Among nondestructive damage detection methods, impedance-based methods have been recognized as an effective technique for damage identification in many kinds of structures. This paper proposes a method to detect cracks in metal structures by combining electro-mechanical impedance (EMI) responses and artificial neural networks (ANN). Firstly, the theories of EMI responses and impedance-based damage detection methods are described. Secondly, the reliability of numerical simulations for impedance responses is demonstrated by comparing to pre-published results for an aluminum beam. Thirdly, the proposed method is used to detect cracks in the beam. The RMSD (root mean square deviation) index is used to alarm the occurrence of the cracks, and the multi-layer perceptron (MLP) ANN is employed to identify the location and size of the cracks. The selection of the effective frequency range is also investigated. The analysis results reveal that the proposed method accurately detects the cracks' occurrence, location, and size in metal structures.

This paper studies detecting and locating loose bolts using nonlinear guided waves. The 3D Finite Element (FE) simulation is used for the prediction of guided waves' interactions with loose bolted joints. The numerical results are verified by experimentally obtained data. The study considers bolted joints consisting of two bolts. It is shown that the guided waves' interaction with surfaces of a loose bolted joint generates Contact Acoustic Nonlinearity (CAN). The study uses CAN for detecting and locating loose bolts. The processed experimentally obtained data show that the CAN is able to successfully detect and locate loose bolted joints. A 3D FE simulation scheme is developed and validated by experimentally obtained data. It is shown that FE can predict the propagation of guided waves in loose bolts and is also able to detect and locate them. Several numerical case studies with various bolt sizes are created and studied using the validated 3D FE simulation approach. It is shown that the FE simulation modeling approach and the signal processing scheme used in the current study are able to detect and locate the loose bolts in imperfect bolted joints. The outcomes of this research can provide better insights into understanding the interaction of guided waves with loose bolts. The results can also enhance the maintenance and repair of imperfect joints using the nonlinear guided waves technique.

This study explores the use of the recently developed "strain-sensing smart skin" (S⁴) method for noncontact strain measurements on cement-based samples. S⁴ sensors are single-wall carbon nanotubes dilutely embedded in thin polymer films. Strains transmitted to the nanotubes cause systematic shifts in their near-infrared fluorescence spectra, which are analyzed to deduce local strain values. It is found that with cement-based materials, this method is hampered by spectral interference from structured near-infrared cement luminescence. However, application of an opaque blocking layer between the specimen surface and the nanotube sensing film enables interference-free strain measurements. Tests were performed on cement, mortar, and concrete specimens with such modified S⁴ coatings. When specimens were subjected to uniaxial compressive stress, the spectral peak separations varied linearly and predictably with induced strain. These results demonstrate that S⁴ is a promising emerging technology for measuring strains down to ca. 30 𝜇𝜀 in concrete structures.

This study investigates the bending behavior of a composite concrete slab roof with different methods of externally strengthing using steel plates and carbon fiber reinforced polymer (CFRP) strips. First, the concrete slab model which was reinforced with CFRP strips on the bottom surface of it is validated using experimental data, and then, using numerical modeling, 7 different models of square-shaped composite slab roofs are developed in ABAQUS software using the finite element modeling. Developed models include steel rebar reinforced concrete slab with variable thickness of CFRP and steel plates. Considering the control sample which has no external reinforcement, a set of 8 different reinforcement states has been investigated. Each of these 8 states is examined with 6 different uncertainties in terms of the properties of the materials in the construction of concrete slabs, which make 48 numerical models. In all models loading process is continued until complete failure occurs. The results from numerical investigations showed using the steel plates as an executive method for strengthening, the bending capacity of reinforced concrete slabs is increased in the ultimate bearing capacity of the slab by about 1.69 to 2.48 times. Also using CFRP strips, the increases in ultimate bearing capacity of the slab were about 1.61 to 2.36 times in different models with different material uncertainties.

In this study, the impact of assigned pixel labels on the accuracy of crack image identification of steel structures is examined by using an atrous separable convolution neural network (ASCNN). Firstly, images containing fatigue cracks collected from steel structures are classified into four datasets by assigning different pixel labels based on image features. Secondly, the DeepLab v3+ algorithm is used to determine optimal parameters of the ASCNN model by maximizing the average mean-intersection-over-union (mIoU) metric of the datasets. Thirdly, the ASCNN model is trained for various image sizes and hyper-parameters, such as the learning rule, learning rate, and epoch. The optimal parameters of the ASCNN model are determined based on the average mIoU metric. Finally, the trained ASCNN model is evaluated by using 10% untrained images. The result shows that the ASCNN model can segment cracks and other objects in the captured images with an average mIoU of 0.716.