Keywords: structural analysis

Synergistic Integration of Digital Twins and Neural Networks for Advancing Optimization in the Construction Industry: A Comprehensive Review

https://doi.org/10.58224/2618-7183-2024-7-4-7
Abstract
The object of research is the potential application of digital twins and neural network modeling for optimizing construction processes. Method. Adopting a perspective approach, the research conducts an extensive review of existing literature and delineates a theoretical framework for integrating digital twins and neural network modeling technologies. Insights from the literature review inform the development of methodologies, while case studies and practical applications are explored to deepen understanding of these integrated approaches to system construction optimization. Results. The review yields the following key findings: Digital Twins: Offer the capability to create high-fidelity virtual representations of physical construction systems, enabling real-time data collection, analysis, and visualization throughout the project lifecycle. This allows for proactive decision-making, improved constructability analysis, and enhanced coordination between design and field operations. Neural Network Modeling: Possesses the power to learn complex relationships from vast datasets, enabling predictive modeling and optimization of construction system behavior. Neural networks can be employed to forecast project timelines, identify potential risks, and optimize construction scheduling and resource allocation. Integration of Digital Twins and Neural Networks: Presents a transformative avenue for optimizing construction processes by facilitating data-driven design, predictive maintenance of equipment and infrastructure, and real-time performance monitoring. This synergistic approach can lead to significant improvements in construction efficiency, reduced project costs, and enhanced overall project quality.
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Vernacular architecture in the space of a modern city, based on deep learning methods and three-dimensional structural analysis

https://doi.org/10.58224/2618-7183-2023-6-6-9
Abstract
This research paper describes the process and results of a project to automatically classify historical buildings using aerial photography and satellite imagery. New computational scientific methods and the availability of satellite images have created more opportunities to work on automated recognition of pieces of historical architecture. In this regard, the convolutional neural network (CNN) is the main classification approach within the project. As a result, the trained model is tested using a validation data set and has a roughly 98% accuracy. In addition, being affected by urbanization and other factors, local architectural heritage faces the challenge of introducing innovations for sustainable development, with originality and authenticity being preserved in redesign and planning. Thus, this study uses a visualized quantitative analysis to analyze the research trends in Russian vernacular architecture and study new ways of coexistence between vernacular architecture, object perception and cultural ecology. The most important task of this study is to analyze the theory of coordination between the emotion social and cultural structure and the cultural ecosystem in vernacular architecture. The main contribution of this study is the proposed concept of a subjective-cultural eco-design system for vernacular architecture sustainable development to establish a 3D structural analysis design paradigm and evaluation analysis matrix, and to ensure that vernacular architecture demonstrates the ability to self-renew by continuous exchange and revision in the dynamic cycle of the current design system.
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