A Multidimensional Evaluation of Digital-Intelligence Construction Management Needs in Railway Four-Electrical Systems Using the Kano-Entropy Weight TOPSIS Integrated Method

The railway four-electrical system, a core component of railway construction, includes power supply, electrification, communication, and signaling subsystems, providing energy and ensuring safe and efficient operations for railway transportation. Identifying core requirements for digital-intelligent construction management is crucial for integrating digital technologies with the railway four-electrical system, building the foundation for an engineering-focused digital intelligent management platform. This study utilizing the Carnot model and the entropy weight Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) reveals the categories and intrinsic priorities of demand in the intelligent construction and management of the four- electrical system. The results demonstrate that the requirements are categorized into seven must-be requirements, such as 3D visualization of project information and intelligent progress collection; five one-dimensional requirements, including engineering progress visualization and Intelligent query for quality Issues; eleven attractive requirements, like progress deviation comparison and digital cockpit; and four indifferent requirements. Must-be requirements are the core foundation of management, while one-dimensional requirements can enhance management efficiency. High-priority attractive requirements, such as engineering interface issue management (Ci = 0.873) and schedule deviation analysis (Ci = 0.834), address critical challenges in railway four-electrical system construction management, including multi-professional interface conflicts and dynamic schedule control. The findings suggest that given the time and budget constraints in railway construction, digital-intelligence development should prioritize fulfilling must-be requirements, enhancing One-dimensional requirements, and focusing on high-priority attractive requirements that address technological challenges. This study provides a decision-making framework for precise requirement identification, resource allocation optimization, and multi-disciplinary coordination enhancement, thereby maximizing the efficiency of digital-intelligence construction.