In order to improve the prediction accuracy of the SSA-BP model,three types of chaotic mapping sequences(CMS)were used to initialize the sparrow position respectively,which helped the SSA-BP model to jump out of the local extremes,thus improving the quality of the solution.Using 200 sets of actual concrete carbonation depth test data as running data,with adhesive dosage,fly ash replacement level,water-cement ratio,CO2 volume fraction,relative humidity,exposure time as input variables,and concrete carbonation depth as output variables,the values of each index were obtained after two runs,and the optimization points of each of the three types of CMS-SSA-BP models were compared and analyzed.The results showed that the SSA-BP models optimized with chaotic mapping sequences(CMS)had better prediction performance.Among them,Tent-SSA-BP model had the best prediction accuracy,Logistic-SSA-BP model had the best prediction stability,and Sine-SSA-BP model had the fastest convergence speed.
关键词
预测性能对比/BP模型/SSA-BP模型/混沌映射序列(CMS)/混凝土碳化深度
Key words
comparative of predictive performance/BP model/SSA-BP model/chaotic mapping sequences(CMS)/depth of concrete carbonation
维普
万方数据