一种混合人工智能方法,用于建模含有飞灰的可持续混凝土的碳化深度
1Department of Civil Engineering, Hakim Sabzevari University, Sabzevar, Iran. raminkazemihsu@gmail.com.
Scientific reports
|May 24, 2024
概括
这项研究引入了一种新的混合人工智能模型,将人工神经网络与基于生物地理的优化相结合,以准确预测可持续混凝土中的碳化深度. 与传统方法相比,新模型显著减少了预测错误,提高了结构耐久性评估.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 钢筋混凝土结构的耐用性受到碳化挑战,碳二氧化碳透到混凝土的过程增加了腐蚀风险.
- 传统的碳化深度 (CD) 测试是耗时的,需要熟练的技术人员.
- 结合飞灰 (CCFA) 的可持续混凝土提供了更好的质地和耐碳化性能.
研究的目的:
- 开发一种基于人工智能的替代方法来建模含有飞灰 (CCFA) 的可持续混凝土的碳化深度 (CD).
- 为CD预测引入和评估一种新的混合人工神经网络 (ANN) 和基于生物地理的优化 (BBO) 模型.
- 将混合ANN-BBO模型的性能与传统ANN模型和以前的研究进行比较.
主要方法:
- 开发一种混合模型,将人工神经网络 (ANN) 与基于生物地理的优化 (BBO) 集成在一起,以估计碳化深度 (CD).
- 利用人工智能技术超越传统的基于实验室的CD测试方法.
- 分析错误分布和性能指标 (A10指数),以验证模型的准确性和可靠性.
主要成果:
- 混合ANN-BBO模型比独立ANN模型减少了11%的预测错误,70%的预测在±1毫米的误差范围内.
- 与ANN (0.5019) 相比,A10指数显示ANN-BBO模型的性能提高了78% (0.8947),表明预测准确度更高.
- 暴露时间,二氧化碳度和水/粘合剂比率被确定为影响CD的最有影响力的变量.
结论:
- 开发的混合ANN-BBO模型为预测可持续混凝土的碳化深度提供了一种可靠和可泛化的方法.
- 这种人工智能驱动的方法为传统的CD测试提供了更有效的替代方案,有助于进行结构耐用性评估.
- 该研究证实了混合ANN-BBO模型在估计含飞灰的混凝土的CD方面的优势.
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