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相关概念视频

Workability of Concrete01:25

Workability of Concrete

157
The workability of concrete is a crucial property that affects its handling, placing, and finishing during construction. It describes the ease with which concrete can be mixed, placed, compacted, and finished. Workability is primarily concerned with the concrete's movement and its ability to resist internal friction and external resistance from molds and reinforcements during the application process.
Concrete's workability is determined by its resistance to internal forces that arise...
157
Pozzolans01:21

Pozzolans

190
Pozzolans are siliceous or aluminous materials blended with Portland cement. They interact with the calcium hydroxide produced during the hydration of Portland cement and contribute to improved strength and durability of concrete. The pozzolanic activity, a measure of a pozzolan's effectiveness, is typically assessed using the strength activity index, as defined in ASTM C 618-93, which calculates the ratio of the compressive strength of cement mixtures with and without pozzolan.
Fly ash is...
190
Design Example: Managing Concrete Workability01:14

Design Example: Managing Concrete Workability

121
This example deals with managing the workability of concrete for a raft foundation project under hot weather conditions. Workability is crucial for ensuring the concrete is easy to place, compact, and finish. In this scenario, a slump test — a common method to measure the workability of fresh concrete — initially indicated low workability. This was attributed to the rapid water loss from the concrete mix, exacerbated by the high temperatures causing the course aggregates to heat up.
121
Effects of Air-entrainment in Concrete01:28

Effects of Air-entrainment in Concrete

140
Air entrainment in concrete significantly enhances the material's durability, especially in environments subjected to freeze-thaw cycles. Introducing small air bubbles into the concrete mix acts as internal voids that accommodate the expansion of water when it freezes, thereby alleviating internal stress and preventing structural cracks. This function is crucial in climates with significant freezing and thawing, as it protects the concrete from repeated stresses that could lead to premature...
140
Fatigue Strength of Concrete01:22

Fatigue Strength of Concrete

285
Fatigue, in the context of materials science and engineering, refers to the weakening or failure of a material caused by repeatedly applied loads, even if these loads are below the strength limit of the material. Fatigue strength in concrete is a critical property that influences its durability and longevity. Concrete can fail in two ways due to fatigue. Static fatigue or creep rupture occurs under a constant load or one that increases slowly. The other failure mode is due to cyclical or...
285
Design Example: Sustainability in Concrete Building01:26

Design Example: Sustainability in Concrete Building

226
As the construction industry moves towards more eco-friendly practices, concrete's adaptability and its ability to incorporate sustainable features make it a key material in the drive towards greener building solutions.
There are multiple approaches to achieve sustainability in a commercial concrete building. For instance, construct a concrete parking area under the building, utilizing pervious concrete paver blocks in open areas to facilitate rainwater collection through an underground...
226

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相关实验视频

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使用可解释机器学习技术的集成多任务深度学习框架优化和预测基于飞灰的可持续混凝土的性能

Bhupesh P Nandurkar1, Jayant M Raut1, Pawan K Hinge1

  • 1Department of Civil Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, 441110, Maharashtra, India.

Scientific reports
|August 21, 2025
PubMed
概括

这项研究引入了一种混合人工智能模型,用于准确预测混凝土的强度,其中包括飞灰. 这种可解释的模型通过提供明确的强度因素来提高建筑安全性和材料设计.

关键词:
自动ML优化混凝土的压力强度深度神经网络飞灰的反应性梯度提升多任务学习框架非破坏性测试SHAP和LIME的解释性

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科学领域:

  • 材料科学与工程
  • 土木工程
  • 建筑中的人工智能

背景情况:

  • 准确的混凝土强度预测对于建筑安全和质量保证至关重要.
  • 现有的方法往往在准确性和可解释性之间做出妥协,特别是使用飞等补充材料.
  • 需要能够处理复杂组合设计和环境因素的可解释模型.

研究的目的:

  • 开发一个高度准确和可解释的混合模型来预测混凝土的压力和拉力强度.
  • 在多任务学习 (MTL) 框架中整合混合设计变量,环境因素和非破坏性测试 (NDT) 数据.
  • 利用先进的机器学习技术提高预测准确性和模型可解释性.

主要方法:

  • 结合梯度增强 (XGBoost) 和深度神经网络 (DNN) 的混合方法被采用.
  • 在多任务学习 (MTL) 框架内使用AutoGluon进行自动化模型优化.
  • 使用SHAP (夏普利添加式解释) 和LIME (局部可解释的模型不可知解释) 来实现解释性.

主要成果:

  • 该模型在测试中获得了0.91的令人印象深刻的R2分数.
  • 平均平方误差 (MSE) 降低了23%,超过现有模型.
  • 功能分析表明,飞灰的百分比对预测有很大影响,约占25%.

结论:

  • 拟议的混合模型为可解释的混凝土强度预测提供了一个强大的平台.
  • 这些发现显示了混合建模,自动化优化和具体应用的可解释性方面的重大进步.
  • 这项工作为优化材料设计和确保建筑结构完整性提供了很大的希望.