In recent years, visual automatic non-destructive testing using machine vision algorithms has been widely used in industry. This approach for detecting, classifying, and segmenting defects in building materials and structures can be effectively implemented using convolutional neural networks. Using intelligent systems in the initial stages of manufacturing can eliminate defective building materials, prevent the spread of defective products, and detect the cause of specific damage. In this article, the solution to the problem of building elements flaw detection using the computer vision method was considered. Using the YOLOv5s convolutional neural network for the detection and classification of various defects of the structure, the appearance of finished products of facing bricks that take place at the production stage is shown during technological processing, packaging, transportation, or storage. The algorithm allows for the detection of foreign inclusions, broken corners, cracks, and color unevenness, including the presence of rust spots. To train the detector, our own empirical database of images of facing brick samples was obtained. The set of training data for the neural network algorithm for discovering defects and classifying images was expanded by using our own augmentation algorithm. The results show that the developed YOLOv5s model has a high accuracy in solving the problems of defect detection: mAP0.50 = 87% and mAP0.50:0.95 = 72%. It should be noted that the use of synthetic data obtained by augmentation makes it possible to achieve a good generalizing ability from the algorithm, it has the potential to expand visual variability and practical applicability in various shooting conditions.

The creation and training of artificial neural networks with a given accuracy makes it possible to identify patterns and hidden relationships between physical and technological parameters in the production of unique building materials, predict mechanical properties, and solve the problem of detecting, classifying, and segmenting existing defects. The detection of defects of various kinds on elements of building materials at the primary stages of production can improve the quality of construction and identify the cause of particular damage. The technology for detecting cracks in building material samples is of great importance in building monitoring, in pre-venting the spread of defective material. In this paper, we consider the use of the YOLOv4 convolutional neural network for crack detection on building material samples. This was based on the creation of its own empirical database of images of samples of aerated concrete. The number of images was increased by applying our own augmentation algorithm. Optimization of the parameters of the intellectual model based on the YOLOv4 convolutional neural network was performed. Experimental results show that the YOLOv4 model developed in this article has high precision in defect detection problems: AP@50 = 85% and AP@75 = 68%. It should be noted that the model was trained on its own set of data obtained by simulating various shooting conditions, rotation angles, object deformations, and light distortions through image processing methods, which made it possible to apply the developed algorithm in practice.

In the construction industry, along with traditional approaches for the visual and instrumental assessment of building materials, methods based on intelligent algorithms are increasingly appearing; in particular, machine learning and neural network technologies. The utilization of modern technologies enables us to enhance building processes to a new quality level, decreasing the construction pace without precision losses compared to traditional methods. This research introduces a novel method for characterizing crushed stone grain morphology using the application of specially designed three-dimensional computer vision neural networks to point data clouds. Flakiness affects the strength, adhesion, and location of crushed stone grains. So, calculating this indicator by determining the planar dimensions of each particle in the crushed stone is necessary for the assessment of its suitability for various types of construction work. Architectures based on PointNet and PointCloudTransformer are chosen as the basis for the classification algorithms. The input data were 3D images of crushed stone grains, the shapes of which were divided into needle-shaped, plate-shaped, and cubic classes. The accuracy quality metric achieved during the training of both models was 0.86. Using intelligent algorithms, along with grain analysis methods via manual selection, sieve analysis, or using special equipment, will reduce manual labor and can also serve as an additional source for verifying the quality of building materials at various stages of construction.

Currently, intelligent technologies are becoming both a topical subject for theoretical discussions and a proper tool for transforming traditional industries, including the construction industry. The construction industry intensively uses innovative methods based on intelligent algorithms of various natures. As practice shows, modern intelligent technologies based on AI surpass traditional ones in accuracy and speed of information processing. This study implements methods using convolutional neural networks, which solve an important problem in the construction industry—to classify crushed stone grains by their shape. Rapid determination of the crushed stone grain class will allow determining the content of lamellar and acicular grains, which in turn is a characteristic that affects the strength, adhesion, and filler placement. The classification algorithms were based on the ResNet50, MobileNetV3 Small, and DenseNet121 architectures. Three-dimensional images of acicular, lamellar, and cuboid grains were converted into single-channel digital tensor format. During the laboratory experiment, the proposed intelligent algorithms demonstrated high stability and efficiency. The total processing time for 200 grains, including the photo recording stage, averaged 16 min 41 s, with the accuracy reaching 92%, which is comparable to the results of manual classification by specialists. These models provide for the complete automation of crushed stone grain typing, leading to reduced labor costs and a decreased likelihood of human error.

