基于人工神经网络的锈蚀钢筋强度预测

Strength prediction of corroded reinforcement based on artificial neural network

影响钢筋锈蚀后强度的因素多而复杂,目前尚没有普遍认可的统一范式。本文采用了人工神经网络自适应分析的方法,通过对大量试验数据的自适应分析,对造成钢筋锈蚀的多重因素进行综合分析,计算力学性能的变化范围,构建了预测模型。从得到的实验结果来看,预测模型具备较好的适用性,能够满足工程精度的要求,有望成为评估锈蚀后钢筋强度的一种新方法。

There are many and complex factors that affect the strength of corroded steel bars,and there is no universally recognized unified paradigm at present.In this paper,the adaptive analysis method of artificial neural network is adopted.Through the adaptive analysis of a large number of test data,the multiple factors that cause reinforcement corrosion are comprehensively analyzed,the range of changes in mechanical properties is calculated,and a prediction model is built.From the experimental results obtained,the prediction model has good applicability,can meet the requirements of engineering accuracy,and is expected to become a new method to evaluate the strength of corroded steel bars.