Si Han, Wang Qidi, Ruan Xin, Fang Xingpo
Department of Bridge Engineering, College of Civil Engineering, Tongji Univ, 1239 Siping Rd, Shanghai, 200092, China.
State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji Univ, 1239 Siping Rd, Shanghai, 200092, China.
Sci Rep. 2025 Feb 21;15(1):6344. doi: 10.1038/s41598-024-85079-4.
Accurate description of the condition of engineering structures is important for ensuring structural safety. Traditional analysis methods based on simplified physical mechanisms cannot accurately characterize the structural condition and neglect the value of the large amount of data generated during the construction process. This paper proposes a data-driven analysis framework that combines physical principles, dimensionality reduction techniques and ensemble learning models to trace back the deep-seated connections between data, achieving multi-factor analysis of structural defects. Using concrete structural cracks in a certain project as an example, the framework considers full life-cycle data, including material, environment, and construction processes, to construct an assessment model. The results show that by establishing a mapping relationship between construction data and structural condition, and integrating cumulative indicators from different construction stages, a reference for describing the structural safety condition can be provided to some extent, along with optimization suggestions, offering an analytical perspective for solving complex structural problems in engineering.
准确描述工程结构状况对于确保结构安全至关重要。基于简化物理机制的传统分析方法无法准确表征结构状况，并且忽略了施工过程中产生的大量数据的价值。本文提出了一种数据驱动的分析框架，该框架结合物理原理、降维技术和集成学习模型来追溯数据之间的深层次联系，实现对结构缺陷的多因素分析。以某项目的混凝土结构裂缝为例，该框架考虑了包括材料、环境和施工过程在内的全生命周期数据，以构建评估模型。结果表明，通过建立施工数据与结构状况之间的映射关系，并整合不同施工阶段的累积指标，可以在一定程度上提供描述结构安全状况的参考以及优化建议，为解决工程中的复杂结构问题提供一种分析视角。
