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首页> 外文期刊>Construction and Building Materials >Intelligent mixture design of steel fibre reinforced concrete using a support vector regression and firefly algorithm based multi-objective optimization model
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Intelligent mixture design of steel fibre reinforced concrete using a support vector regression and firefly algorithm based multi-objective optimization model

机译：钢纤维增强混凝土智能混合设计使用支持向量回归和基于萤火虫算法的多目标优化模型

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Steel fibre reinforced concrete (SFRC) is widely used in the construction concrete industry as it partakes an important role of evolving concrete technology. It consists of steel fibres of various shapes, sizes and geometries that influence the concrete mix composition and mechanical properties. However, compared to traditional concrete, it is difficult to design the mix proportions because more influencing variables need to be considered to optimise multiple properties including ultimate compressive strength, tensile or flexural strength and cost. Therefore, the present study proposes an artificial intelligence based multi-objective optimization model to enable an efficient method of finding the optimum mix design for SFRC. A large dataset including 299 instances for uniaxial compressive strength (UCS) test and 269 instances for flexural strength (FS) test were collected from previous literature. Support vector regression (SVR) model was applied to predict UCS and FS for SFRC. The hyper parameters of SVR models were tuned using a firefly algorithm (FA) and a sensitivity study was conducted to understand the importance of the inputs on the output variables for the algorithms. High correlation coefficients (0.91 for UCS and 0.85 for FS) were achieved on the test dataset. The FA-SVR model was then applied as the objective function for a developed multi-objective FA to search for the optimal SFRC mixture proportion. Pareto optimal solutions were obtained and served as a design guide to determine the optimal SFRC mixtures.(C) 2020 Elsevier Ltd. All rights reserved.
机译：钢纤维钢筋混凝土（SFRC）广泛应用于建筑混凝土行业，因为它的参与了不断变化的具体技术的重要作用。它由各种形状，尺寸和几何形状的钢纤维组成，这些形状和几何形状会影响混凝土混合组成和机械性能。然而，与传统混凝土相比，难以设计混合比例，因为需要考虑更多的影响变量来优化多个性能，包括最终抗压强度，拉伸或弯曲强度和成本。因此，本研究提出了一种基于人工智能的多目标优化模型，以实现用于SFRC的最佳混合设计的有效方法。包括299个用于单轴抗压强度（UCS）测试的299个实例和269个用于弯曲强度（FS）试验的269个实例。支持向量回归（SVR）模型应用于预测SFRC的UCS和FS。使用萤火虫算法（FA）进行调谐SVR模型的超参数，并进行灵敏度研究以了解算法输出变量对输出变量的重要性。在测试数据集上实现了高相关系数（UCS的0.91和FS的0.85）。然后将FA-SVR模型应用于开发的多目标FA的目标函数，以寻找最佳的SFRC混合比例。获得帕累托最佳解决方案并用作确定最佳SFRC混合物的设计指南。（c）2020 Elsevier Ltd.保留所有权利。

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