Open Access

ARTICLE

Monitoring of Drill-and-Blast Workflows at the Tunnel Face Using Computer Vision and Context Reasoning

Chuanjiang Chen¹, Junyong Zhou^1,*, Binbin Du¹, Miaosi Dong^2,*, Liwen Zhang¹, Bitang Zhu³

1 School of Civil Engineering and Transportation, Guangzhou University, Guangzhou, China
2 Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
3 School of Civil Engineering and Architecture, East China Jiaotong University, Nanchang, China

* Corresponding Authors: Junyong Zhou. Email: ; Miaosi Dong. Email:

(This article belongs to the Special Issue: Emerging Artificial Intelligence & Data-Driven Modeling in Civil Engineering)

Computer Modeling in Engineering & Sciences 2026, 147(2), 17 https://doi.org/10.32604/cmes.2026.081546

Received 04 March 2026; Accepted 02 May 2026; Issue published 27 May 2026

Abstract

Computer vision has been widely adopted in intelligent construction monitoring; however, existing studies primarily focus on identifying individual construction elements or isolated activities, with limited capability for integrated monitoring of complete construction workflows. Such workflow-level automation is a prerequisite for intelligent construction and unmanned job sites. To address the challenge of reliable visual recognition in drill-and-blast tunnel environments characterized by uneven illumination, localized glare, and dust interference, this study proposes a methodological framework for construction workflow recognition at the tunnel face using computer vision and context reasoning. The framework consists of three components: (1) a construction workflow model with a sequence library database, (2) a robust construction element recognition model combining an enhanced YOLOv11 with Segment Anything Model 2 (SAM2), and (3) a hierarchical workflow reasoning mechanism driven by domain knowledge. A hierarchical workflow model embedding procedural logic is established through field investigation and normative analysis. SAM2 is employed for automated dataset annotation, while YOLOv11 is structurally enhanced with Convolutional Block Attention Module (CBAM), Adaptive Feature Enhancement (AFE), and Swin Transformer modules to improve feature representation and adaptability to degraded visual conditions. Workflow identification is finally achieved by integrating visual perception outputs with hierarchical context reasoning. Validation in an active drill-and-blast tunnel shows that the proposed method attains an average detection precision of 91.1% across 11 construction element categories, exceeding 95% for large equipment, and an average workflow recognition accuracy of 94%. The results demonstrate the effectiveness of the proposed framework for monitoring the tunnel construction workflow and supporting construction management.

---

Keywords

---

Supplementary Material

Supplementary Material File

---