The transition from a surface forest fire to a crown fire was experimentally studied under laboratory conditions. Using noncontact infrared (IR) diagnostic methods within narrow spectral ranges of infrared wavelengths, the propagation speed of the fire front was determined, along with temperature changes at control points where the combustion transitions from a surface fire to a crown fire. The experiment was conducted under varying incoming airflow velocities and different canopy heights relative to the surface fire. In the infrared range, the radiation from the sample surfaces was recorded using a JADE J530SB thermal imaging camera equipped with an optical filter (2.5–2.7 μm), enabling temperature measurements within the range of 310–1500 K. To interpret the recorded radiation from the test samples, calibration data provided by the manufacturer of the narrowband optical filter was used.

Selecting a proper method for inspecting particular materials is an imperative challenge in the nondestructive testing (NDT) field. In this regard, comparing different techniques to reach the best defect detection efficiency is of great importance. Digital shearography and active thermography are among the new NDT techniques providing many advantages such as full-field, noncontact, accurate and high-speed inspection. In this paper, both methods are employed to be compared in the detection of subsurface defects in carbon fiber-reinforced plastics (CFRPs). In this respect, several CFRP specimens with artificial defects, including delamination and cracks with various depths and sizes, were prepared. After conducting the relevant experiments, the results revealed that both methods are efficient in NDT of CFRPs. Nevertheless, active thermography indicated superior performance in the inspection of delamination by detecting deeper defects. Shearography showed a better performance in the case of cracks and short defects. However, shearography required more test preparation and a longer heating time.

Cracks, spalling, cavities and other damages occur in the process of long-term service of concrete, so that the bearing capacity of the building decreases, which needs to be reinforced by the adhesive steel plate reinforcement method. However, debonding phenomenon can occur at the interface between steel plate and concrete, which affects the overall stiffness and load carrying capacity of the structure, so debonding detection at the steel-concrete interface is particularly important. In this paper, a reference-free Lamb wave-based identification detection method for steel plate-concrete debonding is proposed, in which the Hilbert energy spectrum is used as a damage factor. The method does not require a preset reference signal, which is obtained by comparing the debonding signals. Firstly, the simulation is carried out by finite element software, the circular sensor array is arranged on the surface of the steel plate, and each sensor acts as excitation and reception, the Hilbert energy spectrum of the signal is obtained, the damage coefficient is calculated, the probabilistic imaging algorithm is utilized to realize the localization of the debonding position and the imaging, and finally the simulation is verified by experiments. The results show the feasibility of the method in debonding identification detection.

This study proposes a deep learning-based damage recognition method using vibration excitation of flange bolts, focusing on damage feature extraction and classification control of vibration signals, which holds significant value for health monitoring applications. To address noise and nonlinear features in acoustic emission signals, the Mel-frequency cepstral coefficient (MFCC) is used for feature extraction. The ResNet-50 model is improved by integrating the convolutional block attention module (CBAM) and squeeze-and-excitation (SE) modules to enhance recognition accuracy. This innovative approach effectively overcomes the limitations of traditional methods in complex signal processing, improving both the precision and reliability of the recognition process. Experimental results show that the method performs excellently in bolt damage identification with an accuracy rate exceeding 98%, validating its effectiveness and robustness in acoustic emission signal analysis and damage detection. Furthermore, the method demonstrates strong versatility, capable of adapting to damage monitoring tasks under various working conditions, providing reliable technical support for the engineering application of bolt health monitoring in complex environments.

The effect of annealing temperature of nickel plastically deformed according to various schemes on the change of its microstructure and level of magnetic characteristics was investigated. The processes of recovery and recrystallization in the nickel structure are reflected quite informatively both in the change of such magnetic characteristics as coercive force and maximum magnetic permeability and in the change of the field dependence of differential magnetic permeability. In different structural states (deformed and recrystallized structures), the position of the peak of differential magnetic permeability changes, as does its height. Thus, the established patterns can be used to analyze the processes of nickel recrystallization and assess the change in its structural state during manufacturing or operation.

