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Hassan Ahmadu

Hassan Ahmadu

Merit Award Winner 2014
Modelling Building Construction Durations in Nigeria

Research Abstract

There is now widespread acceptance in the research community that time prediction models for construction projects should take time-influencing qualitative and managerial factors into consideration.  Consequently, several multivariate models combining project scope with these factors have been developed.  However, the literature also points to the limitations of these models: their application is only effective in the regions and countries where they were developed.  This study aims to develop a multivariate time prediction model that is suitable for the Nigerian construction industry.  A self administered questionnaire was used to source information on the project scope factors and qualitative and managerial factors that should be considered in the study.



Principal component regression was used for the data analysis and model development, using SPSS 16.0 for windows.  Three models were developed; two of them - for the public and private sector - had high R2 values.  Through testing and validation they were found to be suitable for predicting construction time. By contrast, the third model, which covered all projects, had a low R2 value and was found to be an unsuitable prediction tool.  The models with high R2 values serve as a useful tool to help project managers and contractors predict construction time, thereby facilitating effective planning.

Winner's Bios

Hassan Ahmadu

Ahmadu Bello University, Nigeria

Hassan completed his PhD at the Ahmadu Bello University in Zaria, Nigeria, having previously gained a BSc in quantity surveying and an MSc in project management from the same institution.  His current research interest is in mathematical modelling, particularly for predicting the duration of construction projects, and he has recently developed a deterministic construction duration prediction model for the Nigerian construction industry.  He looks forward to developing a more robust stochastic model during his PhD programme.


Judge's comments

“A well-written dissertation which is tightly written and nicely argued.  It draws from a broad and authoritative literature review to frame the research question in an exemplary way.  It has a diligent and statistically-rigorous analysis of quantitative data gleaned from a well-designed questionnaire survey.  It is an excellent piece of work and is well deserving of international recognition.”