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In coastal areas, transportation infrastructure using reinforced concrete beam structures always works in environmental conditions with high corrosive agents, under the effect of load, the level of corrosion of steel reinforcement in the structure is high. The beam condition becomes more severe, which leads to a decrease in the beam's bending resistance. The level of corrosion of steel reinforcement in reinforced concrete beam structures depends on many influencing factors. These factors need to be seriously researched and evaluated to provide early warning results for designer, construction worker, operater for exploit the project safely and effectively. This article presents the results of research evaluating the influence of concrete strength on the level of corrosion of steel reinforcement in reinforced concrete beam structures, often taking into account the effect of load causing beam bending. Reinforced concrete beam samples were made with concrete at different strength levels: C30, C40 and C50 MPa; Then it was loaded with a yoke system with a loading level equivalent to 0.8Pmax of the destructive strength of the concrete sample. After the yokes were loaded, the beam samples were subjected to accelerated corrosion testing in a highly concentrated salt water environment. concentration equivalent to 3.5% NaCl for a period of 30 days, equivalent to an actual exploitation time of about 35 years. The corrosion rate of steel reinforcement is increased by supplying direct current to create an electrolytic corrosion environment, ensuring that the steel bars are corroded equivalent to the estimated calculated time. Beam samples after accelerated corrosion testing were evaluated for damage in terms of reinforcement volume.

Introduction. The signal recognition task for the purposes of RF spectrum management can be solved using a signal recognition algorithm with detection at two intermediate frequencies. This algorithm is based on time–frequency analysis using fast Fourier transform (FFT) and signal envelope processing. Due to the relative simplicity of transformations, this algorithm is implemented on commercially available field programmable gate arrays and allows processing received signals in near real-time. However, the justification of the algorithm parameters providing effective signal recognition by the criterion of minimizing the signal-to-noise ratio (SNR) has not performed so far. Aim. Justification of parameters of the developed signal recognition algorithm, providing the minimum required SNR at the algorithm input. Materials and methods. The efficiency of the developed algorithm was estimated by computer simulation in the MATLAB environment. Results. The influence of the parameters of functional blocks and received signals on the efficiency of the developed algorithm was investigated. For chirp, simple pulse, binary, and quadrature phase shift keying signals, the following parameters are recommended: a pulse duration of 5…20 μs; a chirp rate of 0.8…24 MHz/μs; a code duration of 0.5…1 μs. For these signal parameters, the parameters of the algorithm ensuring its efficiency according to the given criterion are as follows: the number of FFT points equals 1024; the Hamming weight window; bandwidths of band-pass filters are 4 MHz; signal envelope amplitude averaging coefficient equals 0.15…0.25. Conclusion. The algorithm with the scientifically valid parameters can be used for recognition of signals at the input minimum SNR for the given types and parameters of signals.

In the realm of urban construction employing excavation techniques, safeguarding existing underground structures from detrimental consequences arising from surface construction operations poses a formidable challenge. The reduction of loads due to excavation activities can induce unintended responses, potentially jeopardizing subterranean infrastructure, particularly high-safety-demanding structures like Tunnel Boring Machine (TBM) tunnels. This article introduces an uncomplicated method for ascertaining the axial displacement of TBM tunnels amidst concurrent surface excavation activities. Primarily, the approach entails the identification of stress variations encountered during soil excavation at the tunnel face. Subsequently, employing the solutions derived for the determination of tunnel deformation subjected to concentrated loads, the deformation incurred by the tunnel due to alterations in excavation-induced stress is quantified. The analytical outcomes are meticulously juxtaposed against results generated from a three-dimensional computational model. The comparative analysis demonstrates that the displacement values and axial deviations calculated using the proposed analytical method exhibit only marginal disparities of 4,3% and 1%, respectively, when compared to those obtained through finite element analysis. This study underscores the efficient predictive capabilities of the analytical method in assessing tunnel deformations, enabling a preliminary estimation of critical parameters associated with the excavation pit. These findings have significant implications for mitigating adverse impacts on existing subterranean infrastructure in densely populated urban areas.

