Integration of deep learning, big data and biomechanics to optimize the layout of new railroad energy bioeffects

This study is centered around optimizing the layout of new railroad energy systems, drawing inspiration from biomechanics and integrating deep-learning and big-data technologies. The overarching aim is to boost energy utilization efficiency and simultaneously minimize the ecological disruptions brought about by energy infrastructure, which contributes to the “dual carbon” goals (carbon peaking and carbon neutrality) by enhancing energy efficiency and reducing environmental impact. This approach not only promotes green transportation but also aligns with sustainable development objectives. Inspired by the complex and well-coordinated mechanisms in biomechanics, a comprehensive biological-effect-inspired evaluation index system is devised. This system takes into account the diverse impacts of energy systems on the surrounding environment, similar to how living organisms interact with their habitats. Just as a living body’s various parts work in harmony, this index system captures the multi-faceted relationships between the energy system and the environment. A hybrid neural network model, designed with inspiration from the neural-like processing in biological systems, combines advanced convolutional and long short-term memory networks. This combination is aimed at effectively extracting both spatial and temporal features, much like how biological neural systems process different types of information related to space and time. For instance, in the human body, the nervous system can quickly respond to changes in the surrounding space and also remember past experiences over time. Additionally, multi-task learning techniques are employed to enable simultaneous analysis of multiple environmental indicators, such as noise, temperature, and magnetic field strength. Experimental results reveal that the proposed biomechanics-inspired approach far surpasses traditional heuristic algorithms. It showcases remarkable prediction accuracy and computational efficiency. By harnessing the power of advanced machine-learning frameworks inspired by biological systems, this method offers precise evaluations and practical insights for optimizing energy layouts. This research not only facilitates the scientific planning of railroad energy systems but also aids in reducing their ecological footprint, in line with the principles of sustainable development. The findings establish a solid foundation for achieving a balance between energy requirements and environmental conservation. They underscore the transformative potential of intelligent technologies, inspired by the wonders of biomechanics, in modern infrastructure planning.