This paper explores the potential of network digital twins (NDTs) in networking (both IP Ethernet networks and optical transport networks), highlighting their integration with cloud-native software-defined networking (SDN) controllers and intent-based networking enabled by generative artificial intelligence (GenAI). The proposed framework represents an approach that combines advanced virtualization, real-time analytics, and GenAI. The use of NDTs enables a comprehensive and dynamic digital representation of the physical network, capturing critical aspects, such as topology, traffic patterns, and performance metrics, which permits data-driven decision-making to lead to more efficient networking operations. The incorporation of cloud-native SDN controllers along with an NDT ensures that the system remains scalable, flexible, and responsive to dynamic network conditions. Intent-based networking, powered by GenAI, allows the network to interpret high-level objectives from operators and autonomously translate them into actionable configurations that are enforced by orchestrators and SDN controllers. This eliminates manual intervention, minimizes errors, accelerates the deployment of network services, and provides a means for easier network management. The presented framework significantly enhances automation, enabling predictive maintenance by identifying potential issues before they impact network performance. It optimizes network design by simulating various configurations and testing their feasibility in a risk-free environment. These capabilities collectively improve operational efficiency, reduce downtime, and ensure optimal resource utilization.

Context: Despite the effort put into developing standards for structuring construction cost, and the strong interest into the field. Most construction companies still perform the process of data gathering and processing manually. That provokes inconsistencies, different criteria when classifying, misclassifications, and the process becomes very time-consuming, particularly on big projects. Additionally, the lack of standardization makes very difficult the cost estimation and comparison tasks. Objective: To create a method to extract and organize construction cost and quantity data into a consistent format and structure, to enable rapid and reliable digital comparison of the content. Method: The approach consists of a two-step method: Firstly, the system implements data mining to review the input document and determine how it is structured based on the position, format, sequence, and content of descriptive and quantitative data. Secondly, the extracted data is processed and classified with a combination of data science and experts’ knowledge to fit a common format. Results: A big variety of information coming from real historical projects has been successfully extracted and processed into a common format with 97.5% of accuracy, using a subset of 5770 assets located on 18 different files, building a solid base for analysis and comparison. Conclusion: A robust and accurate method was developed for extracting hierarchical project cost data to a common machine-readable format to enable rapid and reliable comparison and benchmarking.

The capability of extracting information and analyze it into a common format is essential for performing predictions, comparing projects through cost benchmarking, and for having a deeper understanding of the project costs. However, the lack of standardization and the manual inclusion of the data makes this process very time-consuming, unreliable, and inefficient. To tackle this problem, a novel approach with a big impact is presented combining the benefits of data mining, statistics, and machine learning to extract and analyze the information related to railway costs infrastructure data. To validate the suggested approach, data from 23 real historical projects from the client network rail was extracted, allowing their costs to be comparable. Finally, some machine learning and data analytics methods were implemented to identify the most relevant factors allowing for costs benchmarking. The presented method proves the benefits of data extraction being able to gather, analyze and benchmark each project in an efficient manner, and deeply understand the relationships and the relevant factors that matter in infrastructure costs.