Introduction. Systematic errors of microelectromechanical (MEMS) inertial sensors, such as those related to zero drift, scale factor, and nonorthogonality of sensitive axes, are the main sources of errors in strapdown inertial navigation systems (SINS). Uncompensated sensor errors accumulate over time as motion state errors, thus reducing the overall accuracy of SINS. Consequently, calibration of inertial sensors is a relevant research task. The disadvantage of existing sensor calibration methods consists in a strict requirement for the initial alignment of sensitive sensor axes relative to a reference coordinate system, which complicates the entire process of calibration. Therefore, alternative methods for MEMS sensor calibration should be developed.Aim. To develop a calibration algorithm for microelectromechanical (MEMS) sensors, which allows calibrating sensors regardless of the angular orientation of the sensor axes relative to a reference coordinate system at the initial installation, as well as to simplify the design of testing tools.Materials and methods. Publications in national and international journals on the theory of calibration of inertial sensors were reviewed. A calibration algorithm was developed based on the least squares method.Results. An algorithm for determining the calibration parameters of sensors regardless of the initial alignment of the sensor sensitive axes relative to a reference system was developed. A simple alternative design for testing MEMS sensors was proposed.Conclusion. The method of calibrating MEMS inertial sensors proposed in this work differs from conventional calibration methods by increased reliability of the results and a simplified design of testing tools. Importantly, the results of determining the calibration coefficients of a micromechanical accelerometer (MMA) do not depend on its angular position relative to a geographic coordinate system (GСS). This works contributes to improving the accuracy of SINS based on MEMS inertial sensors.

Introduction. An assessing of the sportsman's physiological reserve (PR) and its dynamics is important when planning and carrying out a training, forecasting sportsman's results. An importance of this problem increases in highperformance sports, and energy consumption sports. A complexity of solving of this problem is caused by the requirement of taking into account of the complex of the biomedical parameters and formation of an integral parameter. This parameter reflects functioning of various body systems which provide significant income to the sportsman’s result achievement. Objective. Development of the method and the system of PR assessing allowing complex investigation of the PR during the training process. Method and materials. For achievement of the aim the tasks were formulated and solved using methods of biomedical research and engineering, mathematical processing and analysis of the diagnostically valuable parameters. Results. The complex of the biomedical parameters reflecting sportsman’s body metabolism in condition of physical exercises is formed. They are the heart rate, the heart rate variability, the pulse frequency, the systolic and diastolic pressure, the respiratory rate, the blood saturation, and the stress index by Baevsky. It is important for PR assessing to assess parameters characterizing sportsman’s physiological reserves at the current moment and its dynamics. The circle diagram is proposed for taking into account of all mentioned parameters and its variation dynamics. The value of the integral PR parameter is an area of a polygon, which is obtained on the circle diagram using normalized values of the diagnostically significant parameters. The method of biomedical investigation of the sportsman and the method of PR assessing based on the complex of the body system parameters are developed. The scheme of assessing of sportsman's body physiological reserves before and after the training is proposed. The scheme allows to assess not only sportsman's body energy consumption during the training but also its recovery after the training. General structures of the biotechnical system and a structures of systems of picking up, registration, processing, and analysis of biomedical signals for assessing of sportsman's physiological reserves are developed. Special attention is given to the development of a wearable device for synchronous registration of the complex of biomedical parameters and algorithms of assessing of the diagnostically significant parameters of sportsman's body physiological reserves. Conclusion. The proposed method of sportsman's physiologic reserves investigation and the structure of the system with spatially distributed architecture allow sport medicine doctor and coach to assess an efficiency of sportsman's training process with respect to his potential capabilities, and efficiently control the training process.

Continuous and steady running of health status remote monitoring systems is essential not to omit episodes of acute exacerbation of chronic disease. Running time of such systems is largely determined by performance capabilities of the patient's wearable system elements. To ensure its long-term operation and efficient performance, the monitoring system must have multilayered structure with the elements realizing recording and picking off biomedical signals, signal processing and analysis, estimation of patient current condition, dynamics of the disease and its prognosis. For this purpose, it is necessary to use smart monitoring algorithms. A specific feature of such algorithms is change of the number of channels used for biomedical signal recording and processing according to the change of patient’s condition. To detect the exacerbation first symptoms by means of the patient's wearable computer, additional channels are activated for recording biomedical signals used to evaluate the expanded complex of diagnostically significant parameters of the disease and their integration when specifying the patient's condition. The system and intelligent monitoring algorithm is tested with the use of heart rate remote control and atrial fibrillation episode detection system. The testing results of the developed system and algorithm are discussed.