碳纤维增强聚合物作为约束增强以最大化工程竹的抗压强度:一个人工神经网络模型

W. E. Silva, D. Silva
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引用次数: 0

摘要

基础设施的类型和材料的选择是必须考虑的主要因素之一。由于它通常在项目中大量量化,它直接影响到它所属的环境和社区。共同决定着我们世界的未来。作为一种坚固、多功能、耐用、可持续和环保的材料,竹子及其衍生物自古以来就被广泛使用;菲律宾很幸运,全国各地都有丰富的淡水资源。作为当地研发重点和市场突出的竹子品种之一,青竹的机械性能非常出色,是许多结构元件的优良材料。但要充分利用它,可能需要增强材料,就像任何其他木质和有机材料一样。通过使用有前途的、适应性强的碳纤维增强聚合物(CFRP)对其进行围护加固,其抗压强度可以从50.83 MPa的平均强度沿晶粒方向扩展。采用Levenberg-Marquardt (LM)算法作为训练算法(TA),双曲正切s型曲线作为传递函数(TF),建立了CFRP约束钢筋厚度、构成压缩面积的边、含水率、温度和密度的人工神经网络(ANN)模型。除密度外,各变量与复合材料截面极限抗压强度的关系均为间接正比关系,并利用Garson算法(GA)进一步验证了其影响。此外,通过物理实验和有限元模拟对结果进行了验证,并将人工神经网络模型与其他预测建模技术进行了比较,结果表明,人工神经网络模型是物理测试的有效补充,人工神经网络预测模型表现最好。研究结果也证实了其他关于工程竹子研究的文献;CFRP-LVB复合截面的破坏是LVB核心随着截面变大而局部孤立破坏的组合,而在较小截面上观察到完全破碎。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Carbon-Fiber-reinforced Polymer as Confinement Reinforcement to Maximize Compressive Strength of Engineered Bamboo: An Artificial Neural Network Model
The type of infrastructure and selection of its materials is one of the principal factors that must be considered. Due to its usual large quantifications on projects, it directly affects the environment and communities where it belonged. And collectively, the future of our world. As a strong, versatile, durable, sustainable, and environmentally beneficial material, bamboo and its derivatives are frequently utilized since the early times; the Philippines is fortunate to have an abundance of it across the country. The mechanical properties of one of the local R&D-prioritized and market-prominent bamboo specie, the Bambusa blumeana, are remarkable and well-known to be an excellent material for many structural elements. But to fully utilize it, reinforcements may be required, just like with any other ligneous and organic materials. Extensions in its compression strength along the grain may be accomplished from its 50.83 MPa average strength by confinement-reinforcing it with the promising, adaptable, and strong Carbon-fiber-reinforced polymer (CFRP). The Artificial Neural Network (ANN) model involving CFRP's confinement reinforcement thickness, edges that constitutes the compression area, moisture content, temperature, and density of Laminated Veneer Bamboo (LVB) was established using the Levenberg-Marquardt (LM) algorithm as the training algorithm (TA) and hyperbolic tangent sigmoid as the transfer function (TF). The relationship of the variables to the composite section's ultimate compressive strength, was indirectly proportional, except for density, and was further checked the influence using Garson's algorithm (GA). In addition, the results were verified using additional physical experimentation and Finite Element (FE) simulations, while the ANN model was compared to other prediction modelling techniques, by which the FE simulation proved to be an effective complement to the physical testing and the ANN prediction model performed the best. The results also reconfirmed other literature on engineered bamboo studies; and the failure of the CFRP-LVB composite section was found to be a combination of isolated partial failures of the LVB core as the cross-sections become larger, while full crushing was observed on smaller cross-sections.
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