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相关概念视频

Soundness of Cement01:17

Soundness of Cement

234
The soundness of cement refers to the ability of cement paste to retain its volume after setting. Unsound cement can lead to expansion and structural damage due to the presence of free lime, magnesia, and calcium sulfate. Free lime hydrates very slowly, expanding and causing unsoundness, which is difficult to detect because it intercrystallizes with other compounds. Magnesia also reacts with water, forming crystals that can disrupt the cement's structure. Calcium sulfate can create...
234
Typical Model Studies01:30

Typical Model Studies

441
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
441
Hydration of Cement01:24

Hydration of Cement

382
Hydration of cement is a chemical reaction between cement particles and water. This process occurs primarily through two mechanisms: through-solution and topochemical. In the through-solution process, anhydrous compounds dissolve into their constituents, hydrates form in the solution, and then precipitate from the supersaturated solution. The topochemical process involves solid-state reactions at the cement particle surface. The through-solution process dominates the topochemical process at the...
382
Aggregate Cement Ratio01:21

Aggregate Cement Ratio

331
The Aggregate Cement ratio refers to the weight of aggregate divided by the weight of cement in a concrete mix. Altering this ratio has profound effects on the concrete's properties. This ratio plays a pivotal role in determining the strength, workability, and durability of concrete. When the Aggregate Cement ratio is higher, the mix is leaner, meaning it has less cement paste to lubricate the aggregate, potentially making the concrete less workable. Such mixes, known as lean, enhance the...
331
Types of Cement II01:22

Types of Cement II

167
Portland blast-furnace cement is made by blending Portland cement clinker with granulated blast-furnace slag, which accounts for 25 to 65 percent of the cement's weight. Despite its similarities to ordinary Portland (Type I) cement in terms of fineness and setting times, its early strength is lower, though it achieves comparable strength later on. It's particularly suited for mass concrete structures and marine environments due to its lower heat of hydration and superior sulfate...
167
Fineness of Cement01:15

Fineness of Cement

219
The fineness of cement directly influences the rate of hydration, as the hydration begins at the surface of the cement particles. In addition to hydration, the fineness of cement is vital for various properties of concrete including workability, gypsum requirement, and long-term behavior. The fineness of cement is represented in terms of the specific surface of cement which is typically measured in square meters per kilogram, with several methods available for this determination.
Direct...
219

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相关实验视频

Updated: Sep 11, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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通过基于大型语言模型的合成数据生成,加强在临床辅助水泥系统中对风湿性行为的分析.

Murat Eser1, Yahya Kaya2, Ali Mardani2

  • 1Department of Computer Engineering, Bursa Uludag University, Bursa 16059, Turkey.

Materials (Basel, Switzerland)
|August 14, 2025
PubMed
概括
此摘要是机器生成的。

这项研究表明,大型语言模型 (LLM) 可以改善水泥和聚碳酸乙烯 (PCE) 混合物的兼容性预测. 通过LLM增强的模型准确地预测了水泥的形状,超过了传统方法.

关键词:
人工智能的人工智能是人工智能.混凝土添加剂的兼容性 混凝土添加剂的兼容性研磨辅助器 研磨辅助器 研磨辅助器 研磨辅助器大型语言模型.具有风湿学性质的物质.监督学习学习监督学习

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科学领域:

  • 材料科学 材料科学 材料科学
  • 化学工程是化学工程的重要组成部分.
  • 数据科学数据科学数据科学

背景情况:

  • 了解水泥和聚碳酸 (PCE) 添加剂的兼容性对于混凝土的性能至关重要.
  • 研磨辅助剂 (GA) 影响水泥性能,它们与PCE添加剂的相互作用需要详细的研究.

研究的目的:

  • 通过使用各种研磨辅助剂 (GA) 调查影响水泥和PCE添加剂兼容性的参数.
  • 评估大型语言模型 (LLM) 对于合成数据增强对水泥风学预测建模的影响.

主要方法:

  • 准备了29种水泥类型,其中7种含有四剂量的GA,以及87种含有三剂量的PCE的糊混合物.
  • 使用赫歇尔-布克利模型评估了质行为 (动态收益应力和粘度).
  • 使用卷积神经网络 (CNN),随机森林 (RF) 和神经分类和回归树 (NCART) 建模的数据,增强了LLM生成的合成数据.

主要成果:

  • 在粘度和动态收益应力 (DYS) 方面,NCART显示出最好的基线性能.
  • 基于LLM的合成数据增强显著提高了所有测试模型 (CNN,RF,NCART) 的预测准确度.
  • 用GA生产的水泥比对照组显示出更高的DYS和粘度,这是由于颗粒尺寸分布更细.

结论:

  • 基于LLM的合成数据增强显示了在水泥添加剂兼容性研究中增强预测模型的巨大潜力.
  • 这些发现强调了GA在改变水泥性能方面的重要性,以及它们与PCE添加剂的相互作用.
  • 这项研究开创了LLM用于材料科学建模中的合成数据增强的先驱性.