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Novel Approach for Concrete Mixture Design Using Neural Dynamics Model and Virtual Lab Concept

机译:基于神经动力学模型和虚拟实验室概念的混凝土混合料设计新方法

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摘要

To solve the concrete mixture design problem, engineers have traditionally relied on guidelines such as those from ACI, and a conservative, labor-intensive, time-consuming, and costly trial-and-error approach that neglects cost or environmental impact of the mixture in the design procedure. In this paper, the concrete mixture design problem is solved through adroit integration of a nonlinear optimization algorithm (OA) and a computational intelligence-based classification algorithm (CA) used as a virtual lab to predict whether desired constraints are satisfied in each iteration or not. The model is tested using previously collected data, three OAs, and three CAs. The outcome of this research is an entirely new paradigm and methodology for concrete mixture design for the twenty-first century. The most cost-effective solutions are achieved by the combination of neural dynamics model of Adeli and Park and enhanced probabilistic neural networks. The cost savings for large-scale concrete projects can be in the millions of dollars.
机译:为了解决混凝土混合物的设计问题,工程师传统上一直使用ACI等准则,而保守,费力,费时且昂贵的反复试验方法忽略了混合物的成本或环境影响。设计程序。在本文中,通过将非线性优化算法(OA)和基于计算智能的分类算法(CA)用作虚拟实验室来预测每次迭代是否满足所需约束的技巧的集成,解决了混凝土混合物的设计问题。 。使用先前收集的数据,三个OA和三个CA对模型进行测试。这项研究的结果是二十世纪混凝土混合物设计的全新范式和方法论。通过将Adeli和Park的神经动力学模型与增强的概率神经网络相结合,可以获得最具成本效益的解决方案。大型混凝土项目的成本节省可达到数百万美元。

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