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相关概念视频

Design Example: Managing Concrete Workability01:14

Design Example: Managing Concrete Workability

84
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.
84
Mixing Concrete01:30

Mixing Concrete

121
Concrete mixing ensures a homogenous blend where aggregates are well-coated with cement paste. Concrete mixing is typically done using two main types of mixers: batch and continuous. Batch mixers handle one batch at a time, thoroughly combining materials before discharging and receiving the next batch. In contrast, continuous mixers receive a steady flow of ingredients, mixing them consistently and discharging without interruption. Within batch mixers, tilting drum mixers mix with internal...
121
Accelerated Curing of Concrete01:25

Accelerated Curing of Concrete

154
Accelerating concrete curing is achieved by applying heat and additional moisture. This process accelerates the hydration of the cement, resulting in an earlier strength gain in the concrete. Steam curing is a method wherein the concrete products are either transported through a chamber on a conveyor belt or encased in plastic, allowing steam at atmospheric pressure to circulate freely around them. This process begins with a phase of moist curing that typically lasts between 3 to 5 hours, after...
154
Workability of Concrete01:25

Workability of Concrete

107
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...
107
Ready Mixed Concrete01:26

Ready Mixed Concrete

101
Ready-mixed concrete, also known as pre-mixed concrete, is prepared in a centralized plant and then transported in trucks to construction sites where it is ready for placement. This type of concrete is categorized into central-mixed, truck-mixed (or transit-mixed), and shrink-mixed. Central-mixed concrete is entirely prepared at a plant and moved to the site in agitator trucks that rotate at a speed of 2 to 6 rpm. Truck-mixed concrete, on the other hand, has the ingredients batched at the plant...
101
Design Example: Aggregate Gradation01:24

Design Example: Aggregate Gradation

99
The right type and quality of aggregates are crucial for concrete as they significantly influence its properties, mix proportions, and cost-effectiveness. If different sources are available for sand, the commonly used fine aggregate in concrete, the selection of sand is primarily based on its gradation.
The grading, or particle-size distribution, of sand is determined using sieve analysis, with standard sizes ranging from 150 μm to 10 mm (ASTM No. 100 sieve to 3⁄8 in. sieve). Sand is...
99

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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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通过混合智能推进混凝土混合比例:一种多目标优化方法

Feixiang Chen1,2,3,4, Wangyang Xu4, Qing Wen1,2,3

  • 1CCCC Second Harbor Engineering Company Ltd., Wuhan 430070, China.

Materials (Basel, Switzerland)
|October 14, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了混凝土混合物设计的混合智能建模方法,结合了人工神经网络 (ANN) 和遗传算法 (GA). 该方法优化了混凝土的性能和成本,提高了施工效率和精度.

关键词:
这里是Scipy图书馆.人工神经网络 (ANN) 是一个人工神经网络.混凝土混凝土混凝土是什么基因算法 (GA) 是一种基因算法.混合设计混合设计混合设计.多目标优化多目标优化

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科学领域:

  • 土木工程和材料科学 土木工程和材料科学
  • 计算智能和优化计算智能和优化

背景情况:

  • 混凝土混合物设计对于结构性能和成本效益至关重要.
  • 传统方法往往缺乏精确性和适应性,以满足复杂的优化目标.
  • 计算智能的进步为优化材料设计提供了新的途径.

研究的目的:

  • 为混凝土混合物设计开发一种新的混合智能建模方法.
  • 使用人工智能预测混凝土的机械性能并优化混合比例.
  • 创建一个实用的,基于人工智能的系统,用于高效的混凝土混合设计.

主要方法:

  • 混合智能建模集成人工神经网络 (ANN) 和遗传算法 (GA).
  • 基于GA的ANN参数的优化,以提高预测准确度 (R2 > 0.95).
  • 使用Scipy库和GA-ANN进行多目标优化,以平衡压力强度和成本.

主要成果:

  • 实现了混凝土机械性能 (R2>0.95,低RMSE和MAE) 的高预测精度.
  • 成功优化混凝土混合物比例,以实现多目标目标,平衡强度和成本.
  • 开发了一个用户友好的基于人工智能的系统,用于实际的混凝土混合设计.

结论:

  • 混合GA-ANN方法为混凝土混合物设计提供了一种新且准确的方法.
  • 开发的AI系统为优化混凝土属性和成本提供了实用解决方案.
  • 这项研究促进了建筑行业混凝土混合设计的效率和精度.