Machine Learning-Based Cost Estimation Models for Office Buildings

With the increasing trend of office buildings towards high-rise, multifunctional, and structurally complex architecture, the difficulty of engineering cost management has increased. Accurately estimating costs during the decision-making stage is crucial for ensuring the overall project’s financial viability. Therefore, finding straightforward and efficient methods for cost estimation is essential. This paper explores the application of algorithm-optimized back propagation neural networks and support vector machines in predicting the costs of office buildings. By employing grey relational analysis and principal component analysis to simplify indicators, six prediction models are developed: BPNN, GA-BPNN, PSO-BPNN, GA-SVM, PSO-SVM, and GSA-SVM models. After considering accuracy, stability, and computation time, the PCA-GSA-SVM model is identified as the most suitable for office building cost prediction. It achieves stable and rapid results, with an average mean square error of 0.024, a squared correlation coefficient of 0.927, and an average percentage error of 5.52% in experiments. Thus, the model proposed in this paper is both practical and reliable, offering valuable insights for decision-making in office building projects.