Showing 2 results for Multi-Criteria Decision-Making
H. Dehghani, M. Amiri Moghadam, S. H. Mahdavi,
Volume 11, Issue 3 (8-2021)
Abstract
Selecting an appropriate flooring system is essential for structures. Flooring system design has traditionally focused on weight loss and minimizing costs. However, in recent years, the focus of this sector has changed to include improving the environmental performance of building materials and construction systems. This paper illustrates a knowledge-based expert system as a tool to assess of flooring systems such as block joisted (BJ), steel-concrete composite (SCC), composite steel deck (CSD) and concrete slab (CS) based on sustainability criteria that are further divided into twenty sub-criteria. Analytical hierarchy process (AHP) is utilized as a multi-criteria decision making technique that helps to compute weights and rankings of sustainability criteria. For this purpose, some questionnaires completed by construction industry experts in order to compare criterions and sub-criteria in addition to assessment of optimized flooring systems. Then, results of the questionnaires are ranked by AHP and the most significant alternative is selected. The AHP results indicate that CSD system 47.9%, CS; 29.8%, SCC; 12.7% and BJ system 9.6% are the most and the least efficient systems, respectively.
M. Rastegar Moghaddam,
Volume 16, Issue 2 (4-2026)
Abstract
Decision-making in the selection of sustainable building components remains one of the most persistent challenges in the construction industry. Projects involve numerous conflicting objectives and highly interdependent variables, yet the rich semantic and relational data embedded in IFC-based BIM models is rarely fully exploited for advanced analytical support. Existing approaches typically suffer from fragmented workflows, inefficient data extraction, and poor integration between modelling, optimisation, and decision-making processes. This study proposes a comprehensive, integrated data-driven decision-support framework that directly addresses these limitations. The framework transforms IFC-based BIM data into a scalable graph database using Neo4j and connects it seamlessly with multi-objective optimisation, Data Envelopment Analysis (DEA), and multi-criteria decision-making (MCDM) within a single coherent pipeline. The framework was implemented and validated on a residential building case study, considering four key sustainability objectives. Results demonstrate that the graph-based representation improves data accessibility and efficient retrieval, while the integrated pipeline effectively reduces the solution space and delivers transparent, high-quality recommendations that balance technical performance with stakeholder preferences. Compared with conventional fragmented methods, the proposed framework offers a more coherent, practical, and potentially scalable solution for complex multi-criteria decision-making problems across the Architecture, Engineering, and Construction (AEC) industry.
