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The construction industry has a significant multiplier effect on the economy as a whole. It provides the basis upon which other sectors can grow by constructing the physical facilities required for the production and distribution of goods and services. Building construction projects is one of the major development constraints in developing countries since their development highly depends on the growth of their physical infrastructures. Developing countries allocate a considerable amount of their scarce financial resources towards the development of their infrastructure needs. However, most of these infrastructure projects in developing countries encounter considerable quality management controlling problems, especially in public building construction projects. These results in decreasing customer satisfaction, lower productivity, and decreased service delivery times. Thus, the study focuses on exploring fuzzy AHP approaches for quality management control practices in public building construction projects. The data analysis for this study was carried out by using fuzzy AHP methods. This method is used to determine the preference weights of the input variables and for ranking. To achieve the objective, the data were collected from primary and secondary sources. Microsoft Excel was used as an analysis tool. The study revealed that quality management plans and quality management control tools are identified as mostly practiced quality management control systems in public building construction projects. Finally, it can be recommended that the project participants implement and communicate the organization's mission, vision, strategy, policies, and processes throughout the organization to enhance the productivity of the projects. [ABSTRACT FROM AUTHOR]

Earned value analysis is an important tool to measure the performance of a project to indicate what will happen to the work in the future. This study focuses on developing a fuzzy-based earned value analysis model for estimating the performance of projects. The model provides to make estimations getting closer and reduces the errors in calculations through the fuzzy linguistic terms. The data for this study were taken from projects which were done between 2020 and 2023 for the six sets of building projects. The data has been analyzed through a fuzzy-based earned value analysis model by using Microsoft Excel as a basic tool. It was found that the fuzzy-based SPI value for all projects was in Scenario 1, which implies that T((...) ≥ 1) = 0 and T((...) ≤ 1) = 1, So the work package is behind the schedule. Similarly, the value of fuzzy-based CPI for all projects except one were in Scenario 1, implies that T((...) ≥1) = 0 and T((...) ≤ 1) = 1, so the work package is behind the budget. Finally, it can be recommended that it needs to properly monitor, and control the project time, and cost to recover deviation in the remaining periods. [ABSTRACT FROM AUTHOR]

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]