Structural health monitoring and performance analysis of a 12-story recycled aggregate concrete structure. (15th February 2020) - Wang, Chunhui; Xiao, Jianzhuang; Zhang, Chuanzeng; Xiao, Xuwen
Highlights: Structural health monitoring is applied on a recycled aggregate concrete structure. Effects of the ambient environment and structural response amplitudes are analyzed. Serviceability is evaluated based on the structural responses during typhoons. Dynamic parameters are predicted by stick-slip model and artificial neural network. Abstract: To encourage the application of recycled coarse aggregates (RCA) produced from construction and demolition (C&D) waste in newly-built structures, this study investigates the dynamic behaviors of a 12-story recycled aggregate concrete (RAC) in the operational environment through structural health monitoring (SHM), and compares with an adjacent 12-story natural aggregate concrete (NAC) structure with similar arrangements of components and structural construction. The SHM has undergone 12 months, and has provided a wealth of data detailing the dynamic behaviors with the variation in the operational environment as well as significant structural responses excited by typhoons and earthquakes. Firstly, the first 5 vibration modes of both structures are identified, and the effects of the ambient temperature, hourly mean wind speed and structural response amplitude are analyzed. A lower natural frequency and a larger damping ratio of the fundamental vibration mode of the RAC structure are found than those of the NAC structure. For both the RAC and NAC structures, the fundamental frequency increases with the increasing ambient temperature, the decreasing hourly mean wind speed, and the decreasing structural response amplitude. The opposite trend of the damping ratios is identified. Secondly, the stick-slip model is used to explain the differences between the modal parameters of the RAC and the NAC structures, and to discover the mechanism of the effects of the ambient temperature, the hourly mean wind speed, and the structural response amplitude. Thirdly, the serviceability of the RAC structure is evaluated based on the full-scale measurements during typhoons. Finally, the artificial neural network (ANN) is validated to have the capacity for predicting the modal parameters precisely, and the model error of the ANN has the potential as an indicator for detecting abnormal structural performances.