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This research applies large language models and reinforcement learning to automate mobile crane operations and generate real-time construction progress reports, marking a significant step toward intelligent infrastructure management. By integrating robot kinematics with generative AI for safety monitoring and documentation, the study demonstrates how ML algorithms can directly enhance efficiency in physical building processes. The work validates the feasibility of autonomous construction systems through full-scale retaining wall experiments, bridging the gap between theoretical AI planning and tangible industrial application.
Large language model and reinforcement learning based autonomous construction and progress updates
arXiv:2610.26899v1 Announce Type: new Abstract: Due to the rapid growth of the global population, traditional construction methods are gradually unable to meet the current housing demand. The advancement of machine learning algorithms has encouraged researchers to explore their applications in the construction industry, leading to more intelligent and efficient building processes. In particular, the rise of large language models in recent years has provided more possibilities for the transformation from traditional construction to smart construction. In this study, a large language model and reinforcement learning based autonomous construction method is proposed. An enhanced soft actor-critic reinforcement learning algorithm is developed to plan actions for a mobile crane according to construction tasks. Robot kinematics and control algorithms are used to ensure that the actions generated by the reinforcement learning algorithm can be executed by the crane. In addition, a customized embedding model is developed to extract information from the construction environment according to user interests. Then, a large language model, ChatGPT 5, is used to update construction progress, provide safety information to control system, and generate construction progress reports. The feasibility of proposed method is verified through several full-scall retaining wall construction experiments.
Abstract:Due to the rapid growth of the global population, traditional construction methods are gradually unable to meet the current housing demand. The advancement of machine learning algorithms has encouraged researchers to explore their applications in the construction industry, leading to more intelligent and efficient building processes. In particular, the rise of large language models in recent years has provided more possibilities for the transformation from traditional construction to smart construction. In this study, a large language model and reinforcement learning based autonomous construction method is proposed. An enhanced soft actor-critic reinforcement learning algorithm is developed to plan actions for a mobile crane according to construction tasks. Robot kinematics and control algorithms are used to ensure that the actions generated by the reinforcement learning algorithm can be executed by the crane. In addition, a customized embedding model is developed to extract information from the construction environment according to user interests. Then, a large language model, ChatGPT 5, is used to update construction progress, provide safety information to control system, and generate construction progress reports. The feasibility of proposed method is verified through several full-scall retaining wall construction experiments.
Artificial Intelligence (cs.AI)
arXiv:2610.26899 [cs.AI]
(or arXiv:2610.26899v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.26899 Focus to learn more arXiv-issued DOI via DataCite
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