Author(s)

Xianglin Du

Corresponding Author

Xianglin Du

Abstract

Complex engineering projects pose significant challenges to the control and evaluation of supervision quality due to their large scale, intricate systems, diverse stakeholders, and high technical complexity. Existing supervision quality evaluation systems exhibit limitations in indicator design, weight allocation, and dynamic feedback mechanisms, making it difficult to comprehensively reflect the actual performance of supervision work in complex projects. Based on this, this paper first analyzes the characteristics of complex engineering projects and their specific demands on supervision quality, identifying key issues in current evaluation systems. Subsequently, a supervision quality evaluation indicator system covering three dimensions—technical, managerial, and comprehensive—is constructed. A combined approach of the Analytic Hierarchy Process (AHP) and Entropy Weighting Method is introduced to determine comprehensive weights, enhancing the scientific rigor and objectivity of evaluation outcomes. Building upon this foundation, a comprehensive evaluation method for supervision quality is proposed, based on fuzzy comprehensive evaluation and an improved TOPSIS model, forming a relatively complete evaluation framework. Finally, the application pathways of information technology and big data in supervision quality evaluation and optimization are explored, and the feasibility and effectiveness of the constructed system are validated through case analysis. The study demonstrates that this system not only enhances the scientific rigor and systematic nature of supervision quality evaluation in complex engineering projects but also provides support for subsequent dynamic optimization and management decision-making.

Keywords

Complex engineering projects, supervision quality, evaluation system, optimization method, information technology application