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As an innovative technology, the impedance-based technique has been extensively studied for the structural health monitoring (SHM) of various civil structures. The technique’s advantages include cost-effectiveness, ease of implementation on a complex structure, robustness to early-stage failures, and real-time damage assessment capabilities. Nonetheless, very few studies have taken those advantages for monitoring the health status and the structural condition of wind turbine structures. Thus, this paper is motivated to give the reader a general outlook of how the impedance-based SHM technology has been implemented to secure the safety and serviceability of the wind turbine structures. Firstly, possible structural failures in wind turbine systems are reviewed. Next, physical principles, hardware systems, damage quantification, and environmental compensation algorithms are outlined for the impedance-based technique. Afterwards, the current status of the application of this advanced technology for health monitoring and damage identification of wind turbine structural components such as blades, tower joints, tower segments, substructure, and the foundation are discussed. In the end, the future perspectives that can contribute to developing efficient SHM systems in the green energy field are proposed.

The smart strand technique has been recently developed as a cost-effective prestress load monitoring solution for post-tensioned engineering systems. Nonetheless, during its lifetime under various operational and environmental conditions, the sensing element of the smart strand has the potential to fail, threatening its functionality and resulting in inaccurate prestress load estimation. This study analyzes the effect of potential failures in the smart strand on impedance characteristics and develops a 1D convolutional neural network (1D CNN) for automated fault diagnosis. Instead of using a realistic experimental structure for which transducer faults can be hard to control accurately, we adopt a well-established finite element model to conduct all experiments. The results show that the impedance characteristics of a damaged smart strand are relatively different from other piezoelectric active sensing devices. While the slope of the susceptance response is widely accepted as a promising fault indicator, this study shows that the resistance response is more favorable for the smart strand. The developed network can accurately diagnose the potential faults in a damaged smart strand with the highest testing accuracy of 94.1%. Since the network can autonomously learn damage-sensitive features without pre-processing, it shows great potential for embedding in impedance-based damage identification systems for real-time structural health monitoring.

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A model is developed for the thermoelastic restrained distortional buckling (RDB) of a steel joist in a composite beam in a steel-framed building that may take place during a compartment fire. The overall or member buckling mode must necessarily involve cross-sectional distortion, since the rigid concrete slab in the composite beam prevents the top flange of the steel joist from freely translating, rotating and twisting as would occur in conventional flexural-torsional buckling. The solution is based on a stiffness approach, with the buckling deformations being represented by a Fourier series and the Ritz method being invoked to determine an eigensolution for the critical temperature. High levels of axial restraint provided by cooler frame members can produce significant thermally induced pre-buckling compression, which can lead to early thermoelastic buckling. This significant axial force can lead to potential failure of the connections at the ends of the member, and premature buckling is advantageous as it can relieve the development of this large compressive action. The numerical solution is used to investigate the influence of the parameters affecting RDB of a composite beam in a fire.