Chen Feixiang, Xu Wangyang, Wen Qing, Zhang Guozhi, Xu Liuliu, Fan Dingqiang, Yu Rui
CCCC Second Harbor Engineering Company Ltd., Wuhan 430070, China.
Key Laboratory of Large-Span Bridge Construction Technology, Wuhan 430070, China.
Materials (Basel). 2023 Sep 28;16(19):6448. doi: 10.3390/ma16196448.
Concrete mixture design has been a key focus in concrete research. This study presents a new method for concrete mixture design by combining artificial neural networks (ANN), genetic algorithms (GA), and Scipy libraries for hybrid intelligent modeling. This method enables the prediction of concrete mechanical properties and the optimization of mix proportions with single or multi-objective goals. The GA is used to optimize the structure and weight parameters of ANN to improve prediction accuracy and generalization ability (R > 0.95, RMSE and MAE < 10). Then, the Scipy library combined with GA-ANN is used for the multi-objective optimization of concrete mix proportions to balance the compressive strength and costs of concrete. Moreover, an AI-based concrete mix proportion design system is developed, utilizing a user-friendly GUI to meet specific strength requirements and adapt to practical needs. This system enhances optimization design capabilities and sets the stage for future advancements. Overall, this study focuses on optimizing concrete mixture design using hybrid intelligent modeling and multi-objective optimization, which contributes to providing a novel and practical solution for improving the efficiency and accuracy of concrete mixture design in the construction industry.
混凝土配合比设计一直是混凝土研究的重点。本研究提出了一种新的混凝土配合比设计方法，该方法结合了人工神经网络（ANN）、遗传算法（GA）和Scipy库进行混合智能建模。该方法能够预测混凝土力学性能，并以单目标或多目标对配合比进行优化。遗传算法用于优化人工神经网络的结构和权重参数，以提高预测精度和泛化能力（R>0.95，RMSE和MAE<10）。然后，将Scipy库与GA-ANN相结合，用于混凝土配合比的多目标优化，以平衡混凝土的抗压强度和成本。此外，还开发了一个基于人工智能的混凝土配合比设计系统，该系统利用用户友好的图形用户界面来满足特定的强度要求并适应实际需求。该系统增强了优化设计能力，并为未来的发展奠定了基础。总体而言，本研究致力于利用混合智能建模和多目标优化来优化混凝土配合比设计，这有助于为提高建筑行业混凝土配合比设计的效率和准确性提供一种新颖且实用的解决方案。
