为了解决大坝变形预测模型易陷入局部最优及不适用大规模数据等问题,采用一种快速高效的基于决策树的梯度提升框架LightGBM,并结合全局优化算法——贝叶斯优化进行大坝变形预测。为验证模型适用性,以两座实际混凝土坝工程为例分析,并与多元线性回归、支持向量回归机和多层神经网络等预测结果进行比较。结果表明,该模型均方根误差(RMSE)和平均绝对误差(MAE)等指标均优于其他方法,验证了该模型的可行性及优越性。LightGBM可对输入参数的重要性进行评估,对影响大坝变形的特征进行筛选,从而确定对大坝变形影响更显著的因素,为后续的安全评估工作提供参考。
Abstract
Deformation as an intuitive monitoring indicator reflects the operation state of a dam. Existing intelligent methods for dam deformation prediction are prone to local optimum and are inapplicable in the case of large-scale data. In this paper, we combine a fast and efficient gradient boosting framework Light Gradient Boosting Machine (LightGBM) based on the decision tree with the global optimization algorithm, Bayesian optimization, to predict dam deformation. Taking two concrete dams as case study, we compared the modelling result with those of multiple linear regression, support vector regression, and multi-layer perceptron to verify the applicability of the present model. The RMSE and MAE of the proposed model are both superior to those of other methods, which manifests the feasibility and superiority of the model. In addition, LightGBM can evaluate the importance of input parameters and select the features that affect dam deformation, so as to determine the factors that have more significant influence on dam deformation and offer reference for following safety assessment.
关键词
大坝变形预测 /
贝叶斯优化 /
梯度提升框架 /
多元线性回归 /
支持向量回归机 /
多层神经网络
Key words
dam deformation prediction /
Bayesian optimization /
LightGBM /
multiple linear regression /
support vector regression /
multi-layer perceptron
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 吴中如. 水工建筑物安全监控理论及其应用[M]. 北京: 高等教育出版社, 2003.
[2] 顾冲时,苏怀智,王少伟. 高混凝土坝长期变形特性计算模型及监控方法研究进展[J]. 水力发电学报,2016, 35(5): 1-14.
[3] 苏怀智,李金友. 重力坝工程病险除控实施效能评估研究述评[J]. 水力发电学报,2018, 37(4): 12-25.
[4] MATA J.Interpretation of Concrete Dam Behaviour with Artificial Neural Network and Multiple Linear Regression Models[J]. Engineering Structures,2011,33(3):903-910.
[5] 万 臣,李建峰,赵 勇,等.基于新维BP神经网络-马尔科夫链模型的大坝沉降预测[J]. 长江科学院院报,2015,32(10):23-27,32.
[6] 吉培荣,黄巍松,胡翔勇. 灰色预测模型特性的研究[J]. 系统工程理论与实践,2001(9):105-108.
[7] 朱军桃,程 胜,邢 尹. 改进灰狼算法优化SVM的大坝变形预测[J]. 桂林理工大学学报,2019,39(3):669-673.
[8] 杨贝贝. 基于小波核函数和支持向量机的大坝变形预测[J]. 人民长江,2016, 47(17): 98-101.
[9] 卢献健,罗 乐,胡应剑,等. 基于GA-PSO-BP的大坝变形监测模型[J]. 桂林理工大学学报,2020,40(2): 1-7.
[10] SALAZAR F, TOLEDO M A, OÑATE E, et al. An Empirical Comparison of Machine Learning Techniques for Dam Behaviour Modelling[J]. Structural Safety, 2015, 56: 9-17.
[11] KE G, MENG Q, FINLEY T, et al. LightGBM: A Highly Efficient Gradient Boosting Decision Tree[C]//Proceedings of the 31st Conference on Neural Information pricessing Systems (NIPS 2017). Long Beach, CA, United States: Neural Information Processing Systems Foundation. December 4-9, 2017: 3149-3157.
[12] SNOEK J, LAROCHELLE H, ADAMS R P. Practical Bayesian Optimization of Machine Learning Algorithms[C]// Proceedings of the 26th Conference on Neural Information pricessing Systems (NIPS 2012). Lake Tahoe, NV, United States: Neural Information Processing Systems Foundation. December 3-6, 2012: 2951-2959.
[13] TANG M, ZHAO Q, DING S X, et al. An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes[J]. Energies, 2020, 13(4): 807.
[14] 崔佳旭,杨 博. 贝叶斯优化方法和应用综述[J]. 软件学报,2018, 29(10): 3068-3090.
[15] 徐 锋,方彦军. 基于贝叶斯优化XGBoost的现场校验仪误差预测[J]. 电测与仪表,2019, 56(18):120-125.
[16] HU Jiang, WU Su-hua. Statistical Modeling for Deformation Analysis of Concrete Arch Dams with Influential Horizontal Cracks[J]. Structural Health Monitoring, 2018, 18(2): 546-562.
基金
国家重点研发计划项目(2018YFC1508603);国家自然科学基金项目 (51739003)
PDF(3306 KB)
