Sharif Digital Repository

To perform active structural health monitoring, guided Lamb waves for damage detection have recently gained extensive attention. Many algorithms are used for damage detection with guided waves and among them, the delay-and-sum method is the most commonly used algorithm because of its robustness and simplicity. However, delay-and-sum images tend to have poor accuracy with a large spot size and a high noise floor, especially in the presence of multiple damages. To overcome these problems, another method that is based on sparse reconstruction can be used. Although the images produced by the sparse reconstruction method are superior to the conventional delay-and-sum method, it has the challenges... 

Damage detection and structural health monitoring using the electromechanical impedance method has been accepted as an effective technique between various approaches of nondestructive evaluation. Many efforts have been made on experimental methods for obtaining the impedance of structures. However, expensive experimental methods encourage researchers to develop theoretical models. In this paper, a new theoretical model is developed for damage detection of L-shaped beams, which are basic components in frame structures, with an embedded piezoelectric wafer active sensor. For this purpose, a chirp signal of voltage is used to activate a piezoelectric patch for inducing local strains that lead... 

The electro-mechanical impedance (EMI) method has been accepted as an effective technique for detecting damages in the Structural health monitoring (SHM). EMI at any point of the structure depends on material properties, geometry and boundary conditions that all appears in dynamic stiffness of the structure. In spite of the expensive experimental methods for measuring the mechanical impedance, or the cheaper one electromechanical impedance, of structures, various analytical methods could be substitutions for them. In this paper, an analytical method is developed to obtain the EMI response of L-shaped beams through calculating the dynamic stiffness of the structure. To verify the model, an... 

This paper proposes an Age-Dependent Clustering (ADC) structure to be used for prognostics. To achieve this aim, a step-by-step methodology is introduced, that includes clustering, reproduction, mapping, and finally estimation of Remaining Useful Life (RUL). In the mapping step, a neural fitting tool is used. To clarify the age-based clustering concept, the main elements of the ADC model is discussed. A Genetic algorithm (GA) is used to find the elements of the optimal model. Lastly, the fuzzy technique is applied to modify the clustering. By investigating a case study on the health monitoring of some turbofan engines, the efficacy of the proposed method is demonstrated. The results showed... 

Triboelectric nanogenerators (TENG) have rapidly advanced in self-powered sensing in transportation engineering owing to its great sensitivity, high energy harvesting performance, simple structures, and low cost. TENG can be used to improve walker and car safety, detect driver fatigue, assess traffic conditions, and extend the useful life of roads. However, collecting sufficient energy have become a significant issue, limiting the lifespan of such self-powered sensors. A new generation of self-powered sensors bridges the gap between the energy acquired and the energy needed for sensing, computing, storage, and transmission. This article provides an overview of the recent progress of TENG... 

Crack detection is a vital component of structural health monitoring. Several computer vision-based studies have been proposed to conduct crack detection on concrete surfaces, but most cases have difficulties in detecting fine cracks. This study proposes a deep learning-based model for automatic crack detection on the concrete surface. Our proposed model is an encoder–decoder model which uses EfficientNet-B7 as the encoder and U-Net’s modified expansion path as the decoder. To overcome the challenges in the detection of fine cracks, we trained our model with a new training strategy on images extracted from an open-access dataset and achieved a 96.98% F1 score for unseen test data. Moreover,...