International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

This article presents the development process of Custom GPT based on GPT-4o for paid users and GPT-4 for non-paid users to promote and explain climate analysis and the use of GIS technologies in scientific projects. The bot’s work will be as a communication system, offering detailed scientific explanations on the topic, navigation and pointing to additional resources such as web pages, reports and visualizations. The article examines the planning of the bot, the structure and volume of the submitted information, as well as the possibilities of expanding its functions in the future. Potential additional tasks that the bot could take on, such as automating processes and assisting with data work, are being explored. The conclusions highlight the benefits of using GPT for the promotion of scientific results and the possibilities for future development of its functionalities.

This paper investigates the inverse problem for thermal bridges – determining design parameters (such as material, geometry, thermal resistance R of the components) of the bridge at a known Psi-factor. By using artificial neural networks, a thermal bridge type IF (wall-floor connection) has been considered to evaluate the effectiveness of the methodology. The results show that the approach provides a fast and accurate way to predict the optimal parameters that meet specific energy efficiency requirements. In the future, this approach could help to determine the parameters of thermal bridges using thermographic images non-invasively.

The publication examines the use of artificial neural networks to calculate the linear thermal conductivity (Psi-factor) of thermal bridges given various parameters, such as geometrical data and the thermal resistance R of the thermal bridge components. The neural network is trained on examples of IF, IW, and B thermal bridges, considering the straightforward task of determining Psi using given parameters. The neural network training results show high accuracy in calculations – RMSE is 1.132% on training data and 1.1423% on test data, and the correlation coefficient (R²) is around 0.9997 for both data sets. The applicability of the approach to seismic conditions in the Balkans is assessed.

This paper examines an innovative approach for modeling the influence of climate parameters on transport infrastructure using artificial neural networks. Through them, detailed climatic data are generated by geographical positions and monthly and annual maps are created for Bulgaria’s territory using the following parameters: surface temperature, diffuse fraction, horizontal solar irradiation, and average albedo of the terrain. Average ground temperature and monthly solar irradiation are essential for maintenance planning and developing strategies to adapt to extreme weather conditions, such as heat waves or frost, which can affect the condition and performance of the road surface. Average monthly temperatures can be used to design effective systems to prevent icing of road surfaces and improve drainage systems. By demonstrating the capabilities of accurate modeling and analysis, this paper highlights the importance of applying artificial neural networks in planning and improving the resilience of transport infrastructure against climate change.

This paper examines an approach using artificial neural networks for innovative modeling of solar data that is needed to realize solar applications for transport infrastructure purposes. Through this modeling, detailed solar data are generated by geographical positions, and monthly and annual maps are created for the territory of Bulgaria for horizontal solar irradiation with its diffuse and direct components and for inclined and reflected solar irradiation, according to Norio Igawa’s model. Diffuse fraction and horizontal and inclined solar irradiation can be helpful in designing solar applications in road infrastructure, such as power signaling systems and street lighting. By demonstrating the capabilities of accurate modeling and analysis of solar data, this paper highlights the importance of applying artificial neural networks in planning and improving the resilience of transport infrastructure against climate change. Using solar energy in transport infrastructure reduces carbon emissions and strengthens environmental sustainability.

The application of old climate data in the design of buildings and transport infrastructure that will be used in the future is problematic because of climate change. On the other hand, it is currently unknown what the success of human actions to limit them will be. This leads to some uncertainty about the exact parameters of the climate until the year 2100. This requires a new approach to design and construction in the context of climate change. The article examines the impact of RCP (Representative Concentration Pathway) climate scenarios and regional climate models based on them, on design and construction for energy efficiency and sustainability. The significance of different RCP scenarios and the need for spatiotemporal scaling of climate data are discussed. The disadvantages of using a static representative climatic year (as it is in the Bulgarian regulatory documents for energy efficiency) are analyzed compared to its dynamic and adaptive variant.

The article focuses on issues related to the assessment and interpretation of existing data on crisis events in the context of transport infrastructure. In the face of global climate change, analyzing these data becomes increasingly complex and requires in-depth consideration. The main challenges the paper addresses include the lack of comprehensive and up-to-date data, limited opportunities to analyze causal relationships between climate events and their impact on transport infrastructure, and shortcomings in existing analysis methodologies. The authors call for the development of new, more efficient methods of data collection and analysis that can serve as a basis for future strategic decisions.

The article presents an in-depth analysis of the data related to the assessment of the impact of climate change on the road infrastructure in Bulgaria. It is necessary to systematically collect, store, and analyze territorial data on the dynamics of changes in various climatic factors such as temperature, snow cover, precipitation, winds, etc. The article examines the main sources of such information in Bulgaria, their limitations, and shortcomings. Identifying the needs and weaknesses in finding usable data is an important step on the way to building a strategy to solve this task.

Climate changes are having an increasingly strong impact on the sustainability and safety of the road infrastructure in Bulgaria. This paper analyzes how changes in climatic conditions over the last three decades affect the condition and functioning of the road network. The aim is to identify critical factors and propose methodological guidelines for design and maintenance that include adaptive strategies to address climate risks. The work emphasizes the need for a complex multidisciplinary approach combining scientific research, strategic planning, and practical implementation.

The National Climate Change Adaptation Strategy for the “Transport” sector until 2030 was developed in the period 2017 – 2019. After 4 years in the conditions of several overlapping crises, the recommendations contained in the document on main adaptation measures relating to institutional capacity building have not started. This is observed against the background of activity from a number of countries that are looking for models to assess the sensitivity of the transport infrastructure to climate events and, from there, measures to achieve sustainability and adaptability in the medium term. The article analyzes the problems of backwardness in Bulgaria and offers recommendations for increasing the participation of all interested participants in the transport sector.

The National Climate Change Adaptation Strategy (NCCAS) for the “Transport” sector until 2030 was developed in the period 2017 – 2019. After 4 years in the context of several overlapping crises, the recommendations contained in the main adaptation measures document have largely not been implemented even in the initial phase. Those recommending the review and updating of legislation regarding adaptation to climate change were not subject to optimization. The article analyzes the problem of lagging behind in Bulgaria in relation to supplementing the standards and presents ideas for methodological approaches through which adaptation measures can be implemented in the design and maintenance of the road infrastructure.

We are witnessing the massive and impressive penetration of artificial intelligence (AI) into many areas of human activity. This process is expected to intensify in the next few decades. In most technical fields, there will be a preponderance of the so-called narrow artificial intelligence with clearly defined tasks and functions. It is usually a coherent set of neural networks trained to solve specific problems. The advantage of narrow AI is that it is entirely controllable and, at the same time, has excellent capabilities. This publication aims to outline guidelines for applying narrow artificial intelligence in investigating the impact of climate change on transport infrastructure. After a brief introduction to narrow artificial intelligence and climate change, various possible areas suitable for AI modeling are explored. Directions and preparatory tasks for collecting climate-sensitive local data on the condition and changes in the transport infrastructure in Bulgaria necessary for AI training are identified.