This article presents the theoretical and in situ experimental studies on state-derivative feedback control of bridge cable vibration using semiactive magnetorheological (MR) dampers. The semiactive feedback control is accomplished using only one MR damper and one accelerometer collocated near the lower end of the cable. Within the framework of reciprocal state space (RSS), the linear quadratic regulator (LQR) control technique is applied to formulate state-derivative feedback control law and derive the feedback and estimator gains for real-time control of cable vibration using MR dampers. The state-derivative feedback control strategy directly uses acceleration information for feedback and state estimation, which is usually the only measure available in practical cable vibration control implementation. More importantly, the control force commanded by the state-derivative feedback control strategy based on energy weighting is a dissipative force except for low velocity and small force, which is therefore implementable by the semiactive MR dampers without clipping. Numerical simulations of state-derivative feedback control for a stay cable in the cable-stayed Dongting Lake Bridge are conducted under sweeping sine excitation and sinusoidal step relaxation excitation, and then the experimental validation of the prototype cable is carried out in the bridge site with the help of the real-time control system dSPACE. Good agreement between the simulation and experimental results is observed.

As the world's first implementation of magnetorheological (MR) smart damping technique in bridge structures, a total of 312 semi-active MR dampers (RD-1005, Lord Corporation) have recently been installed for rain–wind-induced cable vibration control on the cable-stayed Dongting Lake Bridge, China. This project has undergone several stages of in situ experiments and tests: (i) modal tests of undamped cables, (ii) forced vibration tests of MR-damped trial cables, (iii) monitoring of MR-damped and undamped cable responses under rain–wind excitations, (iv) comparative tests using different damper setups, (v) full installation, and (vi) field measurements and real-time control tests after the installation. After outlining the above six stages of the whole project and addressing the experience and lessons learned from both open-loop control and closed-loop control practices, this study focuses on the design considerations of implementing MR dampers for cable vibration control, taking into account the effects of the damper stiffness, damper mass, stiffness of damper support, nonlinearity of the damper, and sag and inclination of the cable. The research efforts make it possible to develop elaborate MR dampers specific for application to bridge stay cables.

Switch rails are indispensable components of high speed railway systems, which have stringent nondestructive testing requirements owing to the severe operating conditions. In this article, an ultrasonic guided wave method is proposed for defect detection and localization using independent component analysis (ICA). The temperature effect is included in the data matrix by a random selection of the signals measured at different temperatures. A damage index named the average standard Euclidian distance (ASED) is used to evaluate the deviations of the test signals from the baseline signals in the feature space consisting of the independent components for the defect detection. Once the defect existence is found, defect localization is conducted by another ICA-based decomposition of a new data matrix with additional test signals for the same defect. Independent components whose coefficient vectors show a high correlation with the standard step change vector are chosen to construct the ICA-based residual signal. Then the time instance and location of the defect is determined by observing the first very high peak occurring in the residual signals. A detectability index for defect location (DIDL) is proposed. Experimental validations are performed for the defects on the foot and web of a switch rail. The results of the ASED curves clearly indicate the existence of artificial defects, and the ICA-based residual signals show the location of the defects. The proposed method is found to be superior to conventional methods such as simple baseline subtraction and optimal baseline subtraction regarding the DIDL.

With the increasing importance of thin film in various applications, there is a need for new techniques with high surface sensitivity to measure physical properties. In this paper, we report results using a recently developed technique based on atomic force microscopy, temperature-dependent shear modulation force microscopy (SMFM), to investigate the surface glass transition. We test the effects of pressure under the tip, modulation frequency, and driving amplitude, which have been the subject of some controversy. The glass transition measurements on polystyrene and poly(methyl methacrylate) with different sample geometries demonstrate that the active volume probed by this technique has lateral dimensions on the order of the tip-sample contact radius. Applications to thin film glass transition measurements and surface segregation in long-chain/short-chain blends demonstrate the general utility of this technique.

Techniques based on the elasto-magnetic (EM) effect have been receiving increasing attention for their significant advantages in cable stress/force monitoring of in-service structures. Variations in ambient temperature affect the magnetic behaviors of steel components, causing errors in the sensor and measurement system results. Therefore, temperature compensation is essential. In this paper, the effect of temperature on the force monitoring of steel cables using smart elasto-magneto-electric (EME) sensors was investigated experimentally. A back propagation (BP) neural network method is proposed to obtain a direct readout of the applied force in the engineering environment, involving less computational complexity. On the basis of the data measured in the experiment, an improved BP neural network model was established. The test result shows that, over a temperature range of approximately −10 °C to 60 °C, the maximum relative error in the force measurement is within ±0.9%. A polynomial fitting method was also implemented for comparison. It is concluded that the method based on a BP neural network can be more reliable, effective and robust, and can be extended to temperature compensation of other similar sensors.