Prediction of Highway Tunnel Cost by Least Squares Support Vector Machine Based on Particle Swarm Optimization

DOI

10.2991/978-94-6463-038-1_37How to use a DOI?

Keywords

highway tunnel; particle swarm; least squares support vector machine; cost prediction

Abstract

In the early stages of a project, the owner is faced with the challenge of deciding whether to invest in the project or not. The road tunnel project involves a large amount of investment, and in order to help the owner to make a selection decision, it is crucial to achieve accurate and efficient road tunnel cost prediction in the early stages. In this paper, the main factors affecting the cost of road tunnels are identified through literature research, and the least squares support vector machine is optimized using a particle swarm optimization algorithm. The results show that the least squares support vector machine model based on the particle swarm optimization algorithm performs well in road tunnel cost prediction, with high fit and low error, and meets the accuracy and practicality requirements of the pre-project.

Copyright

© 2023 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.

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TY  - CONF
AU  - Jing Liao
AU  - Guanghua Li
PY  - 2022
DA  - 2022/12/15
TI  - Prediction of Highway Tunnel Cost by Least Squares Support Vector Machine Based on Particle Swarm Optimization
BT  - Proceedings of the 2022 3rd International Conference on Management Science and Engineering Management (ICMSEM 2022)
PB  - Atlantis Press
SP  - 406
EP  - 414
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-038-1_37
DO  - 10.2991/978-94-6463-038-1_37
ID  - Liao2022
ER  -