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Guided ultrasonic waves are increasingly used in all those structural health monitoring applications that benefit from built-in transduction, moderately large inspection ranges, and high sensitivity to small flaws. This article describes a monitoring system based on the generation and detection of the guided ultrasonic waves from an array of sparse transducers. In a round-robin manner, ultrasonic waves are generated and measured from all possible different pairs of excitation and sensing transducers. The ultrasonic signals are then processed using continuous wavelet transform and empirical mode decomposition to extract few damage-sensitive features that enable the detection and localization of damage. With respect to most of the existing guided ultrasonic wave–based methods, the proposed approach does not require to record data from a pristine structure (baseline data), and damage is inferred by examining the selected features obtained from all the possible combinations of actuator–sensor pairs of the array. In this study, the method is validated using commercial finite element software to model the presence of 10 ultrasonic transducers bonded onto an aluminum plate. The results are promising and ongoing studies are focusing on the experimental validation and the application to other waveguides.

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Highly nonlinear solitary waves (HNSWs) are compact nondispersive waves that propagate in nonlinear medium such as straight chains of spherical particles. In the last two decades, these waves have found many applications in physics and engineering including lensing and nondestructive testing. In the study presented in this paper, we exploit the propagation of HNSWs along a metamaterial formed by granular chains to harvest energy from an oscillating structure. Specifically, an oscillator taps the metamaterial and creates a train of solitary waves along each chain. At the interface between the chains and a solid material, part of the acoustic energy refracts into the solid where it coalesces at a point. Here, a wafer-type transducer converts the focalized stress wave into electric potential. In the research presented in this study, we optimize some of the harvester’s parameters to maximize the electrical power output. The results demonstrate that the proper selection of parameters such as beads’ material and size, speed of the oscillator at the instant of the impact, and modulus of the solid increases, by several orders of magnitude, the amount of power that can be harvested.

This paper presents a method for detection and estimation of structural damage on the basis of modal parameters of a damaged structure using imperialist competitive algorithm. The imperialist competitive algorithm was developed over the last few years in an attempt to overcome inherent limitations of traditional optimize method. In this research, imperialist competitive algorithm has been employed due to its favorable performance in detection of structural damages. The performance of the proposed method has been verified through using a benchmark problem provided by the IASC-ASCE Task Group on Structural Health Monitoring and a number of numerical examples. By way of comparison between location and amount of damage obtained from the proposed method and simulation model, it was concluded that the method is sensitive to the location and amount of damage. The results clearly revealed the superiority of the presented method in comparison with energy index method.

In this paper, a novel and effective damage diagnosis algorithm is proposed to localize and quantify structural damage using incomplete modal data, considering the existence of some limitations in the number of attached sensors on structures. The damage detection problem is formulated as an optimization problem by computing static displacements in the reduced model of a structure subjected to a unique static load. The static responses are computed through the flexibility matrix of the damaged structure obtained based on the incomplete modal data of the structure. In the algorithm, an iterated improved reduction system method is applied to prepare an accurate reduced model of a structure. The optimization problem is solved via a new evolutionary optimization algorithm called the cuckoo optimization algorithm. The efficiency and robustness of the presented method are demonstrated through three numerical examples. Moreover, the efficiency of the method is verified by an experimental study of a five-story shear building structure on a shaking table considering only two sensors. The obtained damage identification results for the numerical and experimental studies show the suitable and stable performance of the proposed damage identification method for structures with limited sensors.

Incomplete sensed data in structures have made exact structural damage detection a serious challenge. In this paper, an effective method is presented for damage detection and estimation in structures based on incomplete modal data of a damaged structure via a pattern search algorithm. An objective function based on the condensed mass and stiffness matrices is formulated. The proposed method determines the damage to structural elements using optimization of the objective function by using pattern search algorithm. The performance of the presented method has been verified through two numerical examples, namely, a two-span continuous beam and a three-story plane frame with and without noise in the modal data containing several damages. Also, the effect of the discrepancy in mass and stiffness between the finite-element model and the actual tested dynamic system has been investigated. Furthermore, the experimental data from the vibration test of a mass–stiffness system are used for verification of the proposed approach. The results show that the presented method is sensitive to the location and severity of structural damage in spite of the incomplete modal data.