In the ever-evolving landscape of construction, accurate cost estimation is the linchpin that can make or break a project. This is especially true for public education buildings, where budgets are tight and every penny counts. A groundbreaking study led by Latif Onur Uğur from Düzce University has introduced a novel method for estimating the approximate costs and contract fees of school buildings, potentially revolutionizing how public institutions and construction companies approach project planning.
The study, published in Düzce University Journal of Science and Technology, delves into the intricate details of 96 school building projects, meticulously analyzing a myriad of parameters that influence construction costs. These parameters range from the number of classrooms and construction duration to more technical aspects like earthquake design class and concrete class. By leveraging these data points, Uğur and his team developed a robust multiple linear regression model that can predict both approximate costs and contract prices with impressive accuracy.
The model’s efficacy is underscored by its high determination coefficients: R²=0.900 for approximate cost price and R²=0.927 for contract price. This means that the model explains a significant portion of the variability in the cost data, making it a reliable tool for preliminary cost estimation. “The error rate between the estimated costs and actual costs is around 17.5% for approximate costs and 18.2% for contract prices,” Uğur explains. “This level of accuracy is a significant step forward in the field of construction cost estimation.”
The implications of this research are far-reaching. For public institutions, this model offers a way to make more informed decisions about budget allocation and project feasibility. For construction companies, it provides a competitive edge by enabling more accurate bidding and resource planning. “Both public institutions and contracting construction companies will be able to make realistic cost estimates by benefiting from these modeling, providing time savings,” Uğur notes.
The study also highlights the potential for further refinement. By increasing the number of data points and conducting similar studies, the error rate could be minimized even further. This iterative process of improvement is crucial in a field where precision can translate into substantial cost savings and enhanced project outcomes.
As the construction industry continues to evolve, driven by technological advancements and a growing emphasis on sustainability, tools like Uğur’s model will become indispensable. They not only streamline the cost estimation process but also pave the way for more efficient and cost-effective construction practices. This research, published in Düzce University Journal of Science and Technology, is a testament to the power of data-driven decision-making in the construction sector. It sets a new benchmark for cost estimation models, promising a future where construction projects are not just completed on time and within budget, but also with a higher degree of predictability and reliability.
