Abstract
Construction projects still face the old–new problem of delivering the projects within the predefined time and cost. This problem becomes more complicated with when addendums and variations are con...
Highlights
Construction projects are characterized by their complex nature
The results show that support vector machine (SVM) model I successfully predicted the cost index for the trained data, and for projects with input parameters out of the range of the training inputs
It can be stated that cost and time are the most important elements of the project, considering that the poor performance in the implementation of construction projects leads to deviation of the quality of these projects on the one hand and deviation in time and cost of the projects on the other hand
Summary
Construction projects are characterized by their complex nature. decision makers in the construction industry face many challenges due to the limited information and data. Cost index (CI) and time index (TI) are important parameters that are used to predict the performance of construction projects. 2. Research objective Main aim of this study is to develop of the forecasting model for predict the performance of CI and TI using support vector machine (SVM) technique for tunnel projects at execution and monitoring stage. This study is important because it provides a new method for measuring the performance of construction projects and evaluates the performance of tunnels projects at execution and monitoring stage by using an intelligent mathematical model such as SVM Technique. (1) Null hypothesis (H0): SVM is a powerful technique to predict the performance of CI and TI for tunnel projects at execution and monitoring stage.
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