A Computational Framework For Infrastructure Systems Under Multiple Factors In Deep Learning

The proposed framework leverages deep learning algorithms to incorporate multiple factors and their interconnections, enabling a comprehensive understanding of infrastructure performance. This paper outlines the framework's methodology, describes the deep learning techniques employed, presents a case study to demonstrate its effectiveness, and discusses potential applications in infrastructure planning and decision-making. Infrastructure systems, such as transportation networks, power grids, and water supply systems, are essential for modern societies. The presents a computational framework that leverages deep learning techniques to model and analyse infrastructure systems under the influence of multiple factors, enabling enhanced decision-making and system performance. The proposed framework integrates deep learning algorithms with comprehensive data sets collected from various sources, including sensors, social media, and historical records, to capture the intricate relationships and dependencies among system components and influencing factors. Through a combination of feature extraction, pattern recognition, and predictive modelling, the framework learns the underlying dynamics of the infrastructure system, enabling accurate predictions and decision support. The trained deep learning models are capable of simulating and predicting the behaviour and performance of the infrastructure system under different scenarios and conditions. This enables the identification of potential vulnerabilities, the optimization of resource allocation, and the development of proactive strategies to enhance system resilience, reliability, and efficiency. Moreover, the framework can provide real-time monitoring and decision support by analysing streaming data and detecting anomalies or critical events.