Developing the Learning Curve Model to Enhance Construction Project Scheduling and Cost Estimating

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Aim The aim of the study is to develop a scheduling and cost estimation model for repetitive construction units by applying the learning curve theory and to contribute to advancements in construction project management practices, promoting efficiency and competitiveness within the industry. Background Construction projects, particularly those with repetitive units like housing developments, face ongoing challenges in accurate scheduling and cost estimation. Traditional estimation methods often overlook the impact of learning effects, which can improve productivity and reduce costs as crews gain experience. Learning curve theory, widely applied in manufacturing, offers a framework to model these gains in construction settings. Integrating learning curves into project planning has the potential to enhance accuracy in forecasting timelines and budgets, ultimately improving project efficiency and resource management. Objective The objective of this study is to develop and apply a learning curve model to enhance scheduling and cost estimation in repetitive construction projects, particularly in a multi-unit housing project. Methods By incorporating historical data and analyzing critical factors that impact project duration and cost, a more reliable forecasting model is developed. The learning curves are created using a three-point approach, supported by artificial neural networks (ANN) and the relative importance index (RII), to systematically assess cost divisions and influential project factors. Results The results indicate that the learning curve model can achieve time savings of 27% and labor cost savings of 36% compared to traditional estimation methods that do not consider the effect of the learning curve in construction projects. Conclusion This research demonstrates that learning curve models, combined with advanced data analysis techniques, provide a robust framework for optimizing project schedules and budgets, ultimately leading to more efficient resource utilization and cost-effective project outcomes. In other words, the study presented in this paper is significant as it can lead to improved project outcomes, cost savings, better resource management, and overall advancement in the construction industry's practices and competitiveness. This approach allows for accurate scheduling and cost forecasting based on data-driven insights.