The contact acoustic emission (AE) monitoring system is time-consuming and costly for monitoring concrete structures in large scope, in addition, the great difference in acoustic impedance between air and concrete makes the detection process inconvenient. In this work, we broaden the conventional AE source localization method for concrete to the non-contact (air-coupled) micro-electromechanical system (MEMS) microphones array, which collects the energy-rich leaky Rayleigh waves, instead of the relatively weak P-wave. Finite element method was used for the numerical simulations, it is shown that the propagation velocity of leaky Rayleigh waves traveling along the air-concrete interface agrees with the corresponding theoretical properties of Lamb wave modes in an infinite concrete slab. This structures the basis for implementing a non-contact AE source location approach. Based on the experience gained from numerical studies, experimental studies on the proposed air-coupled AE source location in concrete slabs are carried out. Finally, it is shown that the locating map of AE source can be determined using the proposed system, and the accuracy is sufficient for most field monitoring applications on large plate-like concrete structures, such as tunnel lining and bridge deck.

Real-time monitoring of the behavior of reinforced soil retaining wall (RSW) is required for safety checks. In this study, a targetless displacement measurement technology (TDMT) consisting of an image registration module and a displacement calculation module was proposed to monitor the behavior of RSW, in which facing displacement and settlement typically occur. Laboratory and field experiments were conducted to compare the measuring performance of natural target (NT) with the performance of artificial target (AT). Feature count- and location-based performance metrics and displacement calculation performance were analyzed to determine their correlations. The results of laboratory and field experiments showed that the feature location-based performance metric was more relevant to the displacement calculation performance than the feature count-based performance metric. The mean relative errors of the TDMT were less than 1.69 % and 5.50 % for the laboratory and field experiments, respectively. The proposed TDMT can accurately monitor the behavior of RSW for real-time safety checks.

Crack width is an important indicator to evaluate the health condition of the concrete structure. The crack width is measured by manual using crack width gauge commonly, which is time-consuming and laborious. In this paper, we have proposed a fast and simplified crack width quantification method via deep Q learning and geometric calculation. Firstly, the crack edge is extracted by using U-Net network and edge detection operator. Then, the intelligent decision of is made by the deep Q learning model. Further, the geometric calculation method based on endpoint and curvature extreme point detection is proposed. Finally, a case study is carried out to demonstrate the effectiveness of the proposed method, achieving high precision in the real crack width quantification.

A method that can estimate global deformation and internal forces using a limited amount of displacement data and based on the shape superposition technique and a neural network has been recently developed. However, it is difficult to directly measure sufficient displacement data owing to the limitations of conventional displacement meters and the high cost of global navigation satellite systems (GNSS). Therefore, in this study, the previously developed estimation method was extended by combining displacement, slope, and strain to improve the estimation accuracy while reducing the need for high-cost GNSS. To validate the proposed model, the global deformation and internal forces of a cable-stayed bridge were estimated using limited multi-response data. The effect of multi-response data was analyzed, and the estimation performance of the extended method was verified by comparing its results with those of previous methods using a numerical model. The comparison results reveal that the extended method has better performance when estimating global responses than previous methods.

The time-reversal method is employed to improve the ability of pipeline defect detection, and a new approach of identifying the pipeline defect depth is proposed in this research. When the L(0,2) mode ultrasonic guided wave excited through a lead zirconate titinate (PZT) transduce array propagates along the pipeline with a defect, it will interact with the defect and be partially converted to flexural F(n, m) modes and longitudinal L(0,1) mode. Using a receiving PZT array attached axisymmetrically around the pipeline, the L(0,2) reflection signal as well as the mode conversion signals at the defect are obtained. An appropriate rectangle window is used to intercept the L(0,2) reflection signal and the mode conversion signals from the obtained direct detection signals. The intercepted signals are time reversed and re-excited in the pipeline again, result in the guided wave energy focusing on the pipeline defect, the L(0,2) reflection and the L(0,1) mode conversion signals being enhanced to a higher level, especially for the small defect in the early crack stage. Besides the L(0,2) reflection signal, the L(0,1) mode conversion signal also contains useful pipeline defect information. It is possible to identify the pipeline defect depth by monitoring the variation trend of L(0,2) and L(0,1) reflection coefficients. The finite element method (FEM) simulation and experiment results are given in the paper, the enhancement of pipeline defect reflection signals by time-reversal method is obvious, and the way to identify pipeline defect depth is demonstrated to be effective.

Data modelling and interpretation for structural health monitoring (SHM) field data are critical for evaluating structural performance and quantifying the vulnerability of infrastructure systems. In order to improve the data modelling accuracy, and extend the application range from data regression analysis to out-of-sample forecasting analysis, an improved most likely heteroscedastic Gaussian process (iMLHGP) methodology is proposed in this study by the incorporation of the outof-sample forecasting algorithm. The proposed iMLHGP method overcomes this limitation of constant variance of Gaussian process (GP), and can be used for estimating non-stationary typhoon-induced response statistics with high volatility. The first attempt at performing data regression and forecasting analysis on structural responses using the proposed iMLHGP method has been presented by applying it to real-world filed SHM data from an instrumented cable-stay bridge during typhoon events. Uncertainty quantification and correlation analysis were also carried out to investigate the influence of typhoons on bridge strain data. Results show that the iMLHGP method has high accuracy in both regression and out-of-sample forecasting. The iMLHGP framework takes both data heteroscedasticity and accurate analytical processing of noise variance (replace with a point estimation on the most likely value) into account to avoid the intensive computational effort. According to uncertainty quantification and correlation analysis results, the uncertainties of strain measurements are affected by both traffic and wind speed. The overall change of bridge strain is affected by temperature, and the local fluctuation is greatly affected by wind speed in typhoon conditions.