Partial Inverse Design of High-Performance Concrete Using Cooperative Neural Networks for Constraint-Aware Mix Generation
High-performance concrete (HPC) 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 HPC. 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 strength consistency between the surrogate-predicted strength of the generated mixes and the target strength with R-squared values of 0.84 to 0.89 and substantially reduces the mean squared error of this strength consistency by approximately 42% and 60%, respectively. The results demonstrate a novel, accurate, and computationally efficient application of artificial intelligence in concrete science by applying a cooperative neural network for constraint-aware partial inverse design of HPC mix generation.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian InferenceSimilar Papers 제목 키워드 기반
Contrastive Diffusion Guidance for Spatial Inverse Problems
We consider a class of inverse problems characterized by forward operators that are partially specified, non-smooth, and non-differentiable. Although generative inverse solvers have made significant progress, we find tha…
Toward using explainable data-driven surrogate models for treating performance-based seismic design as an inverse engineering problem
This study presents a methodology to treat performance-based seismic design as an inverse engineering problem, where design parameters are directly derived to achieve specific performance objectives. By implementing expl…
AutoTandemML: Active Learning Enhanced Tandem Neural Networks for Inverse Design Problems
Inverse design in science and engineering involves determining optimal design parameters that achieve desired performance outcomes, a process often hindered by the complexity and high dimensionality of design spaces, lea…
Active LearningNeural Operator-enabled Topology-informed Evolutionary Strategy for PDE-Constrained Optimization
The inverse design of physical systems governed by partial differential equations is computationally demanding due to the high dimensionality and non-convexity of design spaces. Generative models for inverse design often…
Dimensionality ReductionRepresentation LearningRobustness of Constraint Automata for Description Logics with Concrete Domains
Decidability or complexity issues about the consistency problem for description logics with concrete domains have already been analysed with tableaux-based or type elimination methods. Concrete domains in ontologies are …