Open Guided Waves

Modern society relies on structural health monitoring to assess the condition of mechanical systems and civil infrastructure,ensuring they operate efficiently and safely. While most of studies focus on evaluating structures under controlled or stable lab conditions, a significant number of structures function in uncontrolled and dynamic environments over extended periods.Long-term monitoring in such complex conditions presents additional challenges compared to lab-based assessments. Despite this, few studies address structural health monitoring under these conditions, largely due to the lack of a public benchmark dataset. Such a dataset is crucial for identifying signal features, understanding patterns in guided waves from dynamic environments, and evaluating structural health monitoring methods. To address this gap, this paper presents a public dataset from a long-term outdoor structural monitoring experiment conducted at the University of Utah, Salt Lake City. The monitoring, spanning over 4.5 years, collected approximately 560 million guided waves under both regular environmental variations (e.g.daily temperature changes ranging from −12.2◦C to 52.5◦C, and humidity ranged from 0.5% to 100%) and irregular variations (e.g., rain and snow). The measured guided waves in the public dataset are also affected by sensor drift and installation shifts consistently over time. Additionally, thirteen types of damage were introduced to the monitored structure to support damage detection and severity evaluation under these conditions. The dataset includes measurement times, temperature,humidity, air pressure, brightness, and weather information to aid in damage detection. This paper uses correlation coefficients between adjacent guided waves and optimal correlation coefficients with baseline guided waves to highlight four key challenges in long-term outdoor monitoring: the impact of regular and irregular environmental variations, sensor drift, and installation shifts on guided waves, and the detection of minor damage that causes only slight changes in guided waves. These insights aim to assist researchers in developing more practical methods for structural health monitoring in uncontrolled and dynamic environments.

现代社会依赖结构健康监测（Structural Health Monitoring, SHM）来评估机械系统与土木基础设施的运行状态，保障其高效、安全地运转。尽管绝大多数现有研究聚焦于受控或稳定实验室环境下的结构性能评估，但仍有大量结构需长期处于非受控且动态变化的环境中运行。相较于实验室环境下的评估，此类复杂场景下的长期监测面临更多额外挑战。即便如此，针对此类场景下结构健康监测的研究仍寥寥无几，其主要原因在于缺乏公开的基准数据集。此类数据集对于识别信号特征、解析动态环境下导波（guided waves）的传播模式，以及评估结构健康监测方法均至关重要。为填补这一研究空白，本文发布了一套源自盐湖城犹他大学开展的长期户外结构监测实验的公开数据集。该监测项目历时超4.5年，在常规环境变化（例如日温范围为−12.2℃至52.5℃、相对湿度范围为0.5%至100%）与非常规环境变化（例如降雨、降雪）两种场景下，共采集了约5.6亿条导波数据。本公开数据集内的实测导波信号还持续受到传感器漂移与安装位置偏移的影响。此外，研究人员在被监测结构上引入了13种类型的损伤，以支持此类场景下的损伤检测与损伤程度评估。该数据集包含测量时间、温度、湿度、气压、光照强度以及气象信息，可辅助损伤检测任务。本文通过相邻导波间的相关系数，以及与基准导波的最优相关系数，阐明了长期户外监测面临的四项核心挑战：常规与非常规环境变化、传感器漂移与安装位置偏移对导波信号的影响，以及仅会引发导波信号微弱变化的微小损伤检测难题。本研究的相关结论旨在助力研究人员开发出适用于非受控动态环境的更实用的结构健康监测方法。

搜集汇总

背景与挑战

背景概述

Open Guided Waves是一个长期户外结构监测实验的公共数据集，包含4.5年内收集的约5.6亿条导波数据，覆盖多种环境条件和损伤类型，旨在支持动态环境下的结构健康监测研究。