A Rule-Guided Conditional Diffusion Model for Engineering-Constrained Cost Scenario Generation and Risk Assessment in Textile Geomembrane Projects
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Abstract
While diffusion models excel in generating high-dimensional data, their purely data-driven nature makes it difficult to integrate engineering constraints and physical laws, resulting in generated cost scenarios for textile geomembranes lacking construction feasibility and failing to support reliable risk assessment. Existing diffusion models generate cost scenarios that often violate engineering constraints. This paper solves this problem by integrating explicit domain knowledge into a conditional diffusion model. The proposed model generates cost scenarios that are both statistically faithful and engineeringly feasible, enabling reliable multi-dimensional risk assessment. Methodologically, a 12- dimensional conditional feature model is constructed based on 32 historical projects and national standards. Thirteen engineering constraints are formally extracted and integrated into the Drools rule engine. A constraint-aware Classifier- Free Guidance diffusion architecture is designed, dynamically verifying and correcting feasibility during training loss and sampling. Experiments show that the proposed method achieves an engineering feasibility rate exceeding 93% and an average KL (Kullback-Leibler) divergence of 0.108 and notably, VaR@95% estimation bias below 4.2% across diverse scenarios, with CVaR estimation error within ±5.1%. Extreme high-cost events are captured with over 89% coverage, demonstrating that the generated scenarios enable reliable multi-dimensional risk quantification. This divergence is measured between the distribution of total costs from the generated scenarios and the empirical distribution of total costs from real historical projects within the held-out test set. This research provides a paradigm for intelligent generation and risk assessment of engineering costs that balances data fidelity and physical feasibility, demonstrating promising engineering applications.
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