Temperature Effects on Vibration-Based Damage Detection of a Reinforced Concrete Slab

Abstract

To study the variations in modal properties of a reinforced concrete (RC) slab (such as natural frequencies, mode shapes and damping ratios) under the influence of ambient temperature, a laboratory RC slab is monitored for over a year, the simple linear regression (LR) and autoregressive with exogenous input (ARX) models between temperature and frequencies are established and validated, and a damage identification based on particle swarm optimization (PSO) is utilized to detect the assumed damage considering temperature effects. Firstly, the vibration testing is performed for one year and the variations of natural frequencies, mode shapes and damping ratios under different ambient temperatures are analyzed. The obtained results show that the change of ambient temperature causes a major change of natural frequencies, which, on the contrary, has little effect on damping ratios and modal shapes. Secondly, based on a theoretical derivation analysis of natural frequency, the models are determined from experimental data on the healthy structure, and the functional relationship between temperature and elastic modulus is obtained. Based on the monitoring data, the LR model and ARX model between structural elastic modulus and ambient temperature are acquired, which can be used as the baseline of future damage identification. Finally, the established ARX model is validated based on a PSO algorithm and new data from the assumed 5% uniform damage and 10% uniform damage are compared with the models. If the eigenfrequency exceeds the certain confidence interval of the ARX model, there is probably another cause that drives the eigenfrequency variations, such as structural damage. Based on the constructed ARX model, the assumed damage is identified accurately.

Research on Fatigue Damage Evolution of the Base Plate Structure of China Railway Track System III Type Slab Ballastless Track under Heavy Haul Train Load

“…In [25], Chengbin Chen combined a weighting strategy with tracking minimum absolute contraction and selection operator (lasso) to form a new structural damage detection method. Zhenpeng Wang [26] obtained the functional relationship between temperature and frequency through experiments, established a model between them, and identified structural damage under the influence of temperature according to the particle swarm optimization (PSO) algorithm. Chu-Dong Pan [27] proposed a new moving average Tikhonov regularization method, which can accurately identify motion force and can be used for structural damage prevention and control.…”

Special Feature Vibration-Based Structural Health Monitoring

“…Feature extraction taking into account the influence of surrounding environments and ambient operation conditions is required to increase the accuracy of diagnosis. Wang et al [11] presented the effects of temperature on the vibration monitoring of concrete slab structures. Experiments were performed to find the influence of temperature on modal properties used for damage diagnosis.…”

Sensitivity and Efficiency of the Frequency Shift Coefficient Based on the Damage Identification Algorithm: Modeling Uncertainty on Natural Frequencies

“…The strategy was employed to estimate the size of rectangular geometry damage both numerically and experimentally in a tested cantilever beam. Vibration-based damage identification was used for a three-span continuous beam, a two-span steel grid [14] and a reinforced concrete beam [15], considering the effects of temperature variations. More recently, Toh and Park [16] provided a review applying machine learning algorithms for damage monitoring using vibration factors and interpretation of deep neural networks in order to guide further applications for structural vibration analysis.…”