You only design once (YODO): Gaussian Process-Batch Bayesian optimization framework for mixture design of ultra high performance concrete. (2nd May 2022) - Saleh, Eman
Graphical abstract: Highlights: A mixture design framework for ultra-high-performance concrete (UHPC) is proposed. The framework is based on a Gaussian Process-Batch Bayesian optimization algorithm. This framework offers a list of possible UHPC formulae of a stargeted performance. An open-source software with a Graphical User Interface was developed. Abstract: Ultra-high-performance concrete (UHPC) has superior strength and durability, and hence it has been primarily favored in a variety of applications in structural engineering. While the open literature presents a series of predictive machine learning (ML) models to predict the strength of UHPC from its constituent materials, the reverse problem of identifying possible concrete mixtures with a targeted (pre-tailored) performance persists to exist. Unlike other works, and in an effort to bridge this knowledge gap, this study proposes a Gaussian process (GP) modeling with batch Bayesian optimization (BBO) framework (GP-BBO) to infer the mixture design of UHPC. In this framework, the GP is used as a predictive surrogate model constructed from experimental measurements. After the GP is trained and validated, BBO is used to infer the plausible formulae for the targeted strength by optimizing an acquisition function that trades off exploitation and exploration based on the optimality and variability of the surrogate model. As such, the proposed framework offers a list of possible UHPC formulae of a targeted strength. To facilitate a wide spread of the proposed framework, The ML code is shared for interested researchers to verify and expand upon. In addition, and to negate arising hurdles associated with GP-BBO programming, also an open-source and coding-free software (App) is created that can be directly deployed by UHPC fabricators. In contrast to the conventional trial-and-error-based mixture design, GP-BBO provides a self-adaptive paradigm for efficient sampling of design space for identifying the optimum sampling points. This framework can be extended to infer formulae that satisfy multiple performance objectives such as strength, workability, and durability.