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.

An elasto-magnetic (EM) and magneto-electric (ME) effect based elasto-magneto-electric (EME) sensor has been proposed recently by the authors for stress monitoring of steel cables with obvious superiorities over traditional elasto-magnetic sensors. For design optimization and engineering application of the EME sensor, the design theory is interpreted with a developed model taking into account the EM coupling effect and ME coupling effect. This model is able to approximate the magnetization changes that a steel structural component undergoes when subjected to excitation magnetic field and external stress, and to simulate the induced ME voltages of the ME sensing unit located in the magnetization area. A full-scale experiment is then carried out to verify the model and to calibrate the EME sensor as a non-destructive evaluation (NDE) tool to monitor the cable stress. The experimental results agree well with the simulation results using the developed model. The proposed EME sensor proves to be feasible for stress monitoring of steel cables with high sensitivity, fast response, and ease of installation.

Reinforced concrete (RC) shear walls have been widely applied in high-rise buildings. DBSD (Displacement-Based Seismic Design) of shear wall structures requires more accurate deformation evaluation theories and methods. Boundary elements of RC walls have been found to have a significant impact on the seismic behaviours, but the strain-stress development details including plane section assumption and linear curvature distribution are seldom investigated by specialized experiments. To investigate the failure mechanism of shear walls and the influence of the boundary element, 6 RC wall specimens with different boundary elements are studied in this paper. Strain development data of the shear wall section and along wall height were collected during the loading procedure. The failure mechanism of the RC walls with different boundary elements is studied by discussing the development of the strain. Study emphasis is laid on the adaption of plane-section assumption during the whole service procedure, the curvature development along the wall height during the whole service procedure, the influence of boundary element to moment-curvature relationship. Test results indicate that boundary element can restrain the growth of the strain, enhance the horizontal bearing capacity, and improve the seismic resistant capacity. The study also re-evaluates the plane section assumption and linear curvature distribution currently used based on the results of the deformation of the shear walls by the experimental investigation of the shear walls with different boundary element.

Steel structures are widely used in railway infrastructures. Their stress state is the most important determinant of the safety of these structures. The elasto-magnetic (EM) sensor is the most promising for stress monitoring of in-service steel structures. Nevertheless, the necessity of magnetic excitation to saturation due to the use of a secondary coil for signal detection, keeps from its engineering application. In this paper, a smart elasto-magneto-electric (EME) sensor using magneto-electric (ME) sensing units to take the place of the secondary coil has been exploited for the first time. The ME sensing unit is made of ME laminated composites, which has an ultrahigh ME voltage coefficient and can measure the magnetic induction simply and precisely. Theoretical analysis and characterization experiments firstly conducted on the ME laminated composites showed that the ME sensing units can be applied in the EM sensor for improved performance in stress monitoring. A tension test of a steel bar was carried out to characterize our smart EME sensor and the results showed high accuracy and sensitivity. The present smart EME sensor is a promising tool for stress monitoring of steel structures in railway and other civil infrastructures.