A novel BIM-AI-based framework towards data-driven value engineering optimization for circular economy in construction

Purpose The study aims to develop an integrated framework to enhance the value engineering (VE) approach in construction by leveraging building information modeling (BIM) and artificial intelligence (AI). The framework focuses on material optimization and sustainable resource management while ensuring quality and cost-effectiveness to attain the circular economy (CE). Design/methodology/approach A systematic literature review, guided by Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) method, is conducted to examine the applications of VE in construction. A mixed-method approach combines quantitative analysis, including keyword co-occurrence and clustering, with qualitative content analysis. The Delft Ladder approach is employed to structure the integration of VE with BIM and AI technologies, forming the foundation of the novel industrial practice-based framework. Findings The study reveals significant potential for enhancing VE through digital transformation. Integrating BIM and AI with VE principles demonstrates improved efficiency in material optimization and reduction of environmental impacts. The proposed Framework promotes closed-loop systems in construction by enabling data-driven decision-making, improving resource efficiency and allowing stakeholders to adopt CE principles throughout the construction lifecycle. Practical implications The framework offers construction professionals pragmatic solutions to mitigate embodied carbon, encourage material reuse and fulfill sustainability objectives. It tackles issues in conventional VE implementation by integrating digital technologies with CE procedures for efficient material management. Originality/value This research introduces an innovative framework that uniquely integrates VE principles with BIM and AI functionalities, employing the reduce, reuse and recycle methodology. The framework can enhance value and minimize expenses through optimization and material efficiency to achieve both functionality and cost-effectiveness.