nav emailalert searchbtn searchbox tablepage yinyongbenwen piczone journalimg searchdiv qikanlogo popupnotification paper
2024 06 v.50;No.274 103-111
基于可解释预测模型的Z型立柱片剪切刚度人工认知
基金项目(Foundation): 国家重点研发计划(2017YFB1304000)
邮箱(Email): lvzj@dhu.edu.cn;
DOI: 10.19886/j.cnki.dhdz.2023.0129
中文作者单位:

东华大学机械工程学院;上海仓储物流设备工程技术研究中心;上海精星仓储设备工程有限公司;

摘要(Abstract):

由立柱、斜横撑等钢结构件组合而成的立柱片的剪切刚度对高层工业货架侧向抗震性能至关重要。由于结构件的特殊性以及连接方式的多样性,目前使用通用的解析计算模型描述立柱片的剪切行为还比较困难。为此,以Z型立柱片为例提出了一种可解释的立柱片剪切刚度预测模型。通过基于性能试验的有限元仿真获取了不同立柱片结构的剪切刚度数据,并基于XGBoost算法建立立柱片剪切刚度可解释预测模型。与神经网络等其他机器学习算法相比,该预测模型分析与有限元数值计算结果接近且便于理解。以此为基础,采用SHAP(SHapley addictive explanation)方法以可视化的方式对Z型立柱片剪切行为进行人工认知。结果显示,提高剪切性能的关键在于立柱和斜横撑截面的设计以及斜横撑结构配置模式;而不同结构配置模式下,可以采用针对性的设计方法对立柱片剪切性能进行改进。

关键词(KeyWords): 立柱片;;剪切刚度;;数据驱动;;XGBoost;;SHAP;;人工认知
参考文献

[1] GILBERT B P,RASMUSSEN K J R,BALDASSINO N,et al.Determining the transverse shear stiffness of steel storage rack upright frames [J].Journal of Constructional Steel Research,2012,78:107-116.

[2] TALEBIAN N,GILBERT B P,BALDASSINO N,et al.Factors contributing to the transverse shear stiffness of bolted cold-formed steel storage rack upright frames with channel bracing members [J].Thin-Walled Structures,2019,136:50-63.

[3] 高文龙,万建宏,刘思威.非对称开口薄壁型钢截面构件的稳定性设计理论 [J].建筑钢结构进展,2021,23(5):53-62.GAO W L,WAN J H,LIU S W.A stability design theory for the steel members using asymmetric thin-walled open-sections [J].Progress in Steel Building Structures,2021,23(5):53-62.

[4] Rack Manufacturers Institute.Specification for the design,testing and utilization of industrial steel storage racks:ANSI-RMI MH16.1[S].Charlotte:Rack Manufacturers Institute,2021.

[5] European Committee for Standardization.Steel static storage systems:EN15512[S].Brussels:European Committee for Standardization,2009.

[6] Standards Association of Australia.Steel storage racking:AS4084[S].Sydney:Standards Association of Australia,2012.

[7] SAJJA S R,BEALE R G,GODLEY M H R.Shear stiffness of pallet rack upright frames [J].Journal of Constructional Steel Research,2008,64(7/8):867-874.

[8] TALEBIAN N,GILBERT B P,BALDASSINO N,et al.Finite element modeling of bolted cold-formed steel storage rack upright frames [J].Applied Mechanics and Materials,2016,846:251-257.

[9] ROURE F,PEK?Z T,SOMALO M R,et al.Cross-aisle stiffness analysis of industrial welded cold-formed steel rack upright frames [J].Thin-Walled Structures,2019,141:332-344.

[10] SAJJA S R,BEALE R G,GODLEY M H R.Cross-aisle stiffness tests on rack upright frames[C]//Twentieth International Specialty Conference on Cold-Formed Steel Structures,St.Louis,Missouri,U.S.A.,2010:367-381.

[11] QU D C,CAI X P,CHANG W.Evaluating the effects of steel fibers on mechanical properties of ultra-high performance concrete using artificial neural networks [J].Applied Sciences,2018,8(7):1120.

