基于FOA-RF算法的心墙砾石土压实质量预测模型

Research on compaction quality prediction model of core wall gravel soil based on FOA-RF algorithm

针对随机森林(RF)算法预测心墙砾石土压实质量存在决策树数量选取缺乏深入研究和忽视P0.075质量分数对压实质量影响的问题,提出了一种基于果蝇优化(FOA)算法的随机森林算法(FOA-RF算法),并构建了基于FOA-RF算法的心墙砾石土压实质量预测模型(FOA-RF模型)。该模型一方面通过对料源参数和干密度进行相关性分析,新增了P0.075质量分数作为模型的输入参数;另一方面利用FOA算法对随机森林进行优化,解决了RF算法难以取得决策树数量最优解、没有同时考虑决策树数量与随机特征数影响的问题。以西南某在建砾石土心墙堆石坝工程为例,分别应用基于传统RF、BP神经网络、多元线性回归的预测模型和FOA-RF模型进行压实质量预测。结果表明,FOA-RF模型在预测精度上具有优越性,并基于该模型开发压实质量预测模块,将该模块嵌入碾压质量实时监控系统中可实现压实质量的实时预测。

Aiming at the problems that the random forest(RF)used in predicting compaction quality of core gravel soil,such as decision tree number selection and ignoring the influence of P0.075 mass fraction on compaction quality,a random forest(FOA-RF)algorithm based on the fruit fly optimization algorithm(FOA)is proposed,and a core wall gravel soil compaction quality prediction model based on the FOA-RF algorithm considering the content of P0.075 is constructed.On one hand,this model can analyze the correlation between material source parameters and dry density,and P0.075 content can be added as the input parameter.On the other hand,the FOA algorithm is used to optimize the random forest,which solves the problem that the RF algorithm is difficult to obtain the optimal solution of decision tree number and does not consider the influence of decision tree number and random feature number at the same time.Finally,taking a gravel-core wall rockfill dam project in construction in Southwest China as an example,the prediction model based on the traditional RF algorithm,BP neural network,multiple linear regression and the FOA-RF model was used to predict the compaction quality respectively.The result shows that the FOA-RF algorithm has superiority in prediction accuracy.Based on this model,a compaction quality prediction module can be developed and embedded in a real-time monitoring system for rolling quality,which can realize real-time prediction of compaction quality.