The article discusses the possibilities of using effective methods of technical analysis to determinethe trend direction of financial assets on the multi-market platform MetaTrader 5.The advantages and expediency of using technical analysis methods in various areas of financialmarkets are shown by examples of works by Russian and foreign authors. In order to determinethe direction and strength of the trend, the insufficiency of the use of such technical indicators asBollinger bands (BB), MACD, MA, EMA, constructed according to various laws of the distributionof prices of financial assets, is justified. Graphical analysis tools are considered as a special caseof technical analysis, characterized by special visibility and visualization of the process understudy. The article provides examples of technical analysis figures, shows them to belong to thebasic types of graphical analysis. The concept of a modern trading strategy – innovative trading isconsidered, a comparative analysis of the trading strategy, its opportunities and risks is carriedout. The paper describes the developed software module in the form of a trading indicator on theMetaTrader 5 platform in Meta Quotes Language 5 (MQL5) in combination with a script in thehigh-level Python language for algotrading using technical analysis methods. The indicator isbased on the construction of support and resistance lines to determine the direction and strengthof the trend. An algorithm for determining the direction and strength of the trend with technicalanalysis tools has been developed, which as a result will improve the accuracy of the trend directionforecast by integrating a technical indicator into the system. For the experimental part of theprocess, a financial instrument was used – the EUR/USD currency pair, the total study period wassix months. As a result, the algorithm worked correctly in real time without human intervention fortwenty-four weeks. Based on the test results, it is possible to judge the prospects of research in thisarea for solving a large range of applied problems of financial mathematics and analytics.The application of the created indicator can be extended to any trading operations on variousfinancial instruments and their combinations. Рассмотрены возможности применения эффективных методов технического анали-за для определения направления тренда финансовых активов на мультирыночной плат-форме MetaTrader 5. Показаны преимущества и целесообразность применения методовтехнического анализа в разных сферах финансовых рынков на примерах работ российских изарубежных авторов. В работе для определения направления и силы тренда обосновананедостаточность применения таких технических индикаторов, как полосы Боллинджера(BB), MACD, MA, EMA, построенных по различным законам распределения цен финансовыхактивов. Рассмотрены инструменты графического анализа, как частный случай техниче-ского анализа, отличающийся особой наглядностью и визуализацией исследуемого процес-са. В статье приведены примеры фигур технического анализа, показана их принадлежатьк базовым типам графического анализа. Рассмотрено понятие современной торговойстратегии – инновационный трейдинг, проведен сравнительный анализ торговой страте-гии, ее возможности и риски. В работе приводится описание разработанного программно-го модуля в виде торгового индикатора на платформе MetaTrader 5 на языке Meta QuotesLanguage 5 (MQL5) в сочетании со скриптом на высокоуровневом языке Python для алгот-рейдинга с применением методов технического анализа. В основу индикатора заложенопостроение линий поддержки и сопротивления для определения направления и силы тренда.Разработан алгоритм определения направления и силы тренда инструментами техниче-ского анализа, что в результате позволит повысить точность прогноза направленноститренда путем интеграции технического индикатора в систему. Для экспериментальнойчасти процесса использован финансовый инструмент – валютная пара EUR/USD, общийпериод исследования составил шесть месяцев. В результате алгоритм корректно отрабо-тал в реальном режиме времени без участия человека в течение двадцати четырех недель.По результатам тестирования можно судить о перспективности исследований данногонаправления для решения большого спектра прикладных задач финансовой математики...