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Statistical Analysis on the Cost and Duration of Public Building Projects
Authors:Ayman A Abu Hammad  Souma M Alhaj Ali  Ghaleb J Sweis  Rateb J Sweis
Affiliation:1Assistant Professor, Dept. of Civil Engineering, College of Engineering, Applied Science Univ., P.O. Box 926296, Amman 11931, Jordan (corresponding author). E-mail: dr-abuhammad@asu.edu.jo
2Assistant Professor, Dept. of Industrial Engineering, Hashemite Univ., P.O. Box 150459, Zarqa 13115, Jordan. E-mail: souma@hu.edu.jo
3Associate Professor, Dept. of Civil Engineering, College of Engineering, Univ. of Jordan, Amman 11942, Jordan. E-mail: gsweis@ju.edu.jo
4Assistant Professor, College of Business Administration, Univ. of Jordan, Amman 11942, Jordan. E-mail: r.sweis@ju.edu.jo
Abstract:A probabilistic model is proposed to predict the risk effects on time and cost of public building projects. The research goal is to utilize a real history data in estimating project cost and duration. The model results can be used to adjust floats and budgets of the planning schedule before project commencement. Statistical regression models and sample tests are developed using real data of 113 public projects. The model outputs can be used by project managers in the planning phase to validate the schedule critical path time and project budget. The comparison of means analysis for project cost and time performance indicated that the sample projects tend to finish over budget and almost on schedule. Regression models were developed to model project cost and time. The regression analysis showed that the project budgeted cost and planned project duration provide a good basis for estimating the cost and duration. The regression model results were validated by estimating the prediction error in percent and through conducting out-of-sample tests. In conclusion, the models were validated at a probability of 95%, at which the proposed models predict the project cost and duration at an error margin of ±0.035% of the actual cost and time.
Keywords:Construction costs  Construction management  Project delivery  Regression analysis  Regression models  Statistics  
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