[12] 宋一铭,吕志军,陈东,等.非连续截面薄壁钢构件稳定性智能预测方法 [J].机械强度,2018,40(3):653-659.SONG Y M,LYU Z J,CHEN D,et al.Intelligent prediction method on stability of thin-wall steel component with non continuous cross section [J].Journal of Mechanical Strength,2018,40(3):653-659.

[13] SHAH S N R,RAMLI SULONG N H,EL-SHAFIE A.New approach for developing soft computational prediction models for moment and rotation of boltless steel connections [J].Thin-Walled Structures,2018,133:206-215.

[14] LYU Z J,ZHAO P C,LU Q,et al.Prediction of the bending strength of boltless steel connections in storage pallet racks:an integrated experimental-FEM-SVM methodology [J].Advances in Civil Engineering,2020,2020:5109204.

[15] PENG Z,LI J,HAO H.Data driven structural damage assessment using phase space embedding and Koopman operator under stochastic excitations [J].Engineering Structures,2022,255:113906.

[16] SUN H,BURTON H V,HUANG H L.Machine learning applications for building structural design and performance assessment:state-of-the-art review [J].Journal of Building Engineering,2021,33:101816.

[17] 任重,刘家巍,戴柳丝.冷弯薄壁型钢货架梁柱节点子结构抗连续性倒塌性能研究 [J].建筑钢结构进展,2022,24(9):36-44.REN C,LIU J W,DAI L S.Progressive collapse esistance of cold-formed thin-walled steel storage rack beam-to-upright connection substructure [J].Progress in Steel Building Structures,2022,24(9):36-44.

[18] ZHOU S Q,LIU Z Y,WANG M,et al.Impacts of building configurations on urban stormwater management at a block scale using XGBoost [J].Sustainable Cities and Society,2022,87:104235.

[19] M?LLER A,RUHLMANN-KLEIDER V,LELOUP C,et al.Photometric classification of type Ia supernovae in the SuperNova Legacy Survey with supervised learning [J].Journal of Cosmology and Astroparticle Physics,2016,2016(12):8.

[20] 陈洞天,单杰,周文丹.基于Xgboost的心血管疾病预测模型和指标分析研究 [J].现代医院,2021,21(6):958-961.CHEN D T,SHAN J,ZHOU W D.An xgboost-based cardiovascular disease prediction model and index analysis research [J].Modern Hospitals,2021,21(6):958-961.

[21] 汪祖民,聂晓宇,王颖洁,等.基于小样本的胶囊网络轴承故障诊断方法 [J].计算机工程与设计,2023,44(4):1259-1266.WANG Z M,NIE X Y,WANG Y J,et al.Capsule network bearing fault diagnosis method based on small sample[J].Computer Engineering and Design,2023,44(4):1259-1266.

[22] 乔世超,王轶男,吕佳阳,等.基于SC-XGBoost的电站燃煤低位发热量软测量方法[J/OL].煤炭科学技术.https://kns.cnki.net/kcms2/detail/11.2402.TD.20230524.1444.003.html.QIAO S C,WANG Y N,LYU J Y,et al.SC-XGBoost based soft measurement method for coal low heat value in power station [J/OL].煤炭科学技术.https://kns.cnki.net/kcms2/detail/11.2402.TD.20230524.1444.003.html.

[23] LYU Z Q,YU Y,SAMALI B,et al.Back-propagation neural network optimized by K-fold cross-validation for prediction of torsional strength of reinforced concrete beam [J].Materials,2022,15(4):1477.

[24] LUNDBERG S M,ERION G,CHEN H,et al.From local explanations to global understanding with explainable AI for trees [J].Nature Machine Intelligence,2020,2:56-67.

基本信息:

DOI:10.19886/j.cnki.dhdz.2023.0129

中图分类号:TH114;TH122

引用信息:

[1]褚铭,吕志军,陈齐等.基于可解释预测模型的Z型立柱片剪切刚度人工认知[J].东华大学学报(自然科学版),2024,50(06):103-111.DOI:10.19886/j.cnki.dhdz.2023.0129.

基金信息:

国家重点研发计划(2017YFB1304000)

引用

GB/T 7714-2015 格式引文
MLA格式引文
APA格式引文
检 索 高级检索