Real-time hybrid simulation (RTHS), which has the advantages of a substructure pseudo-dynamic test, is widely used to investigate the rate-dependent mechanical response of structures under earthquake excitation. However, time delay in RTHS can cause inaccurate results and experimental instabilities. Thus, this study proposes a model-based adaptive control strategy using a Kalman filter (KF) to minimize the time delay and improve RTHS stability and accuracy. In this method, the adaptive control strategy consists of three parts-a feedforward controller based on the discrete inverse model of a servohydraulic actuator and physical specimen, a parameter estimator using the KF, and a feedback controller. The KF with the feedforward controller can significantly reduce the variable time delay due to its fast convergence and high sensitivity to the error between the desired displacement and the measured one. The feedback control can remedy the residual time delay and minimize the method's dependence on the inverse model, thereby improving the robustness of the proposed control method. The tracking performance and parametric studies are conducted using the benchmark problem in RTHS. The results reveal that better tracking performance can be obtained, and the KF's initial settings have limited influence on the proposed strategy. Virtual RTHSs are conducted with linear and nonlinear physical substructures, respectively, and the results indicate brilliant tracking performance and superb robustness of the proposed method.

Impact damper is a passive damping system that controls undesirable vibration with mass block impacting with stops fixed to the excited structure, introducing momentum exchange and energy dissipation. However, harmful momentum exchange may occur in the random excitation increasing structural response. Based on the mechanism of impact damping system, a semi-active impact damper (SAID) with controllable impact timing as well as a semi-active control strategy is proposed to enhance the seismic performance of engineering structures in this paper. Comparative experimental studies were conducted to investigate the damping performances of the passive impact damper and SAID. The extreme working conditions for SAID were also discussed and approaches to enhance the damping effect under high-intensity excitations were proposed. A numerical simulation model of SAID attached to a frame structure was established to further explore the damping mechanism. The experimental and numerical results show that the SAID has better control effect than the traditional passive impact damper and can effectively broaden the damping frequency band. The parametric studies illustrate the mass ratio and impact damping ratio of SAID can significantly influence the vibration control effect by affecting the impact force.

The dynamic characteristics of wind turbine blades are usually monitored by contact sensors with the disadvantages of high cost, difficult installation, easy damage to the structure, and difficult signal transmission. In view of the above problems, based on computer vision technology and the improved YOLOv5 (You Only Look Once v5) deep learning model, a non-contact dynamic characteristic monitoring method for wind turbine blade is proposed. First, the original YOLOv5l model of the CSP (Cross Stage Partial) structure is improved by introducing the CSP2_2 structure, which reduce the number of residual components to better the network training speed. On this basis, combined with the Deep sort algorithm, the accuracy of structural displacement monitoring is mended. Secondly, for the disadvantage that the deep learning sample dataset is difficult to collect, the blender software is used to model the wind turbine structure with conditions, illuminations and other practical engineering similar environments changed. In addition, incorporated with the image expansion technology, a modeling-based dataset augmentation method is proposed. Finally, the feasibility of the proposed algorithm is verified by experiments followed by the analytical procedure about the influence of YOLOv5 models, lighting conditions and angles on the recognition results. The results show that the improved YOLOv5 deep learning model not only perform well compared with many other YOLOv5 models, but also has high accuracy in vibration monitoring in different environments. The method can accurately identify the dynamic characteristics of wind turbine blades, and therefore can provide a reference for evaluating the condition of wind turbine blades.

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

Dynamic irregularity and acceleration of bridges subjected to high-speed trains provide crucial information for comprehensive evaluation of the health state of under-track structures. This paper proposes a novel approach for real-time estimation of vertical track dynamic irregularity and bridge acceleration using deep generative adversarial network (GAN) and vibration data from in-service train. The vehicle-body and bogie acceleration responses are correlated with the two target variables by modeling train-bridge interaction (TBI) through least squares generative adversarial network (LSGAN). To realize supervised learning required in the present task, the conventional LSGAN is modified by implementing new loss function and linear activation function. The proposed approach can offer pointwise and accurate estimates of track dynamic irregularity and bridge acceleration, allowing frequent inspection of high-speed railway (HSR) bridges in an economical way. Thanks to its applicability in scenarios of high noise level and critical resonance condition, the proposed approach has a promising prospect in engineering applications.

Using a device composed of two lateral pressure plates (LPPs) and a steel reinforcement bar to apply horizontal pressure on slope surfaces, a newly developed prestress-reinforced embankment (PRE) is proposed, to which can be adopted in strengthening railway subgrades. In this study, an analytical model, which is available of calculating additional confining stress (σ_H) at any point in a PRE, was established based on the theory of elasticity. In addition, to verify the proposed analytical model, three dimensional (3D) finite element analyses were conducted and the feasibility in application was also identified and discussed. In order to study the performance of the PRE, the propagation of σ_H in a PRE was analyzed and discussed based on the analytical model. For the aim of convenience in application, calculation charts were developed in terms of three dimensionless parameters, and they can be used to accurately and efficiently predict the σ_H in a PRE regardless of the embankment slope ratio and LPP side length ratio. Finally, the potential applications of the proposed analytical model were discussed.

Base isolation is one of the most widely implemented and well-known technique to reduce structural vibration and damages during an earthquake. However, while the base-isolated structure reduces storey drift significantly, it also increases the base drifts causing many practical problems. This study proposes the use of Shape Memory Alloys (SMA) wires for the reduction in base drift while controlling the overall structure vibrations. A multi-degree-of-freedom (MDOF) structure along with base isolators and Shape-Memory-Alloys (SMA) wires in diagonal is tested experimentally and analytically. The isolation bearing considered in this study consists of laminates of steel and silicon rubber. The performance of the proposed structure is evaluated and studied under different loadings including harmonic loading and seismic excitation. To assess the seismic performance of the proposed structure, shake table tests are conducted on base-isolated MDOF frame structure incorporating SMA wires, which is subjected to incremental harmonic and historic seismic loadings. Root mean square acceleration, displacement and drift are analyzed and discussed in detail for each story. To better understand the structure response, the percentage reduction of displacement is also determined for each story. The result shows that the reduction in the response of the proposed structure is much better than conventional base-isolated structure.