使用Ridgelet神经网络进行混凝土裂检测,并通过先进的人类进化优化进行优化
Yongqing Lin1, Mehdi Ahmadi2, Khalid A Alnowibet3
1Beijing Jiaotong Vocational Technical College, Beijing, China.
Scientific reports
|February 9, 2025
概括
这项研究引入了使用高级人类进化优化 (AHEO) 算法进行优化的Ridgelet神经网络 (RNN),用于准确地检测混凝土裂. 这种新的方法在确定具体框架中的结构性弱点方面取得了很高的表现.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 混凝土结构容易发生裂,从而损害其完整性和耐用性.
- 准确的裂诊断对于保持结构安全和性能至关重要.
研究的目的:
- 开发一种用于混凝土裂诊断的创新算法.
- 使用一种新的进化算法优化深度神经网络 (DNN).
主要方法:
- 一个优化的深度神经网络 (DNN) 被称为Ridgelet神经网络 (RNN) 被开发出来.
- 使用新的高级人类进化优化 (AHEO) 算法增强了RNN模型.
- 一个标记的图像数据集被用于训练,AHEO算法改进权重,调整输出层,并增强数据集.
主要成果:
- 在混凝土裂检测方面,RNN/AHEO模型实现了高精度 (99.665%) 和F1得分 (99.035%).
- 该模型与CNN,CrackUnet,R-CNN,DCNN和U-Net等现有方法相比,表现出更高的性能.
- AHEO算法有效地优化了RNN用于裂的二进制分类.
结论:
- 开发的RNN/AHEO模型为混凝土裂诊断提供了强大的,高度准确的解决方案.
- 这种创新方法显著改善了在具体框架中检测结构性弱点的方法.
- 该研究强调了将先进的神经网络与基础设施监控的进化优化相结合的潜力.
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