李启明, 喻泽成, 余波, 宁超列. 钢筋混凝土柱地震破坏模式判别的两阶段支持向量机方法[J]. 工程力学, 2022, 39(2): 148-158. DOI: 10.6052/j.issn.1000-4750.2020.12.0937
引用本文: 李启明, 喻泽成, 余波, 宁超列. 钢筋混凝土柱地震破坏模式判别的两阶段支持向量机方法[J]. 工程力学, 2022, 39(2): 148-158. DOI: 10.6052/j.issn.1000-4750.2020.12.0937
LI Qi-ming, YU Ze-cheng, YU Bo, NING Chao-lie. TWO-STAGE SUPPORT VECTOR MACHINE METHOD FOR FAILURE MODE CLASSIFICATION OF REINFORCEDCONCRETE COLUMNS[J]. Engineering Mechanics, 2022, 39(2): 148-158. DOI: 10.6052/j.issn.1000-4750.2020.12.0937
Citation: LI Qi-ming, YU Ze-cheng, YU Bo, NING Chao-lie. TWO-STAGE SUPPORT VECTOR MACHINE METHOD FOR FAILURE MODE CLASSIFICATION OF REINFORCEDCONCRETE COLUMNS[J]. Engineering Mechanics, 2022, 39(2): 148-158. DOI: 10.6052/j.issn.1000-4750.2020.12.0937

钢筋混凝土柱地震破坏模式判别的两阶段支持向量机方法

TWO-STAGE SUPPORT VECTOR MACHINE METHOD FOR FAILURE MODE CLASSIFICATION OF REINFORCEDCONCRETE COLUMNS

  • 摘要: 研究提出了一种钢筋混凝土(RC)柱地震破坏模式判别的两阶段支持向量机(Support Vector Machine,简称SVM)方法。根据RC柱的三种地震破坏模式(弯曲破坏、弯剪破坏和剪切破坏),建立了RC柱地震破坏模式判别的两阶段SVM模型;基于270组试验数据,利用十折交叉验证和网格寻优方法确定了两阶段SVM的关键模型参数(即惩罚参数和核函数参数)的最优取值;同时,利用基于SVM的回归特征消去法(SVM-RFE),分析了轴压比、剪跨比、箍筋间距与截面有效高度比(s/h0)、纵筋参数和箍筋参数等特征参数对RC柱地震破坏模式的影响程度;通过与两种经典机器学习方法和五种传统破坏模式判别方法的对比分析,验证了该方法的有效性。分析结果表明:所提出的两阶段SVM方法对于三种破坏模式的判别准确率都能达到90%以上,整体判别准确率比经典的机器学习方法提高10%以上,比传统的地震破坏模式判别方法提高20%以上;对于RC柱是否发生弯曲破坏,剪跨比和纵筋参数的影响较大,其次是箍筋参数和s/h0,而轴压比的影响相对较小;对于RC柱是否发生弯剪破坏,纵筋参数的影响较大,其次是剪跨比和s/h0,而箍筋参数和轴压比的影响相对较小。

     

    Abstract: A two-stage support vector machine (SVM) method for the failure mode classification of reinforced concrete (RC) columns is proposed. A two-stage SVM model is established to classify the seismic failure modes of RC columns according to three failure modes, namely, flexure failure, flexure-shear failure and shear failure. The optimal values of model parameters (i.e., penalty parameters and kernel function parameter) of the two-stage SVM model are determined by using ten-fold cross-validation and grid-search based on 270 experimental data. Subsequently, the characteristic parameters including the axial load ratio, shear span ratio, hoop spacing to depth ratio (s/h0), longitudinal reinforcement index and transverse reinforcement index on the seismic failure modes of RC columns are analyzed by the support vector machine-recursive feature elimination (SVM-RFE). The classification accuracy of the proposed classification method is validated by comparing with two classical machine learning methods and five traditional classification methods. The results indicate that the accuracy of the proposed method is generally higher than 90% for three failure modes, which is 10% higher than the classical machine learning methods and 20% higher than the traditional classification methods. The shear-span ratio and longitudinal reinforcement index have significant influences on whether the RC column fails in flexure. They are followed by the transverse reinforcement index and s/h0, while the axial load ratio has negligible influence. The longitudinal reinforcement index has significant influence on whether the RC column fails in flexure-shear. It is followed by the shear-span ratio and s/h0, while the transverse reinforcement index and axial load ratio have negligible influence.

     

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