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相关概念视频

Design Example: Managing Concrete Workability01:14

Design Example: Managing Concrete Workability

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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.
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Optimization Problems01:26

Optimization Problems

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Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
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Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

365
The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
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Optimal Foraging00:48

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How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
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Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

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Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
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Design Example: Maintaining Level of an Embankment01:19

Design Example: Maintaining Level of an Embankment

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Constructing a roadway embankment over uneven terrain requires precise leveling to ensure stability and proper drainage. Surveyors use a leveling instrument and staff to calculate ground elevations and determine the required fill material at each point along the embankment alignment.The process begins by positioning a leveling instrument near a benchmark with a known elevation. A backsight reading establishes the instrument height, which serves as a reference for subsequent measurements. A...
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相关实验视频

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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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一个改进的多目标动画麦优化算法,用于资源受限的建筑项目组织设计.

Qing Xue1, Chunjiang Wu2, Jiyang Nie1

  • 1School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China.

Scientific reports
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PubMed
概括
此摘要是机器生成的。

本研究介绍了多目标动画麦优化 (MOAOO) 算法,以改进建筑项目组织设计. MOAOO提高了资源配置,平衡了成本和持续时间等相互矛盾的目标,并证明了工程管理的实际价值.

关键词:
有限制的项目日程安排.成本贸易贸易成本贸易这种算法是Metaheuristic算法.在外面,在外面.项目管理 项目管理资源 资源 资源 资源时间 时间 时间 时间

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科学领域:

  • 工程管理工程管理工程管理
  • 计算智能是一种计算智能.
  • 优化算法 优化算法

背景情况:

  • 全球城市化推动了大规模的建筑项目,需要优化组织设计.
  • 传统方法在资源效率,多目标冲突解决和动态调整方面面临挑战.
  • 现有的优化技术对于复杂的建筑项目组织设计是不够的.

研究的目的:

  • 开发动画麦优化算法 (MOAOO) 的第一个多目标扩展,用于建筑项目组织设计.
  • 解决资源分配和多目标冲突管理中的经验决策的局限性.
  • 为增强动态调整能力引入一种新的优化方法.

主要方法:

  • 通过扩展动画麦优化生物机制来开发MOAOO.
  • 整合了混合位置更新规则,使用精英参考点和随机扰动.
  • 实施了三层约束处理机制和双值趋同监控系统.
  • 集成的动态精英保留,非主导排序和动态档案机制,用于协作优化.

主要成果:

  • 在 ZDT 测试函数上,MOAOO 证明了与先进算法 (Pre-DEMO,MOEA/D-OED,Pi-MOEA) 相比具有竞争力的性能.
  • 在一个案例研究中,实现了资源波动 (72.7%) 的显著减少.
  • 成功地平衡了相互矛盾的目标:持续时间 (279天) 和成本 (1.34万美元).
  • 在平均118次代中有效地融合.

结论:

  • MOAOO是AOO的首个多目标变体,为建筑项目设计提供有效的协作优化.
  • 该算法克服了传统方法的局限性,在施工调度中提供了实际价值.
  • MOAOO为复杂,多目标的工程管理挑战提供了强大而高效的解决方案.