The paper emphasizes an effective quantification of hidden damage in composite structures using ultrasonic guided wave (GW) propagation-based structural health monitoring (SHM) and an artificial neural network (ANN) based active infrared thermography (IRT) analysis. In recent years, there has been increased interest in using a global-local approach for damage localization purposes. The global approach is mainly used in identifying the damage, while the local approach is quantifying. This paper presents a proof-of-study to use such a global-local approach in damage localization and quantification. The main novelties in this paper are the implementation of an improved SHM GW algorithm to localize the damages, a new pixel-based confusion matrix to quantify the size of the damage threshold, and a newly developed IRT-ANN algorithm to validate the damage quantification. From the SHM methodology, it is realized that only three sensors are sufficient to localize the damage, and an ANN- IRT imaging algorithm with only five hidden neurons in quantifying the damage. The robust SHM methods effectively identified, localized, and quantified the different damage dimensions against the non-destructive testing-IRT method in different composite structures.

Glass fiber-reinforced polymer structures (GFRPS) are widely used in civil and mechanical fields due to their light weight and corrosion resistance. However, these structures are prone to damage with very-low-energy impacts. The reliability of such structures is of prime importance before their installation and usage. This study aimed to identify, visualize, localize, and verify multiple barely visible impact damage (BVID) in a GFRPS using a combination of guided waves (GW)-based online structural health monitoring (SHM) and thermal strain-based nondestructive testing (NDT) approaches. Global NDT techniques like the use of a laser Doppler vibrometer (LDV) and digital image correlation (DIC) were used in the experimental analysis. The effectiveness of the experimental LDV-GW process was also checked numerically with the spectral element method (SEM). A threshold-based baseline free SHM approach to effectively localize the damages was proposed along with quick DIC verification of composite structure with thermal loading based on short-pulse heating as an excitation source. This study analyzed combined experimental- and numerical-based SHM-NDT methods in characterizing the multiple BVIDs located in a GFRPS.

The aim of the presented examination is condition-monitoring of GFRP-reinforced concrete structural members using elastic wave propagation. As an example, a deck slab is selected. The deck slab is made of concrete of the targeted C30/37 class under three-point bending. During loading cycles, the specimen is observed with a digital image correlation (DIC) system, which enables calculation of the strain field. The measuring setup consists of two Baumer 12.3 Mpx cameras with VS-1220HV lenses, combined in a Q400 system by Dantec Dynamics GmbH. Elastic waves are also measured based on signals recorded with PZT (lead–zirconate–titanate) sensors. Additionally, the typical crack-opening measurements are made. The appearance of a crack and its growth causes changes in both the shape and amplitude of the registered signals. However, the changes are not obvious and depend on the location of the sensors. Due to the impossibility of determining simple parameters with respect to disturbingly wide cracks, for damage detection, an artificial neural network (ANN) is applied. Perfect determination of the specimen’s condition (100% properly classified patterns) is possible based on whether the element is under loading or not.

Bolted connections, commonly applied in civil engineering structures, have many advantages. According to current trends, bolted connections in steel structures are designed as prestressed ones. Unfortunately, precise control of the prestressing forces is difficult, while the loosening (due to, e.g., dynamic interactions) may be dangerous for the entire structure. There are many control methods applied in the determination of the tightening level, among which are vision-based methods. The methods described so far enable—thanks to image processing—damage detection in connections with visible connectors. The level of the considered loosening was significant—in many cases, changes in connectors were visible with the naked eye, whereas the procedure presented here enables the detection of very small changes, impossible to detect without manual inspection of every single connector. It is not necessary to observe the connectors directly, but the near surrounding of the node should be visible. As a measurement technique, Digital Image Correlation (DIC) was used. The applied measurement method and the high sensitivity of the presented procedure makes the presented research original. The currently presented procedure, employing Artificial Neural Networks, based on the laboratory examination of an example of one selected beam-to-column connection of a two-story steel portal frame, was perfect in the detection of a change and in the determination of the number of loosened rows, 95%, and their location, 94%, with the number of false alarms below 1%.

The paper presents the experimental research and numerical simulations of reinforced concrete beams under torsional load. In the experimental tests Digital Image Correlation System (DIC System) Q-450 were used. DIC is a non-contact full-field image analysis method, based on grey value digital images that can determine displacements and strains of an object under load. Numerical simulations of the investigated beams were performed by using the ATENA 3D – Studio program. Creation of numerical models of reinforced concrete elements under torsion was complicated due to difficulties in modelling of real boundary conditions of these elements. The experimental research using DIC can be extremely useful in creating correct numerical models of investigated elements. High accuracy and a wide spectrum of results obtained from experimental tests allow for the modification of the boundary conditions assumed in the numerical model, so that these conditions correspond to the real fixing of the element during the tests.