs) + 17.262. Commercial office – public, Final time = (-0.951*noflrs) + (0.001*floarea) + (1.101*initime) + 12.631. Hotel/Motel – private, Final time = (-3.203*noflrs) + (0.001*floarea) + (1.206*initime) + 0.81*initicost + 3.378. Industrial factories – private, Final time = (6.782*noflrs) + (0.002*floarea) (1.161*initime) + (0.034*initicost) + (-21.524. Institutional education – private, Final time = (-2.933*noflrs) + (-0.003*floarea) + (0.959*initime) + (0.178*initicost) + 20.662. Institutional education – public, Final time = (-12.199*noflrs) + 0.001*floarea) + (1.184*initime) + (0.506*initicost) + 12.775. Residential houses – private, Final time = (9.604*noflrs) + (-0.013*floarea) + (0.949*initime) + (0.006*initicost) + 2.098. Residential houses – public, Final time = (-0.465*noflrs) + (0.005*floarea) + (1.039*initime) + (0.186*initicost) + 10.477.                                                             The regression equations for estimating the final contract period of projects in the construction industry are recommended for use. However, more data is required for developing the other models for hotel/motel, industrial (factories and commercial recreational centers.

Full Text [PDF]