Computer Aided Cognitive BIM Framework for Intelligent Decision Support in Civil Engineering Infrastructure Using Hybrid Artificial Intelligence

Abstract

The integration of Building Information Modelling (BIM) with artificial intelligence (AI) represents a transformative paradigm in civil engineering infrastructure management. This paper proposes a novel Computer Aided Cognitive BIM (CAC-BIM) framework that leverages hybrid artificial intelligence techniques to provide intelligent decision support for civil engineering infrastructure projects. The framework integrates deep learning, fuzzy logic, knowledge-based systems, and multi-agent architectures within a cognitive computing environment to enhance decision-making processes across the infrastructure lifecycle. The proposed methodology employs a mixed-methods research design combining computational modelling, case study validation, and expert evaluation. Results demonstrate that the CAC-BIM framework achieves a 34.7% improvement in decision accuracy, 28.3% reduction in project delays, and 22.1% cost optimization compared to conventional BIM-assisted approaches. The framework's hybrid AI architecture demonstrates superior performance in handling uncertainty, multi-criteria optimization, and real-time adaptive reasoning in complex infrastructure scenarios. This research contributes to the advancement of intelligent construction management and provides a scalable computational framework for next-generation civil engineering decision support systems.