Maximizing Performance of Geopolymer Mortar: Optimizing Basalt and Carbon Fiber Content Composition

ABSTRACT

This study focuses on the optimization of geopolymer composites, considering the parameters of composition and performance. The research explores the integration of algorithm-built hybrid implementations and a hybrid intelligent system to solve complex optimization problems in geopolymer composite materials. Firstly, an algorithm-built hybrid implementation is proposed, combining experimental results with various data processing methods. This approach enables the utilization of composite algorithms, offering several advantages, such as scalability and adaptability to different loads. The models developed in this study provide a flexible and extensible architecture, allowing for efficient problem-solving in optimization tasks. Secondly, a hybrid intelligent system is introduced, comprising statistical simulation models that combine different control and design problem-solving approaches. Markov chains are employed to address the quantitative aspects of loosely structured tasks and process performance evaluation. Criterion methods are utilized for quantitative conclusions, ensuring the optimal adaptation of the results from both applications. The research culminates in the identification of the optimal composition, denoted as G + FC + CFI, with specific weight content. This composition consists of cement, activator, fireclay, and carbon fiber I, with 100 g, 90 g, 100 g, and 2.5 g, respectively. The findings from this study provide valuable insights into the optimization of geopolymer composites, employing algorithm-built hybrid implementations and a hybrid intelligent system. The proposed approaches offer enhanced efficiency and accuracy in solving complex optimization problems in the field of geopolymer composite materials. The identified optimal composition demonstrates the potential for improving performance in composition and weight content.

摘要

本研究的重点是优化地质聚合物复合材料，考虑成分和性能参数. 该研究探索了算法构建的混合实现和混合智能系统的集成，以解决地质聚合物复合材料中的复杂优化问题. 首先，将实验结果与各种数据处理方法相结合，提出了一种基于算法的混合实现方法. 这种方法能够利用复合算法，提供了几个优点，如可扩展性和对不同负载的适应性. 本研究中开发的模型提供了一个灵活和可扩展的体系结构，允许在优化任务中高效地解决问题. 其次，介绍了一种混合智能系统，该系统包括将不同的控制和设计问题解决方法相结合的统计仿真模型. 马尔可夫链用于解决松散结构任务和过程性能评估的定量方面. 标准方法用于定量结论，确保两种应用的结果的最佳适应. 研究最终确定了具有特定重量含量的最佳组成，表示为G+FC+CFI. 该组合物由水泥、活化剂、粘土和碳纤维I组成，分别为100 g、90 g、100 g和2.5 g. 这项研究的发现为利用算法构建的混合实现和混合智能系统优化地质聚合物复合材料提供了有价值的见解. 所提出的方法在解决地质聚合物复合材料领域的复杂优化问题时提供了更高的效率和准确性. 所确定的最佳组成证明了在组成和重量含量方面提高性能的潜力.

KEYWORDS:

• Geopolymer composites
• optimization
• algorithm-built hybrid implementation
• hybrid intelligent system
• composite algorithms
• performance evaluation

关键词:

• 地质聚合物复合材料
• 优化
• 算法构建的混合实现
• 混合智能系统
• 复合算法
• 绩效评估

Highlights

• The study proposes an algorithm-built hybrid implementation to optimize geopolymer composites based on composition and performance parameters.

• The hybrid intelligent system includes statistical simulation models that combine different control and design problem-solving approaches, using Markov chains to address quantitative aspects of loosely structured tasks and process performance evaluation.

• The optimal composition, denoted as G + FC + CFI, is identified with specific weight content consisting of cement, activator, fireclay, and carbon fiber I, with 100g, 90g, 100g, and 2.5g, respectively

• The proposed approaches provide enhanced efficiency and accuracy in solving complex optimization problems in the field of geopolymer composite materials and valuable insights into their optimization.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the University of Kalisz [PIN: 618 188 02 48].