The availability of a dataset for validation and verification purposes of novel data-driven strategies and/or hybrid physics-data approaches is currently one of the most pressing challenges in the engineering field. Data ownership, security, access and metadata handiness are currently hindering advances across many fields, particularly in Structural Health Monitoring (SHM) applications. This paper presents a simulated SHM dataset, comprised of dynamic and static measurements (i.e., acceleration and displacement), and includes the conceptual framework designed to generate it. The simulated measurements were generated to incorporate the effects of Environmental and Operational Variations (EOVs), different types of damage, measurement noise and sensor faults and malfunctions, in order to account for scenarios that may occur during real acquisitions. A fixed-fixed steel beam structure was chosen as reference for the numerical benchmark. The simulated monitoring was operated under the assumptions of a Single Degree of Freedom (SDOF) for generating acceleration records and of the Euler-Bernoulli beam for the simulated displacement measurements. The generation process involved the use of parallel computation, which is detailed within the provided open-source code. The generated data is also available open-source, thus ensuring reproducibility, repeatability and accessibility for further research. The comprehensive description of data types, formats, and collection methodologies makes this dataset a valuable resource for researchers aiming to develop or refine SHM techniques, fostering advancements in the field through accessible, high-quality synthetic data.

翻译：目前，工程领域最紧迫的挑战之一是为验证和确认新型数据驱动策略和/或混合物理-数据方法提供数据集。数据所有权、安全性、访问权限以及元数据处理的便利性，正阻碍着许多领域的进展，尤其是在结构健康监测应用领域。本文提出了一个模拟的结构健康监测数据集，包含动态和静态测量数据，并介绍了设计用于生成该数据的概念框架。模拟测量数据的生成考虑了环境与运行变化的影响、不同类型的损伤、测量噪声以及传感器故障与失灵，以涵盖实际采集过程中可能出现的各种场景。数值基准选用固定-固定钢梁结构作为参考。模拟监测基于单自由度假设生成加速度记录，并基于欧拉-伯努利梁理论生成模拟位移测量。数据生成过程采用了并行计算，相关细节在所附的开源代码中提供。生成的数据同样以开源形式提供，从而确保了可复现性、可重复性和可访问性，以支持进一步研究。对数据类型、格式及采集方法的全面描述，使得该数据集成为研究人员开发或改进结构健康监测技术的宝贵资源，通过提供可访问的高质量合成数据推动该领域的进步。