Original scientific paper

https://doi.org/10.17559/TV-20240721001870

IGWO-SVM: An Enhanced Cost Prediction Model for Construction Projects

DaWen Zhang ; School of Civil Engineering, Southwest Jiaotong University Hope College, ChengDu, SiChuan, China, 610400 *

* Corresponding author.

Abstract

Accurate cost prediction is crucial for successful construction project management. This study proposes an improved Grey Wolf Optimizer-Support Vector Machine (IGWO-SVM) model for construction project cost prediction. The model incorporates Tent mapping and quantum well techniques to enhance the global search capability and prediction accuracy. Experimental results show that the IGWO-SVM model achieves a prediction error rate of 0.01%, significantly outperforming traditional methods. The model reduced total construction days by 1.72%, total cost by 1.89%, and improved quality levels by 15.31% on average. The IGWO-SVM model demonstrates high stability and accuracy, providing a reliable tool for construction project cost prediction and optimization.

Keywords

construction project; cost prediction; cost optimization; grey wolf optimizer; support vector machine

Hrčak ID:

332840

URI

https://hrcak.srce.hr/332840

Publication date:

29.6.2025.

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