Abstract

Building Information Modelling (BIM) has digitized construction, yet automated cost estimation still suffers from fragmented data and deterministic forecasts that ignore uncertainty. To address this gap, this study introduces a novel framework integrating agentic artificial intelligence (AI) with large language models (LLMs) to enable probabilistic cost estimation from disparate BIM data. The system employs four specialized collaborative agents operating via a shared memory module centered on an LLM with natural language understanding, code generation, and chain-of-thought reasoning. A prototype using GPT-4 Turbo, AutoGen, and Monte Carlo simulation was tested on three real-world structures. Compared to three baselines, the framework reduced processing time (4.2 vs. 18.5–68.0 min), manual interventions (0.8 vs. 9–14), and improved entity resolution accuracy (86.5% vs. 46–62%) with well-calibrated probabilistic forecasts, achieving 86.0% empirical coverage for nominal 90% prediction intervals (Prediction Interval Coverage Probability [PICP] = 86.0%, Prediction Interval Width [PIW] = 0.28; p < 0.01). Qualitative analysis confirmed effective semantic conflict resolution and actionable risk visualization via tornado diagrams. The framework tackles long-standing BIM estimation challenges by delivering probabilistic, transparent outputs. Future work includes digital twin integration, open-source LLM deployment, and during-construction forecasting.

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