Construction and demolition waste (CDW) is one of the largest contributors to global waste streams, simultaneously posing significant environmental and resource management challenges. The management of CDW, particularly its potential for energy recovery and industrial symbiosis, has garnered increasing attention as part of a circular economy approach. This comprehensive review explores global practices in CDW management, analysing theoretical developments, technological advancements, and emerging resource recovery and reuse trends. Background: CDW accounts for more than a third of all waste generated in the EU. A wide variety of materials, such as concrete, bricks, wood, glass, metals, and plastics, make it a very un-homogenous waste stream with high potential for material recovery through different approaches. Methods: This review draws on an extensive analysis of scientific literature, case studies, and industry reports to assess current practices in the CDW stream and assessment of the feasibility of energy recovery, industrial symbiosis, and object reconstruction. Results: The originality of the current research is based on a Latvian case study on CDW management that provides valuable insights into household-level practices and progress towards relevant UN SDGs. Conclusions: Various CDW streams have an undeniable potential for valorization through various approaches. Currently, the most common approach is recovery and recycling, although CDW has the potential to broaden its application within the circular economy framework. For instance, industrial symbiosis is a solution that can not only boost the valorization of CDW but also significantly increase material circularity.

In this paper, it is shown that based on an analysis of the implemented functions of existing robot manipulators the task of automation of the safe capture of objects by a robot during the assembly process is poorly developed. In the process of analysis, there were discovered technological solutions to three main tasks for the development of a subsystem for capturing objects by a robotic manipulator: determination of the dimensions and shape of the capturing object; determination of the distance from the robot manipulator to all the points of the capturing object, determination of the capture point of the object and clarification of the distance to the robot manipulator. It is shown that all of the above tasks are not sufficiently solved. Therefore, it was proposed to develop a methodology for creating an intelligent software and hardware subsystem for capturing an object by robot manipulator. The developed methodology consists of six steps: obtaining a stereo image and building an in-depth map; determination of the distance from the robot manipulator to all the points of the object; determination of the contour of the object; determination of the capture point of the object and clarification of the distance to the robot manipulator; determination of the degree of capturing the object; determination of the movement of the manipulator to capture an object at the desired point. To find the capture point, it is proposed to use the contour search method on the object’s depth map, and to search for the finest part on the contour, limit it to a segment and find its middle point. To implement the algorithm for determination of the distance to the object, the degree of its capture and movement to the desired point, the dependencies of the calculations on the depth map and the physical characteristics of the manipulator are formalized. The capabilities of the StereoPi microprocessor are analyzed and its use for the hardware solution of the capture function by the robotic arm is proposed. The simulation of the intelligent software and hardware subsystem for capturing an object of complex shape has been performed. Conclusions are drawn about the independence of the developed subsystem from the type of object and its viewing angle by a robot manipulator. In addition, an increase in the accuracy of capturing the object by a robot manipulator equipped with an intelligent subsystem is shown in comparison with its existing analog. Potential problems in the implementation of the proposed methodology are highlighted.

In order to obtain knowledge about the target audience, the preparation process of w e a k l y-structured multi-dimensional data of sociological surveys were automated. The following techniques have been developed for automating data prepa-ration: machine representation, preprocessing of the data from the sociological surveys in order to clean and filter it, transformation of data into feature space based on a formalized research objective, nonlinear dimensionality reduction and vis u a li -zation of the multi-dimensional data. As research has shown, the procedures associated with obtaining of primary and secondary feature spaces are the most significant. The Orange3 framework, which includes component-based data mining software and is used as a module for Python, were used to create IT of preparing weakly structured multidimensional data of sociological surveys in the Data Mining system. Approbation of the automated preparation process of weakly-structured multi- dimensional data within the sociological surveys Data Mining system allowed to increase the reliability of decision-making on the lifestyle of the respondents compared to a sociologist of the master's qualification level and the respondents own responses.

This article examines the potential for enhancing the efficiency of inventory management and utilization of construction and demolition waste to create and apply secondary resources during the reconstruction of damaged infrastructure, including objects destroyed by war. This is achieved through the development of an intelligent information technology (IIT). The urgency of addressing this issue is underscored by the fact that large-scale reconstruction of damaged infrastructure demands significant expenditures of domestic and investment-based primary resources. Given the inevitable scarcity of these resources, the development of a secondary raw material market and the ability to extract valuable materials from waste streams for effective reuse become critically important. An analysis of European colleagues' experiences in effectively implementing a circular economy business model to address the limitations of primary resource utilization highlights the necessity of developing IIT for construction and demolition (C&D) waste inventory and utilization. Prior developer experience indicates that IIT can be implemented as a geoinformation system (GIS), enabling the identification of infrastructure objects through a knowledge base. The developed GIS knowledge base contains spatial and attribute data that align with the requirements of the circular economy business model, forming the foundation for a conceptual object inventory system. The intelligent component of the IIT is based on the use of large language models to analyze images of waste generation sites (Sources). This analysis involves interpreting visual cues, classifying types of construction materials, and providing an approximate volume assessment. The article also presents solutions for the preliminary classification of waste reception centers (Sinks) based on attribute and geospatial data, along with the development of an interactive Sinks-Sources map and the determination of efficient routes for transporting C&D waste to facilitate the creation of secondary resources.