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相关概念视频

Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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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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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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相关实验视频

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增强基于深度学习的斜率稳定性分类,使用新的元启发式优化算法来进行特征选择.

Bilel Zerouali1, Nadjem Bailek2,3, Aqil Tariq4

  • 1Laboratory of Architecture, Cities and Environment, Department of Hydraulic, Faculty of Civil Engineering and Architecture, Hassiba Benbouali University of Chlef, B.P. 78C, 02180, Ouled Fares, Chlef, Algeria.

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概括

机器学习模型,特别是生成对抗网络 (GAN),有效地对斜率稳定性进行分类. 将特征选择与GAN结合起来,可显著提高关键地质工程应用的准确性.

关键词:
功能选择 功能选择地质技术的地质技术.自然危害 自然危害斜坡的稳定性 斜坡的稳定性土壤的稳定性 土壤的稳定性

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科学领域:

  • 地质技术工程 地质技术工程
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 斜坡稳定性对于基础设施安全和危险减轻至关重要.
  • 确定影响斜坡稳定的关键因素对于准确的评估至关重要.
  • 传统方法可能无法完全捕捉斜率稳定性预测的复杂性.

研究的目的:

  • 确定影响斜坡稳定的最有影响的因素.
  • 为了评估各种机器学习模型的性能,用于斜坡稳定性分类.
  • 评估高级特征选择技术对模型性能的影响.

主要方法:

  • 相关性分析和随机森林回归因特征的重要性.
  • 深度学习模型的评估:RNN,LSTM和GAN.
  • 将二进制bGGO特征选择与GAN模型集成.

主要成果:

  • 凝聚力,单位重量,斜坡高度和摩擦角度被确定为关键因素.
  • 该GAN模型实现了高精度 (0.913) 和AUC (0.9285).
  • bGGO-GAN模型表现出卓越的性能,在627个样本上达到95%的准确性.

结论:

  • 先进的机器学习,特别是具有特征选择的GAN,显著改善了坡度稳定性分类.
  • bGGO-GAN模型提供了高精度,可概括性和增强的预测值.
  • 这种方法为地质工程和危险减轻提供了宝贵的见解.