CONSTRUCTION COST PREDICTION OF PUBLIC BUILDINGS IN ADDIS ABABA USING ARTIFICIAL NEURAL NETWORK MODEL

All parties involved in the construction of a project; owners, contractors, and donors need reliable information about the cost in the project initiation phase. Conceptual cost estimation is a challengeable task for cost engineers, project managers, and decision-makers which necessitate the use of cost prediction models to provide improved estimates of the total construction costs. This study was conducted to predict the construction cost of public buildings in Addis Ababa using Artificial Neural Network (ANN) cost prediction model. ANN is a powerful means to handle non-linear problems and subsequently map relationships between complex input/output data and address uncertainties. ANNs are capable of automatically learning how to perform work based on the information gathered for training; thus it is relatively easier to generate prediction models over other traditional nonlinear statistical methods. To achieve the objective, the necessary data were collected from building contractors through a questionnaire survey, and then data analysis was made by Statistical Package for Social Science (SPSS) and Matrix laboratory (MATLAB). Accordingly, the major cost-driving factors and factors affecting the accuracy of cost prediction were identified and their impacts on the cost of construction projects were assessed. The top five important cost-driving parameters that determine the total cost of public building construction projects are: number of stories, type of project, height of the building, type of foundation, and area of the floor. Based on the survey analysis the top five significant factors influencing the accuracy of cost prediction are: currency exchange fluctuations, completeness of project documents, clarity in the scope of the project, accuracy and relevancy of cost data, and estimators experience level.