Utilizing Artificial Neural Networks to Compare Construction Cost Estimates

Utilizing Artificial Neural Networks to Compare Construction Cost Estimates
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Abstract
Construction cost forecasting is crucial for construction enterprises to remain competitive and grow. For initial feasibility studies and a successful conclusion, reliable building cost estimates must be made early. A vast array of variables influences the cost estimate. This investigation compares regression analysis, support vector machines, and artificial neural networks (RA). Estimating building expenses and forecasting price increases are critical for builders, estimators, and project owners. The pricing technique is challenging since building prices have always been susceptible to fluctuations that tend to increase over time. The construction cost index has frequently been utilized in project cost estimates. The problem is that few organizations provide estimates for it. The most significant addition to this work is providing a reliable method for calculating project costs, especially considering the present inflation rate. Construct cost forecasts are created using an Artificial Neutral Network (ANN) approach. The choice of transfer function may have a substantial effect on the neural networks' performance. This Study aims to create a model for predicting building expenses and compare it to regression analysis and support vector machines while considering the cost of materials.
Table of Contents Abstract 2 1.0 Introduction. 4 1.1 Background. 4 1.2 Motivation of the Study. 5 1.3 Different Techniques for Project Cost Estimation. 6 1.4 Statement of the Problem.. 8 1.5 Aim of the paper 8 2.0 Predicting construction cost using ANN Model 9 3.0 Research Methodology. 10 3.1 Collection of Data for the Research. 10 3.2 Building the ANN Models. 11 3.3 Trial Models. 12 3.4 Analysis of Results. 12 4.0 Conclusion. 13 Appendices. 15 References. 17 1.0 Introduction
According to some, the accomplishment of a construction project is measured by how well it adheres to the owner's expectations for quality, timing, and budget. Because of this, a construction manager or contractor needs efficient tools for job scheduling and budget or cost estimation. Any construction project needs to have an early budget or cost prediction stage. An expected profit can quickly become lost due to inaccuracy in the budget or cost prediction (Matel et al., 2022). It is crucial to remember that estimating the costs of building projects is challenging because numerous unpredictable elements impact them. According to Matel et al. (2022), a variety of categories can have a significant impact on project costs. These variables include, among others, the cost of materials, transportation costs, site conditions, project scale, and schedule. The cost of the materials, which affects the overall cost of building, is one of those elements.
1.1 Background
Fan and Sharma (2021) state that the problem of predicting project costs is classified as a multifaceted issue. It has been evaluated utilizing approaches such as support vector machines, artificial neural networks, and regression analysis for the project. These prediction strategies utilize historical cost data to establish a fu...