Optimization of the shotcrete spraying process in blast-based tunnel construction: Insights for enhancing spraying quality
Shotcrete is important to form the primary lining of blast-based tunnel construction , but this process heavily relies on operator expertise. Therefore, this research investigates the following research question: How to effectively model and optimize the shotcrete spraying process to construct high-quality primary linings? This paper proposes a spraying process optimization method, comprising three key components: (1) a spraying trajectories parameterization model; (2) a dynamic spraying deposition model; and (3) a spraying process parameter optimization algorithm. Validation through a railway tunnel case study demonstrates the method's effectiveness, yielding optimal spraying parameters with a mean absolute error of 0.6 cm against a target thickness of 19.4 cm. These findings provide actionable solutions for the spraying process, and reduce reliance on expertise. Future research directions can focus on integrating advanced material characterization into the spraying deposition model and developing intelligent sprayers for precise spraying parameter implementation • Controlling points are introduced to optimize the spraying trajectory of shotcrete. • A dynamic deposition model of shotcrete is developed based on fluid dynamics. • A two-stage spraying parameter optimization algorithm is proposed by a metaheuristic algorithm. • The effectiveness of the proposed method is validated through a real-world tunnel construction.
Authors
- Jiajing Liu
- Jun Sun (ORCID: https://orcid.org/0000-0003-0642-7446)
- Fenghua Liu (ORCID: https://orcid.org/0009-0003-3689-1052)
- Botao Zhong
Institutions
- Hunan University (CN)
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- Automation in Construction
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1016/j.autcon.2025.106234
- Citations
- 5
- Primary Topic
- Tunneling and Rock Mechanics
- Type
- article
- Field-Weighted Citation Impact
- 4.77
Funders
- National Natural Science Foundation of China
- Huazhong University of Science and Technology
- Fundamental Research Funds for the Central Universities