A critical problem facing data collection in structural health monitoring, for instance via sensor networks, is how to extract the main components and useful features for damage detection. A structural dynamic measurement is more often a complex time-varying process and therefore, is prone to dynamic changes in time-frequency contents. To extract the signal components and capture the useful features associated with damage from such non-stationary signals, a technique that combines the time and frequency analysis and shows the signal evolution in both time and frequency is required. Wavelet analyses have proven to be a viable and effective tool in this regard. Wavelet transform (WT) can analyze different signal components and then comparing the characteristics of each signal with a resolution matched to its scale. However, the challenge is the selection of a proper wavelet since various wavelets with varied properties that are to analyze the same data may result in different results. This article presents a study on how to carry out a comparative analysis based on analytic wavelet scalograms, using structural dynamic acceleration responses, to evaluate the effectiveness of various wavelets for damage detection in civil structures. The scalogram’s informative time-frequency regions are examined to analyze the variation of wavelet coefficients and show how the frequency content of a signal changes over time to detect transient events due to damage. Subsequently, damage-induced changes are tracked with time-frequency representations. Towards this aim, energy distribution and sharing information are investigated. The undamaged and damaged simulated comparative results of a structure reveal that the damaged structure were shifted from the undamaged structure. Also, the Bump wavelet shows the best results than the others.

Vibration-based structural damage identification through optimization techniques has become an interesting research topic in recent years. Dynamic characteristics such as frequencies and mode shapes are used to construct the objective function. The objective functions based on only frequencies are not very sensitive to damage in large structures. However, objective functions based on both mode shapes and frequencies are very effective. In real measurement condition, the number of installed sensors is limited, and there are no economic reasons for measuring the mode shapes at all degrees of freedom. In this kind of circumstances, mode expansion methods are used to address the incompleteness of mode shapes. In this article, the system equivalent reduction and expansion process is applied to determine the unmeasured mode shapes. Two experimental examples including a cantilever beam and a truss tower are investigated to show system equivalent reduction and expansion process’ efficiency in estimating unmeasured mode shapes. The results show that the technique used for expansion is influential. Damage identification is formulated as an optimization problem, and the residual force vector based on expanded mode shapes is considered as an objective function. In order to minimize the objective function, grey wolf optimization and Harris hawks optimization are used. Numerical studies on a 56-bar dome space truss and experimental validation on a steel frame are performed to demonstrate the efficiency of the developed approach. Both numerical and experimental results indicate that the combination of the grey wolf optimization and expanded mode shapes with system equivalent reduction and expansion process can provide a reliable approach for determining the severities and locations of damage of skeletal structures when it compares with those obtained by Harris hawks optimization.

Energy harvesting from piezoelectric materials is quite common and has been studied for the past few decades, but, recently, there have been a lot of new advancements in harnessing electrical energy via piezoelectric materials. In this regard, several studies were carried out in electrochemistry and fluid flow. Furthermore, consideration of productive and valuable resources is important to meet the needs of power generation. For this purpose, energy harvesting from fluids such as wind and water is significant and must be implemented on a large scale. So, developing self-powering devices can resolve the problem like that, and piezoelectric materials are gaining interest day by day because these materials help in energy generation. This review paper discusses different techniques for harnessing energy from fluid flows using piezoelectric materials. In addition, various vibration-based energy-harvesting mechanisms for improving the efficiency of piezoelectric energy harvesters have also been investigated and their opportunities and challenges identified.

Energy harvesting from piezoelectric materials is quite common and has been studied for the past few decades. But recently, there have been a lot of new advancements in harnessing energy via piezoelectric materials. In this regard, several studies were carried out in analytical chemistry. This paper provides a detailed review of different piezoelectric materials, their structures, their fabrication processes, and their applications in analytical chemistry. Detection of the various gases percentage in ambient air is a valuable analytical chemistry technique. Additionally, the benefits of using piezoelectric materials, i.e., crystal for gas and liquid chromatography, virus detection including COVID-19 virus detection, water determination, trace metal analysis and the ability to measure micro weights with quartz crystal with some other applications are also described in this review. Energy harvesting is incredibly important and must be implemented on a large scale. So, developing self-powering devices can resolve the problems, and piezoelectric materials are gaining interest day by day because these materials help in energy generation.