Dataset on guided waves from long-term structural health monitoring under uncontrolled and dynamic conditions - Kang Yang; Zekun Yang; Hanbo Yang; Junkai Zhou; Zhongzheng Ren Zhang; Linyuan Wang; Zhihui Tian; Sungwon Kim; Joel B. Harley (Scientific Data)
Abstract Few studies address guided wave structural health monitoring under controlled and dynamic environments, largely due to the lack of a public benchmark dataset. 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 6.4 million guided waves under both regular environmental variations (e.g., daily temperature changes ranging from 260.95 K (−12.2 °C) to 325.65 K (52.5 °C)) 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. The provided public dataset aims to assist researchers in developing more practical methods for structural health monitoring in uncontrolled and dynamic environments.