Work

Bridging the Information Gap in Smart Construction: An LLM-Based Assistant for Autonomous TBM Tunneling

Year: 2025

Type: article

Abstract: The development of autonomous tunneling is crucial for building the intelligent underground infrastructure that smart cities require. However, in complex urban environments, the need for frequent manual intervention during Tunnel Boring Machine (TBM) operation remains a challenge, hindering overall efficiency and safety. To address the human–machine collaboration gap, this study analyzes practical experiences from six tunnel projects that use autonomous driving systems. Building on this foundation, we develop an intelligent assistant powered by a large language model (LLM). The assistant constructs a complete service architecture and intervention mechanism, proposes a phased intention recognition framework, and uses conversational interaction to achieve efficient human–machine communication. Experimental results demonstrate the strong classification performance of our intention recognition model. Furthermore, engineering case studies validate the assistant’s effectiveness in enhancing operational transparency, increasing user trust, bridging the human–machine information gap, and ultimately ensuring safer and more reliable tunneling. This research provides a feasible and innovative technological path for human–machine collaboration in the construction of critical urban infrastructure.

Source: Smart Cities

DOI: 10.3390/smartcities8060212

Authors: Min Hu, Hongzheng Gao, Qing Mi, Bingjian Wu, Jing Lu, Yongchang Liu

Institution: Shanghai University

Language: English

Topic: Tunneling and Rock Mechanics

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,413