The article discusses the possibility of using the acoustic-electrical transformation method to detect cracks and mechanical compressive strength of concrete. Numerical and experimental studies of changes in the parameters of the electromagnetic response of model samples of concrete made of a cement-sand mixture with a crack to a deterministic pulsed acoustic impact are presented. It is shown that the presence of a crack is determined by changes in the amplitude-frequency parameters of the electromagnetic response from the sample. An example of determining the locations of weakening of the mechanical strength of a concrete construction beam based on the parameters of electromagnetic signals is given. The results of comparative tests for determining the mechanical compressive strength of concrete, obtained using a calibrated sclerometer and an acoustic-electric method, are shown. The results of monitoring the mechanical strength of concrete structures of an operating bridge crossing over a river are also presented based on the parameters of the electromagnetic response that arise during impact probing with acoustic pulses.

During the picking, storage, and transportation processes, collisions between fruits can cause mechanical damage and reduce the overall quality of the fruit. In order to ensure the quality of fruits, it is necessary to carry out nondestructive testing on fruits. This paper investigates a method for nondestructive evaluation (NDE) of early mechanical damage in apples using pulsed infrared thermography (PIRT). Thermal excitation was applied to apples, and temperature differences were captured with an infrared camera. Fast Fourier transform, principal component analysis, and morphological algorithms were used to process and analyze the images. The experimental results show that the morphological algorithm performs better than other algorithms in defect edge detection, enabling clear identification of defect features and reducing noise interference. We provide an efficient and accurate NDE solution for mechanical damage in apples, which is significant for improving the quality of agricultural products and extending their shelf life.

The growing demand for carbon fiber reinforced polymer (CFRP) pipes in industry and their susceptibility to impact damage have led to great focus on damage detection in these structures. This paper proposes a method for detecting impact damage in CFRP pipes using low-power ultrasonic thermography with sweep excitation. The bottom of ultrasonic transducer was designed into a curved surface to improve its coupling to the composite pipes. In accordance with the attributes of local defect resonance (LDR), the frequency band was ascertained by analyzing the displacement spectrum at the site of impact damage. The LDR frequencies were further validated from the thermal responses of ultrasonic thermography inspection and finite element analysis of the specimen eigenfrequencies. Finally, the method was used to detect various impact damage on CFRP pipes. Since the LDR frequency at the defect is covered by the frequency range of the narrowband sweep excitation, impact damages can be efficiently activated, resulting in an obvious temperature rise.

To model the wave field of an ultrasonic transducer in materials with strong anisotropy (monocrystalline alloys of turbine blades, composite materials, welded joints, etc.), a physically descriptive asymptotic representation is obtained for quasi-spherical body waves excited by a surface source in an arbitrarily anisotropic elastic half-space. The asymptotics is derived by the stationary phase method from the integral representation of the solution in terms of contour integrals of the inverse Fourier transform. The peculiarities of their derivation and numerical implementation are discussed on the examples of a transversely isotropic composite material and a monocrystalline nickel alloy with cubic anisotropy. The dependence of the stationary points on the direction is more complicated here than in the isotropic case, up to the appearance of multiple stationary points and folds, giving rise to additional wave fronts and caustics. A comparison is made with the plane waves described by eigensolutions of the classical Christoffel equation. It is shown that, despite the phenomenon of multiple wave fronts, varying the plane-wave orientation allows us to obtain the same group velocity vectors as for any of the waves described by the asymptotics.

This study investigates the application of acoustic emission (AE) signals for monitoring fatigue damage during high-cycle fatigue (HCF) tests and proposes a fatigue damage monitoring strategy based on the Bhattacharyya coefficient (BC). A comprehensive description of the AE signal processing workflow is provided, encompassing the acquisition of signal waveform data, the calculation of probability distributions, and the utilization of the BC to quantify the similarity between reference and true distributions. Comparative analysis with traditional infrared thermography demonstrates that BC exhibits superior sensitivity in HCF tests, effectively capturing the progressive stages of fatigue damage. Additionally, the study evaluates the influence of varying bin widths on BC calculations and identifies an optimal bin width (BW). The optimal bin width not only balances the monitoring sensitivity and stability but also reduces the influence of external noise.