Surfaces Of Building Structures Research Articles

METHODS AND PRACTICES OF TRAINING AND APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS FOR DETECTING VARIOUS DEFECTS ON THE SURFACES OF BUILDING STRUCTURES

The subject of research in this article is the practice of implementing and applying types of artificial intelligence – convolutional neural networks and data science - to detect various defects on the surfaces of building structures. The purpose of the study is to identify the advantages and disadvantages of implementing and using convolutional neural network technologies together with other software and hardware components to automate traditional methods of monitoring the technical condition of the external surfaces of buildings and structures. The article solves the following tasks: to substantiate the effectiveness of implementation of convolutional neural network technologies and data science methods; to apply them in practice with software and hardware technologies to automate traditional methods of performing work on detecting various defects on the surfaces of building structures in the construction and operation of buildings and structures; to show the problems and shortcomings of these methods and technologies. To solve the tasks, an integrated approach was used with the use of general scientific and special research methods (analysis, explanation, generalisation, comparison). The following results have been obtained: the features that affect the accuracy of the analysis of the collected data used by neural convolutional network technologies to detect various defects on the surfaces of building structures have been identified; practices and methods for more efficient and accurate application of this technology have been reflected. Conclusions: the study allowed to identify the practical opportunities and problems that exist in this technology; recommendations for the effective use of this technology were developed; factors influencing the more efficient use of this technology in industry were identified.

Computer vision-based 3D coordinate acquisition of surface feature points of building structures

In the present structure health monitoring work, regular monitoring of the deformation and displacement of the target building structure is an important means to evaluate the safety of the structure. Aiming at the difficulties of traditional measurement methods such as complex operation, high cost and too few monitoring points, this paper proposes a method based on computer vision to obtain 3D coordinates of surface feature points of building structures. Based on the principle of restoring the target structure from the camera motion, the sparse point cloud model of the target building is initially reconstructed and the attitude information of each camera is obtained simultaneously. The depth information of each pixel in the original image is obtained by trial matching based on the principle of pole line search, and corrected and optimized based on the consistency of optics and geometry. Based on the original sparse point cloud, the depth information of each image was fused and reprojected based on the depth map registration principle to reconstruct the dense point cloud model of the target building. Finally, the 3D coordinates of the target building surface points are obtained and measured based on the scale factor method. In order to verify the feasibility of the method, seven buildings of different sizes were selected for field measurement and compared with manual measurement results. The results show that the measurement results of the proposed method are almost the same as those of manual measurement, and the error level fully meets the requirement of the error limit of the general structural health monitoring work. The method described in this paper obtains a large number of 3D coordinates of detection points on the surface of building structures in a non-contact form at one time, improves the flexibility and intensity of measurement point layout in the actual measurement work, and its operation is simple and low cost.

Analysis of Linear Surfaces of Building Structures

Purpose of reseach is to analyze the practice in the application of surfaces formed by the movement of a straight line. It is known that among the second-order surfaces cones, cylinders, hyperboloids of one sheet and hyperbolic paraboloids, as well as lines represented in the polar coordinate system in the form of intricate shapes that can be represented in space by the above-mentioned surfaces, adding a third dimension, have rectilinear generators. The strength resulting from covering each point of the listed surfaces with straight lines from different families does not make the structure heavier but strengthens it and makes it light compared to monoliths without reinforcements made of other materials, in which stability is not based on Shukhov calculation formulas. Methods Finding families of rectilinear generators for second-order surfaces calculation of which is based on the separation of equations that represent a second-order surface as a difference of squares in one part of the equation and as a product with an arbitrary parameter in the other part. Results. Analyzing second-order surfaces, we came to the conclusion that cones, cylinders are prone to this method of Shukhov calculations; equation of the form F (x,y)=0 in space defines a cylindrical surface whose generators are parallel to axis oz. Similarly, F (x, z)=0 defines a cylindrical surface with generators parallel to axis oy and F (y;z)=0 is a cylindrical surface with generators parallel to axis ox. A hyperboloid of one sheet, hyperbolic paraboloid, i.e. 10 surfaces out of 14, make up more than 70%. Conclusion. As a result of applying these formulas for calculating reinforced building structures, city buildings will acquire a new appearance, which will create a comfortable environment for residents, as well as lead to saving construction material resources.

ТЕПЛОВИЗИОННЫЙ МЕТОД ОПРЕДЕЛЕНИЯ ВЛАЖНОСТНЫХ ПОЛЕЙ ПОВЕРХНОСТЕЙ СТРОИТЕЛЬНЫХ КОНСТРУКЦИЙ ЗДАНИЙ И СООРУЖЕНИЙ

The current issues of excess heat losses by construction projects are identified and analyzed. It is established that the heat loss of buildings depend on the thermal qualities of external building structures of different materials, the main of which are density, humidity, thermal conductivity. In this regard, practical methods for identifying temperature and humidity fields of the surfaces of building structures in the mode of their operation are of particular importance. The technical capabilities of modern thermal imaging equipment for obtaining and processing thermograms of thermal imaging of the controlled object are presented. A practical example of thermal definition and analysis of temperature and humidity fields of translucent enclosing building structures in the form of a window in a window opening is given. It is confirmed that thermal imaging diagnostics is able to state the distribution of heat losses in the control zone. On the basis of this knowledge, measures are developed to reduce them (alignment) and subsequently assess the effectiveness of their implementation