Tubular bones are layered waveguide structures composed of soft tissue, cortical and porous bone tissue, and bone marrow. Ultrasound diagnostics of such biocomposites are based on the guided wave excitation and registration by piezoelectric transducers applied to the waveguide surface. Meanwhile, the upper sublayers shield the diseased interior, creating difficulties in extracting information about its weakening from the surface signals. To overcome these difficulties, we exploit the advantages of the Green’s matrix-based approach and adopt the methods and algorithms developed for the guided wave structural health monitoring of industrial composites. Based on the computer models implementing this approach and experimental measurements performed on bone phantoms, we analyze the feasibility of using different wave characteristics to detect hidden diagnostic signs of developing osteoporosis. It is shown that, despite the poor excitability of the most useful modes associated with the diseased inner layers, the use of the improved matrix pencil method combined with objective functions based on the Green’s matrix allows for effective monitoring of changes in the elastic moduli of the deeper sublayers. We also note the sensitivity and monotonic dependence of the resonance response frequencies on the degradation of elastic properties, making them a promising indicator for osteoporosis diagnostics.

One of the limitations of using glued connections in practice is related to the need for connection quality control. Still, the need for the non-destructive quality control of finished products to determine the compliance of the developed structure with the designed one still exists. Considering the small amount of research on timber–concrete composites with glued connections, there is a lack of research on non-destructive methods for the quality control of rigid connections in timber–concrete composite structures. During the literature analysis, no information was found on the possibilities of testing the quality of the rigid timber-to-concrete connection. Therefore, two well-known methods—operational modal analysis and ultrasonic testing—were tested to verify the possibilities of applying these methods in determining defects in the rigid glued connection between the concrete and timber layers in the timber–concrete composite structures. A series of small-scale specimens produced by the stone chips method with and without artificially made defects in the timber-to-concrete adhesive connection was tested by both methods. Operational modal analysis shows significant changes in mode shape, frequency values, and spectral density diagrams. Despite the sufficiently large reflection of the ultrasonic signal on the timber and concrete boundary, the transmitted signal is sufficient to perform local ultrasonic tests for detecting defects in the adhesive connection. Thus, it is concluded that the principles of both methods can be applied in practice, and further research is needed to develop testing technology.

The aim of this article is the development of a new artificial intelligence (AI) system for the condition assessment of concrete structures. To study the process of concrete degradation, the so-called spatiotemporal waveform profiles were obtained, which are sets of ultrasonic signals acquired by stepwise surface profiling of the concrete surface. The recorded signals at three frequencies, 50, 100 and 200 kHz, were analyzed and informative areas of the signals were identified. The type of the created neural network is a multilayer perceptron. Stochastic gradient descent was chosen as the learning algorithm. Measurement datasets (test, training and validation) were created to determine two factors of interest—the degree of material degradation (three gradations of material weakening) and the thickness (depth) of the degraded layer varied gradually from 3 to 40 mm from the surface. This article proves that the training datasets were sufficient to obtain acceptable results. The built networks correctly predicted the degree of degradation for all elements of the test dataset. The relative error in prediction of a thickness of degraded layer did not exceed 3% in the case of a thickness of 25 mm. It is shown that the results for the Fourier amplitude spectra are significantly worse than the results of neural networks built based on information about the measured signals themselves.

Cortical bone plays a crucial role in bone strength in the context of osteoporosis. Thinning of the cortex and intracortical porosity developing from the endosteum are the main independent indicators of osteoporosis-related fractures risk, which can be separately quantified by advanced imaging techniques. Numerous studies have shown the sensitivity of ultrasonic guided waves to changes in the thickness and porosity of cortical bone both in models and in human subjects. The present study demonstrates feasibility of the pattern recognition approach to separate determination of factors of diagnostic interest - cortical thickness (CTh) and intracortical porosity as the relative thickness of the porous layer (rPTh), where a set of guided waves velocities at different frequencies are used as evaluation criteria. The experiments were carried out on acrylic plastic plates as compact bone phantoms of different thicknesses with simulated pores in the form of CNC-drilled holes from below. The distance-time slopes of fast and slow wave packets at 250, 100 and 50 kHz were obtained as distance-time slopes in the corresponding spatiotemporal waveform profiles built by stepwise acquisition of ultrasonic signals. The evaluation criteria (distance-time slopes) in the training dataset formed the decision rules - 3-dimensional surfaces of the criterion distribution in the field CTh-rPTh. For the test object, the intersection of the segments of the probabilistic solution according to three decision rules gave the evaluation answer. The method allowed us to determine the values of both factors CTh and rPTh with reasonable diagnostic accuracy at different thicknesses of the covering soft tissues. The proposed approach can be adapted for the assessment of osteoporosis after obtaining an appropriate database in human studies.