开发一种脑启发的多层神经网络架构,以快速准确地估计混凝土的压力强度
Bashar Alibrahim1, Ahed Habib2, Maan Habib3
1Department of Civil Engineering, Eastern Mediterranean University, via Mersin 10, Famagusta, North Cyprus, Türkiye.
Scientific reports
|January 15, 2025
概括
一个新的多叶片人工神经网络 (MLANN) 改善了混凝土压力强度的估计. 与传统的人工神经网络 (ANN) 和集体学习神经网络 (ELNN) 相比,这种新的方法提高了准确性和效率.
科学领域:
- 土木工程 土木工程是指土木工程.
- 机器学习 机器学习
- 材料科学 材料科学 材料科学
背景情况:
- 混凝土的压力强度对于结构完整性至关重要,但传统的测试是耗时的.
- 人工神经网络 (ANN) 提供了准确估计的潜力,但深度网络可能是低效的,容易出现错误.
- 现有的ANN经常与大数据集和泛化作斗争,导致精度降低.
研究的目的:
- 为改进混凝土压力强度估计引入一种新的多叶片人工神经网络 (MLANN) 架构.
- 为了提高预测混凝土压力强度的准确性和效率.
- 解决传统ANNs的局限性,例如长时间的培训时间和噪音易感性.
主要方法:
- 开发和评估了一种新的MLANN架构,灵感来自于大脑叶片处理.
- 利用独特的隐藏层架构 ("叶片") 来优化数据处理和减少训练噪音.
- 与传统的人工神经网络 (ANN) 和集体学习神经网络 (ELNN) 相比,比较了MLANN的性能.
主要成果:
- MLANN显著提高了混凝土压力强度的估计性能.
- 根平均平方误差 (RMSE) 降低了高达32.9%,平均绝对误差 (MAE) 降低了高达25.9%.
- 提高了A20指数高达17.9%,表明一个更强大的和可通用的模型.
结论:
- 在预测混凝土压力强度方面,MLANN架构提供了卓越的准确性和效率.
- 这种进步可以导致更可靠,更具成本效益的土木工程设计和施工.
- MLANN为结构分析提供了一个精细的模型,克服了传统神经网络的局限性.
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