Integrating Large Language Models and Agent-Based Simulation for Construction Safety Behavior Management: Strategy Focusing and Explainable Report Generation

Unsafe behaviors by construction personnel are the primary cause of construction accidents. How to rationally allocate management resources within limited constraints is a core challenge in construction safety management. This study develops and validates a workflow framework integrating Large Language Models (LLMs) and Agent-Based Modeling (ABM), termed LLM-ABM, aimed at achieving intelligent focusing of management strategies and automated interpretation of simulation results. First, an ABM model was constructed in the context of petroleum engineering to analyze the association between different management resource allocations and the evolutionary outcomes of unsafe behaviors under this specific engineering scenario. Second, a complete response surface comprising 231 combinations with a step size of 0.05 was constructed as an evaluation benchmark. On this basis, a manually designed search framework-guided two-round iterative search flow of the LLM-ABM was developed, and ten rounds of independent comparative experiments were conducted against random search to evaluate its strategy-focusing capability. Subsequently, the simulation results were fed into the LLM, and three experts were invited to evaluate the quality of the generated reports from the perspectives of data fidelity, logical coherence, and recommendation actionability. The comparison of the four typical strategies showed that, under the current model settings, active management was associated with a lower mean unsafe personnel ratio (J = 8.05%) and a lower peak unsafe personnel ratio (Pmax = 10.43%), while the balanced strategy was associated with maintaining late-stage average safe personnel ratio (Slate = 60.63%). In terms of strategy focusing, under the current discrete grid with a step size of 0.05 and fixed ABM model settings, the Top-10% hit rate of LLM-ABM reached 20.00%, numerically higher than the 9.50% of random search. Moreover, the final selected strategies exhibit better global rankings on the discrete grid with lower variance. Although this difference did not reach statistical significance due to the limited number of trials, the large effect size preliminarily suggests that LLM-ABM has the potential to focus search resources on high-quality strategy regions. Regarding report generation, expert evaluations yielded scores of 0.95 for data fidelity, 0.87 for logical coherence, and 0.85 for recommendation actionability. This study preliminarily demonstrates that the LLM-ABM workflow possesses the potential to focus search on high-quality strategy regions under a limited budget, as well as the feasibility of transforming structured simulation data into traceable management recommendations, providing a complete technical pathway of “generation–evaluation–feedback–interpretation” for construction safety management in petroleum engineering.