用协作神经网络实现高性能混凝土的部分逆向配比设计。
Partial Inverse Design of High-Performance Concrete Using Cooperative Neural Networks for Constraint-Aware Mix Generation
- 采用协同训练的神经网络框架，结合补全与强度预测模型。
- 在不同约束条件下，生成性能一致的配比方案，误差降低50%~70%。
- 适合需要快速生成合规配比的工程优化场景。
高性能混凝土的配合比设计涉及相互依赖的变量和实际约束，复杂度高。尽管数据驱动方法已提升正向设计的预测能力，但逆向设计仍受限，尤其当部分变量固定时，需推断其余变量。本文提出一种协作神经网络框架，用于高性能混凝土的部分逆向设计。该框架融合插补模型与代理强度预测器，通过协同训练学习。训练完成后，可单次前向传播生成符合约束且性能一致的配合比，无需针对不同约束重新训练。相比基线模型（包括自编码器及基于高斯过程代理的贝叶斯推断），该方法在不同任务中取得0.87至0.92的决定系数，均方误差分别降低约50%和70%。结果表明，该框架为约束感知、数据驱动的配合比设计提供了准确且计算高效的解决方案。
原文摘要 · Abstract (English)
High-performance concrete requires complex mix design decisions involving interdependent variables and practical constraints. While data-driven methods have improved predictive modeling for forward design in concrete engineering, inverse design remains limited, especially when some variables are fixed and only the remaining ones must be inferred. This study proposes a cooperative neural network framework for the partial inverse design of high-performance concrete. The framework integrates an imputation model with a surrogate strength predictor and learns through cooperative training. Once trained, it generates valid and performance-consistent mix designs in a single forward pass without retraining for different constraint scenarios. Compared with baseline models, including autoencoder models and Bayesian inference with Gaussian process surrogates, the proposed method achieves R-squared values of 0.87 to 0.92 and substantially reduces mean squared error by approximately 50% and 70%, respectively. The results show that the framework provides an accurate and computationally efficient foundation for constraint-aware, data-driven mix proportioning.
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