盾构法是隧道施工的主流方法,广泛应用于软土地层与复合地层.盾构机在上软下硬地层中掘进时,准确预测滚刀的磨损以便及时更换滚刀是确保施工安全与效率的关键.基于机器学习中的无监督kmeans聚类算法、有监督Transformer算法以及遗传算法,提出了一种根据刀盘前方地层条件、滚刀布设以及施工参数预测滚刀正常磨损的方法.kmeans聚类算法用于实时分析当前盾构施工参数间的关系,从而划分不同施工状态,并为不同施工状态分配不同的磨损修正系数作为Transformer模型的输入参数.根据地层条件及施工参数相关关系确定磨损修正系数初始值的大小,借助遗传算法对磨损修正系数进行优化.Transformer算法中,以地层条件、施工参数、滚刀安装半径和切削距离以及kmeans聚类得到的磨损修正系数作为输入参数,以滚刀磨损量作为输出参数,并由遗传算法对模型的超参数...Abstract：Shield tunneling is the main method for tunnel construction,widely used in soft strata and composite strata.How to accurately predict the tool wear when the shield machine is tunneling in the upper soft and lower hard strata is an important issue to ensure construction safety and efficiency.Based on the unsupervised clustering algorithm,supervised Transformer algorithm and genetic algorithm in machine learning,a method is proposed to predict tool wear based on the ground conditions,tool placement and construction parameters.The kmeans clustering algorithm is used to analyze the relationship between the shield construction parameters,so as to classify different construction states in order to assign diffe.
