Abstract
Abstract Each project management system aims to complete the project within its identified objectives: budget, time, and quality. It is achieving the project within the defined deadline that required careful scheduling, that be attained early. Due to the nature of unique repetitive construction projects, time contingency and project uncertainty are necessary for accurate scheduling. It should be integrated and flexible to accommodate the changes without adversely affecting the construction project’s total completion time. Repetitive planning and scheduling methods are more effective and essential. However, they need continuous development because of the evolution of execution methods, essentially based on the repetitive construction projects’ composition of identical production units. This study develops a mathematical model to forecast repetitive construction projects using the Support Vector Machine (SVM) technique. The software (WEKA 3.9.1©2016) has been used in the process of developing the mathematical model. The number of factors affecting the planning and scheduling of the repetitive projects has been identified through a questionnaire that analyzed its results using SPSS V22 software. Three accuracy measurements, correlation coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), were used to check the mathematical model and to compare the actual values with predicted values. The results showed that the SVM technique was more precise than those calculated by the conventional methods and was found the best generalization with R 97 %, MAE 3.6 %, and RMSE 7 %.
Highlights
A few projects have given scheduling and planning optimisation models that handle a few constraints on small-scale case studies of preplanned exercises
The researcher has concluded that SVM is a useful technique that predicts repetitive construction projects' duration
This research's primary goal is to use the SVM technique as an artificial intelligence technique to predict the optimum time of repetitive construction projects
Summary
A few projects have given scheduling and planning optimisation models that handle a few constraints on small-scale case studies of preplanned exercises. These models do not consider the dynamic nature of repetitive construction project (RCP) conditions throughout the project's lifecycle [1]. Projects that lag behind schedule are indicators of lousy project performance and low project productivity for the team [2]. A repetitive project delay may cause time and cost overruns and lousy quality [3]. Many factors may affect the planning and scheduling of repetitive projects. Using the Support Vector Machine technique, this study investigates these factors and develops a mathematical model to predict repetitive construction projects
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