1.宁波大学 土木与环境工程学院，浙江 宁波 315211;2.电子科技大学 计算机科学与工程学院， 四川 成都 611731;3.青岛农业大学 建筑工程学院，山东 青岛 266109
王建民（1974—），男，山西运城人，宁波大学教授，博士生导师，博士. E-mail：wangjianmin@nbu.edu.cn
柳俊哲（1964—），男，黑龙江五常人，青岛农业大学教授，博士生导师，博士. E-mail：junzheliu@163.com
TU528.2
国家自然科学基金资助项目（51878360，52178223）；宁波市自然科学基金资助项目（202003N4136）
1.School of Civil and Environmental Engineering， Ningbo University， Ningbo 315211， China;2.School of Computer Science and Engineering， University of Electronic Science and Technology of China， Chengdu 611731， China;3.School of Architecture Engineering， Qingdao Agricultural University， Qingdao 266109， China
制备了陶粒轻骨料混凝土与普通混凝土叠合试块，以分组试验数据为小样本，采用端到端的梯度提升决策树（GBDT）集成学习算法，建立了混凝土叠合面处理方式、浇筑间隔时间及法向作用力等输入特征参数与叠合面黏结强度之间的预测模型；并将GBDT模型预测结果与支持向量回归、K近邻回归、决策树和BP神经网络等模型的预测结果进行综合对比.结果表明：GBDT模型预测结果的拟合优度、平均绝对误差和均方根误差均优于其它模型，其测试样本集的平均相对误差明显小于其它模型.所建立的GBDT模型具有较高的准确率，可对混凝土叠合面黏结强度的变化进行满意的预测分析.
By making composite blocks of ceramsite lightweight aggregate concrete（LWAC） and normal concrete（NC）， the end-to-end gradient boosting decision tree（GBDT） predicting model was proposed based on the grouping experiment， which correlates composite interface preparing method， casting interval time and normal force on composite interface to bonding strength between LWAC and NC. The results from GBDT bonding strength prediction model are compared with those from the support vector machine regression model， K-nearest neighbor regression model， the decision tree and BP neural network. The comparison shows that the designed GBDT model is more robust than the other four models with superior predictive performance synthetically testified by the goodness of fit， mean absolute error and root mean squared error. In addition， the mean relative error from GBDT prediction for the test samples is obviously smaller than that from the other four models. An effective and satisfactory prediction result can be obtained for the composite interface bonding strength between LWAC and NC from the established GBDT model.
王建民,叶钰蓉,饶超敏,卓仁杰,柳俊哲.基于
