Research on the Application of Data Mining in the Construction Project Cost Estimation Model

DOI

10.2991/978-94-6463-793-9_44How to use a DOI?

Keywords

Construction Engineering; Quality Management; Cost Estimation; Data Mining

Abstract

In construction engineering management, cost estimation is crucial, but traditional models often lead to inaccurate estimation due to the failure to accurately capture the dynamic cost changes. To this end, we developed a data mining-based model for construction engineering cost estimation, using least squares support vector machine (LS-SVM) to establish a mathematical framework for SVM, and refined by an improved particle swarm optimization algorithm (PSO). The results confirm the estimation of construction cost. The results show that the data mining technology significantly improves the accuracy of construction engineering cost estimation, and the model performs better than other existing estimation models.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Rui Wang
AU  - Jing He
AU  - Jinrui Pan
AU  - Cancan Liao
PY  - 2025
DA  - 2025/07/28
TI  - Research on the Application of Data Mining in the Construction Project Cost Estimation Model
BT  - Proceedings of the 2025 8th International Conference on Traffic Transportation and Civil Architecture (ICTTCA 2025)
PB  - Atlantis Press
SP  - 529
EP  - 535
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-793-9_44
DO  - 10.2991/978-94-6463-793-9_44
ID  - Wang2025
ER  -