This paper presents a non-destructive test method for steel corrosion in reinforced concrete bridges by using a 3-dimensional digital micro-magnetic sensor to detect and analyze the self-magnetic field leakage from corroded reinforced concrete. The setup of the magnetic scanning device and the measurement mode of the micro-magnetic sensor are introduced. The numerical analysis model is also built based on the linear magnetic charge theory. Compared to the self-magnetic field leakage data obtained from magnetic sensor-based measurement and numerical calculation, it is shown that the curves of tangential magnetic field at different lift-off height all intersect near the edge of the steel corrosion zone. The result indicates that the intersection of magnetic field curves can be used to detect and evaluate the range of the inner steel corrosion in engineering structures. The findings of this work propose a new and effective non-destructive test method for steel corrosion, and therefore enlarge the application of the micro-magnetic sensor.

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.

Deflection data provides important information about the mechanical characteristics and structural health condition of bridges. The study presented here pertains to development of a deep learning based approach for structural health monitoring by employing the bridge deflections. The method presented herein uses the long short-term memory (LSTM) framework in detecting the state of damage by tracking the feature changes of time-series deflection and temperature data. Deflection and temperature data of Chongqing Egongyan Rail Transit Suspension Bridge was employed over a period of 15 months to develop the proposed method. The concept of square error index (SE) is introduced as an assessment tool for estimation of the bridge damage level. Results from the present study indicated that the statistical characteristics of SE index are proportional to the level of damage, and are only sensitive to abnormal changes in deflection. Structural health monitoring data over the period of 15 months indicated that the proposed approach has the capability to detect cable damages as low as 0.5%.

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 (B_xL(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.