Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS) deformation monitoring system demonstrated that: (1) the Kalman filter is capable of denoising the bridge deformation monitoring data; (2) the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3) in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity); the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data using sensing technology.

This paper proposed a new computing method to quantitatively and non-destructively determine the corrosion of steel strands by analyzing the self-magnetic flux leakage (SMFL) signals from them. The magnetic dipole model and three growth models (Logistic model, Exponential model, and Linear model) were proposed to theoretically analyze the characteristic value of SMFL. Then, the experimental study on the corrosion detection by the magnetic sensor was carried out. The setup of the magnetic scanning device and signal collection method were also introduced. The results show that the Logistic Growth model is verified as the optimal model for calculating the magnetic field with good fitting effects. Combined with the experimental data analysis, the amplitudes of the calculated values (BxL(x,z) curves) agree with the measured values in general. This method provides significant application prospects for the evaluation of the corrosion and the residual bearing capacity of steel strand.

No abstract

Based on the metal magnetic memory effect, this paper proposed a new non-destructive testing method for the internal tensile force detection of steel bars by analyzing the self-magnetic flux leakage (SMFL) signals. The variation of the SMFL signal of the steel bar with the tensile force indicates that the curve of the SMFL signal has a significant extreme point when the tensile force reaches about 65% of the yield tension, of which the first derivative curve has extreme points in the elastic and yielding stages, respectively. To study the variation of SMFL signal with the axial position of the steel bar under different tensile forces, a parameter reflecting the fluctuation of the SMFL signal along the steel bar is proposed. The linear relationship between this parameter and the tensile force can be used to quantitatively calculate the tensile force of steel bar. The method in this paper provides significant application prospects for the internal force detection of steel bar in the actual engineering.

No abstract

No abstract

No abstract

Concrete fatigue strain evolution plays a very important role in the evaluation of the material properties of concrete. To study fatigue strain and fatigue damage of concrete in reinforced concrete beams under constant amplitude bending fatigue loading, constant amplitude bending fatigue experiments with reinforced concrete beams with rectangular sections were first carried out in the laboratory. Then, by analyzing the shortcomings and limitations of existing fatigue strain evolution equations, the level-S nonlinear evolution model of fatigue strain was constructed, and the physical meaning of the parameters was discussed. Finally, the evolution of fatigue strain and fatigue damage of concrete in the compression zone of the experimental beam was analyzed based on the level-S nonlinear evolution model. The results show that, initially, fatigue strain grows rapidly. In the middle stages, fatigue strain is nearly a linear change. Because the experimental data for the third stage are relatively scarce, the evolution of the strain therefore degenerated into two phases. The model has strong adaptability and high accuracy and can reflect the evolution of fatigue strain. The fatigue damage evolution expression based on fatigue strain shows that fatigue strain and fatigue damage have similar variations, and, with the same load cycles, the greater the load level, the larger the damage, in line with the general rules of damage.