CAREER: Data to Models (D2M), A Domain-Guided Translation of Sensor Data to Analytical Structural Models

尽管自主资产管理的潜力已被证明，但数据驱动的方法尚未对结构工程实践产生重大影响。一个主要的障碍是，土木结构固有的高度可变性导致实验室开发的算法在没有专家指导的情况下挣扎。该学院早期职业发展计划（Career）奖支持解决这一问题的研究。这项工作介绍了一种将原始的多源传感器数据转换为功能分析模型的方法，该模型实际地代表了建成的结构行为。考虑了三种数据类型：来自图像和激光扫描的空间数据，非破坏性评估数据，如探地雷达，以及来自加速度计和应变计的响应数据。此外，该方法引入了结构本体（结构组件关系的图形映射）的使用，作为一种以计算机可以理解的方式编码领域知识的方法。这在专门的传感器测量和更广泛的功能模型之间建立了联系，减少了工程师的投入。预计研究结果将支持基础设施评估中的各种未来应用，包括创建数字双胞胎。这些传感器数据锚定到结构的分析模型中，使实践工程师能够更有效地解释物理行为，运行模拟，并促进资产管理决策。这项工作的技术方法解决了三个开放的研究问题：(1)从空间和非破坏性评估数据中面向模型的结构部件识别，(2)对编码领域知识进行推理以建立结构部件关系，(3)将响应数据与部件信息融合以表示建成行为。为了解决(1)和(2)，该框架使用机器学习框架独特地结合了结构工程本体（领域知识）和概率图形模型（推理）。它首先识别数据中的单个结构组件（例如梁，钢筋），然后推理它们在3D空间中的关系和功能。因此，从计算机视觉技术（例如，组件类型，几何形状）获得的各种噪声信息可以合成为具有上下文意义的候选结构。为了解决(3)，通过系统识别技术将候选结构与响应数据（加速度、应变）相结合，以推断不可观察的特性，如边界条件、材料特性和连接刚度。最终的输出是一个经过校准的模型，能够通过有限元等标准工具进行分析。同时，这项工作将研究成果整合到核心工程课程中。学生将直接与数据交互，学习如何创建现实世界结构的结构模型，同时学习人工智能及其在土木工程中的应用。结构本体将利用交互式学习指南，允许学生在解决问题时探索结构关系。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Despite a demonstrated potential for autonomous asset management, data-driven methods have yet failed to impact structural engineering practice significantly. A major obstacle is that the high variability inherent in civil structures causes laboratory-developed algorithms to struggle without expert guidance. This Faculty Early Career Development Program (CAREER) award supports research to address this issue. This work introduces an approach to translating raw, multi-source sensor data into functional analytical models which realistically represent the as-built structural behavior. Three data types are considered: spatial data from images and laser scanning, non-destructive evaluation data such as ground penetrating radar, and response data from accelerometers and strain gages. In addition, the methodology introduces the use of a structural ontology (a graphical map of structural component relationships) as a way to encode domain knowledge in a way a computer can understand. This creates a link between specialized sensor measurements and broader functional models, with reduced input from an engineer. It is envisioned that the results of the research would support various future applications in infrastructure assessment, including the creation of digital twins. These anchor sensor data to an analytical model of the structure, enabling practicing engineers to interpret physical behavior more effectively, run simulations, and facilitating asset management decision. The technical approach of this work addresses three open research problems: (1) model-oriented identification of structural components from spatial and non-destructive evaluation data, (2) reasoning on encoded domain knowledge to establish structural component relationships, (3) fusing response data with component information to represent as-built behavior. To address (1) and (2), the framework uniquely combines a structural engineering ontology (domain knowledge) and probabilistic graphical models (reasoning) using a machine learning framework. It begins by identifying individual structural components (e.g. beams, reinforcement) in the data, then reasoning on their relationships and functions in 3D space. Thus, the diverse and noisy information obtained from computer vision techniques (e.g., component types, geometry) can be synthesized into a candidate structure that makes contextual sense. To address (3), candidate structures are combined with response data (accelerations, strains) via system identification techniques to infer unobservable properties such as boundary conditions, material properties, and connection stiffnesses. The final output is a calibrated model capable of analysis through standard tools like finite elements. In parallel, this work integrates research products into core engineering courses. Students will interact directly with the data, learning how create structural models of real-world structures while learning about AI and its applications in civil engineering. The structural ontology will be leveraged an interactive learning guide, allowing students to explore structural relationships as they work on a problem.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.