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All across the world, the construction industry is a very crucial sector for development. Cost prediction is the process of forecasting the cost of construction projects at an early stage. An effective estimation technique is necessary to reduce the effort, time, and cost. Therefore, the study aims to develop a mathematical model to predict the cost of Ethiopian Roads Authority road projects in the case of Ethiopia. The data were collected from primary and secondary sources. The method of data analysis for the study was multiple regression analysis, and it was done using SP SS and Microsoft Excel. The data were obtained from a road document review and a questionnaire survey of 62 respondents and 15 interviewers who are working in ERA. The major cost-influential factors were found to be: inflation rate, bridges, culverts, project length, width, type and scope, contract duration, and location. As a result, a multiple regression model was developed using nine significant parameters taken from 18 completed road projects. The multiple regression analysis result revealed that the model can perform at a success rate of 83.8%. The MAPE of the model was ±16.44%. The conclusion and recommendations are likely to support and reduce ineffective estimation trends. [ABSTRACT FROM AUTHOR]

Project performance is a critical issue for the construction industry. The construction industry is complex in its nature because it contains a large number of parties involved in the planning, execution, and monitoring of all types of civil works. Moreover, construction industries are always unique and heterogeneous; they will always encounter many unforeseen events that will negatively affect the construction everywhere, and the construction projects suffer from many problems and complex issues in the performance of building projects. Therefore, the study was focused on assessing the factors affecting the performance of building projects. The study would employ an integrated methodology of factor analysis and fuzzy AHP methods. First, the factor analysis is used to classify and reduce the input variables and their variable coefficients are determined. Second, fuzzy AHP is used to determine the preference weights of the input variables, and for ranking. To achieve this objective, data were collected from primary and secondary sources. This study used an integrated methodology as a data analysis method, and SPSS software version 23 and Microsoft Excel were used as analysis tools. The study pointed out that project location (0.083), changes in design (0.068), market fluctuation (0.067), financial difficulties (0.066), and size of the project (0.064) are identified as the most significant factors that affect the performance of building construction projects. Finally, it can be recommended that the practitioners take appropriate actions in improving the performance of building projects, and the methodologies can be implemented to demonstrate its practicality. [ABSTRACT FROM AUTHOR]