建筑结构水平振动测试数据
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为填补多结构类型建筑非接触式水平振动数据空白，本研究针对 24 层框架 - 剪力墙结构、五层钢框架、26 层剪力墙结构及 60 层框架 - 核心筒 4 类典型建筑，采用 Polytec RSV-150 激光多普勒测振仪，在自然环境激励（风荷载、环境脉动）下采集楼层振动速度数据。数据采集时间范围为 2024 年 7 月至 2025 年 9 月，空间范围覆盖深圳市 4 个测试场地，数据格式为 TXT 与 PPC，数据量约 83.2MB，未明确提及时间精度与空间精度。研究过程严格遵循《建筑与桥梁结构监测技术规范》（GB50982-2014），通过多维度质量控制措施保障数据有效性。该数据集可直接用于建筑结构模态参数识别（如固有频率、阻尼比及机器学习模型训练（如振动异常预警模型），为建筑结构动力学研究、工程设计优化及既有建筑安全评估提供高质量数据支撑，有效解决传统接触式振动测试布设难、有扰动、覆盖局限等问题。
To fill the gap in non-contact horizontal vibration data of multi-structure-type buildings, this study targeted four typical building types: a 24-story frame-shear wall structure, a 5-story steel frame structure, a 26-story shear wall structure, and a 60-story frame-core tube structure. A Polytec RSV-150 laser Doppler vibrometer was employed to collect floor vibration velocity data under natural environmental excitations including wind loads and ambient vibrations. The data collection period spans from July 2024 to September 2025, with spatial coverage across four test sites in Shenzhen. The data is stored in TXT and PPC formats, with a total volume of approximately 83.2 MB, and no explicit specifications for temporal or spatial accuracy are provided. This research strictly complies with the Technical Code for Structural Monitoring of Buildings and Bridges (GB50982-2014), and multi-dimensional quality control measures are adopted to ensure data validity. This dataset can be directly applied to building structural modal parameter identification (e.g., natural frequencies, damping ratios) and the training of machine learning models (e.g., vibration anomaly early warning models), providing high-quality data support for building structural dynamics research, engineering design optimization, and safety assessment of existing buildings, and effectively solving the problems of difficult layout, induced disturbance, and limited coverage associated with traditional contact-based vibration testing.
