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优化混凝土裂检测和回声状态网络方法,改进鱼类迁移优化.

Zhichun Fang1, Xiuhong Wang2, Jiaojiao Gao3

  • 1Institute of Civil and Architectural Engineering, Tongling University, Tongling, 244061, Anhui, China.

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PubMed
概括
此摘要是机器生成的。

本研究介绍了用于混凝土裂检测的增强鱼类迁移优化 (IFMO) 和回声状态网络 (ESN) 模型. 与现有方法相比,新模型显著提高了准确性和F1得分.

关键词:
混凝土裂探测器 混凝土裂探测器响应状态网络的回声状态网络功能提取 功能提取图像的分类图像的分类.改善了鱼类迁移优化,改善了鱼类迁移的优化.SDNET2018 数据集 SDNET2018 数据集

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科学领域:

  • 土木工程 土木工程是指土木工程.
  • 人工智能的人工智能
  • 材料科学 材料科学 材料科学

背景情况:

  • 混凝土结构易受各种因素的裂影响,影响安全性和结构完整性.
  • 精确的混凝土裂检测对于基础设施评估至关重要,但现有的模型面临着过度装配和计算复杂性等挑战.
  • 目前的方法往往缺乏可靠的裂识别和分类所需的效率和稳定性.

研究的目的:

  • 为混凝土裂检测和分类提出一个优化的模型.
  • 使用人工智能提高混凝土裂识别的准确性和效率.
  • 解决现有的混凝土裂检测模型的局限性.

主要方法:

  • 开发了一种增强的鱼类迁移优化 (IFMO) 算法.
  • 优化回声状态网络 (ESN) 模型与IFMO集成,以提高性能.
  • 拟议的ESN/IFMO模型在SDNET2018数据集上进行了评估.

主要成果:

  • 在混凝土裂检测方面,ESN/IFMO模型表现出卓越的性能.
  • 与最先进的模型 (DL,DINN,AlexNet,CNN,LSTM) 相比,准确性增加了3.75-8.19%.
  • 与现有方法相比,F1得分得到了5.14-12.55%的改善.

结论:

  • ESN/IFMO模型为混凝土裂识别提供了更有效的解决方案.
  • 优化的ESN布局显著提高了检测准确度.
  • 这种方法显示了对现实世界基础设施安全评估的巨大潜力.