建筑工程智能感知技术研究综述

周淑威; 徐可; 刘扬

doi:10.3724/j.gyjzG25072701

建筑工程智能感知技术研究综述

doi: 10.3724/j.gyjzG25072701

周淑威¹,
徐可^2,3,
刘扬²

1. 许昌市域轨道交通有限公司, 河南许昌 461000;
2. 许昌学院土木工程学院, 河南许昌 461000;
3. 河南省建筑固废生态高质化利用工程技术研究中心, 河南许昌 461000

基金项目:

河南省科技攻关项目（232102320106，242102320031）。

详细信息

作者简介:
周淑威，硕士，高级工程师，主要从事施工安全信息化、施工管理智能化方向研究。电子信箱 :zhousw202106@163.com

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- 被引次数: 0
出版历程
- 收稿日期: 2025-07-27
- 网络出版日期: 2026-11-06

A Review on Intelligent Perception Technologies in Building Engineering

1. Xuchang Urban Rail Transit Co., Ltd., Xuchang 461000, China;
2. College of Civil Engineering, Xuchang University, Xuchang 461000, China;
3. Engineering Technology Research Center for Ecological and High-Quality Utilization of Construction Solid Waste, Xuchang 461000, China

摘要

摘要: 在建筑业数字化转型背景下，施工现场产生了大量包括文本、视觉、语音与传感器在内的多源异构且动态变化的数据。传统的规则驱动分析方法难以充分挖掘各模态之间的互补信息，难以满足对实时感知与协同决策的实际需求。首先，提出“输入—任务—融合—挑战—展望”的逻辑框架，系统回顾了施工现场中4类主要模态数据的输入特征与任务映射关系，分析了多模态人工智能（AI）在安全监测、进度控制、设备维护及文档处理等关键环节的典型应用，揭示其在语义提取、行为识别与状态建模中的应用价值。然后，比较了数据级、特征级与决策级三类融合策略的技术原理与适用情境，探讨了“边—云—端”协同架构的演进路径，并总结当前面临的数据异构、模型迁移性差、部署资源受限与隐私合规等关键技术挑战。最后，提出融合领域知识、优化边缘部署与加强标准建设的研究方向与发展建议，旨在为多模态AI在智能建造中的持续演进提供系统性参考。
- 智能建造 /
- 多模态人工智能 /
- 施工现场感知 /
- 数据融合 /
- 施工管理 /
- 语义理解
Abstract: With the ongoing digital transformation in the construction industry， construction sites are generating large volumes of heterogeneous and dynamically evolving data from multiple sources， including text， images， speech， and sensors. Traditional rule-based analytical methods are insufficient to fully exploit the complementary information across different modalities and fail to meet the demands of real-time perception and collaborative decision-making. This paper adopted a structured framework consisting of input， task， fusion， challenge， and prospect to systematically review the characteristics of four primary types of modal inputs in construction scenarios and their corresponding task mappings. It analyzed typical applications of multimodal artificial intelligence （AI） in critical functions such as safety monitoring， progress tracking， equipment maintenance， and document processing， demonstrating its value in semantic extraction， behavior recognition， and status modeling. Moreover， it summarized three major fusion strategies at the data， feature， and decision levels， and discussed their technical principles and applicable contexts. The evolution of collaborative system architectures involving edge， cloud， and on-site devices was examined， along with an overview of significant technical challenges， including data heterogeneity， poor model transferability， limited computational resources， and compliance with data privacy regulations. Finally， this paper proposed future research directions， including the integration of domain knowledge， optimization of edge deployment， and reinforcement of standardization efforts， to provide a systematic reference for the ongoing advancement of multimodal AI in intelligent construction.
- intelligent construction /
- multimodal artificial intelligence /
- construction site perception /
- data fusion /
- construction management /
- semantic understanding

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