PREDICTION AND INTERPRETATION OF FAILURE MODES OF GROUTED SLEEVE BY COMBINED KNOWLEDGE-DRIVEN AND DATA-DRIVEN METHODOLOGY
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摘要:
为精准预测全灌浆套筒失效模式(钢筋拉断、钢筋拔出)，该文提出一种基于知识和数据联合驱动的全灌浆套筒失效模式预测方法。根据348例全灌浆套筒试验数据，通过机器学习回归算法与规范公式分别得到全灌浆套筒灌浆连接承载力预测值与钢筋极限抗拉承载力计算值，并基于领域知识建立两者与失效模式关系的特征参数，将其与试验数据组成新的数据库。基于新的数据库，通过机器学习分类算法对全灌浆套筒失效模式自动预测。研究结果表明，随机森林算法对全灌浆套筒灌浆连接承载力与失效模式均具有最好的预测效果。相比其他全灌浆套筒失效模式预测方法，该文提出的知识和数据联合驱动预测方法更具竞争力，总体分类准确率可达到94.3%。结合Shapley additive explanations (SHAP)方法和Partial dependent plot (PDP)方法对预测结果进行解释，审视各参数对失效模式的影响。SHAP与PDP的解释结果都表明钢筋锚固长度与灌浆料抗压强度是对失效模式影响最大的两个试验参数，其中PDP解释结果还给出两者影响失效模式转化的临界值，分别为6.01 d (d为钢筋直径)、80.87 MPa。
Abstract:In order to accurately predict the failure mode (rebar fracture and rebar pull-out) of grouted sleeves, a method driven by knowledge and data is proposed. Based on the test data of 348 cases of monotonically stretched grouted sleeves, the predicted value for grouting connection bearing capacity of grouted sleeve and the calculated value for ultimate tensile bearing capacity of rebar are obtained through machine learning regression algorithm and code formula respectively. The characteristic parameters of the relationship between them and failure mode are established based on domain knowledge, which is combined with the test data to form a new database. The failure mode is automatically predicted by using the new database and machine learning classification algorithm. The results show that the random forest algorithm has the best prediction effect on the bearing capacity and the failure mode. Compared with other prediction methods of the failure mode, the method driven by knowledge and data is more competitive in this paper. The general classification accuracy can reach 94.3%. The prediction results are interpreted by combining the methods of shapley additive explanations (SHAP) and partial dependent plot (PDP), and the influence of various parameters on failure modes is examined. Both the interpretive results of SHAP and PDP indicate that the anchorage length of rebar and the compressive strength of grouting material are the two test parameters that have the greatest influence on the failure mode. The interpretive results of PDP also give their critical values affecting the transformation of failure modes, which are 6.01d (d is the diameter of rebar) and 80.87 MPa, respectively.
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Keywords:
- grouted sleeve /
- failure mode /
- knowledge-driven /
- data-driven /
- interpretation of results
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图 1 知识和数据联合驱动方法分析流程
Figure 1. Analysis process of combined data-driven and knowledge-driven method
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图 2 套筒灌浆连接承载力预测结果
Figure 2. Prediction results of bearing capacity of grouted sleeve connection
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图 5 知识与数据联合驱动的测试集混淆矩阵
Figure 5. Confusion matrices of test set based on combined data-driven and knowledge-driven method
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图 6 使用新数据检验模型的泛化性能
Figure 6. Checking generalisation performance for models using new data
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13 Ld、Pu/Fu、fc、Z、Q的PDP解释结果验证
13. Validation of PDP interpretation results for Ld, Pu/Fu, fc, Z and Q
表 1 348例全灌浆套筒试验数据
Table 1 Test data of 348 cases of grouted sleeves
文献来源 L/mm n D/T G Z Q fc/MPa t/mm Ld /mm d/mm fu/MPa F/kN M [7] 195~215 1~4 10.05~12.67 0 0 0 63.0~91.8 8.0~11.0 3.50~9.10 16~20 596.0~618.0 82~320 0(62),1(31) [24] 268~396 4~5 11.11~12.00 0 0 0 72.7 9.5~11.0 7.00~7.50 16~25 591.0~637.0 119~316 0(2),1(7) [25] 380~480 5~6 12.75 0 0 0 80.2 9.0 4.00~8.00 25 585.0 226~261 0(6) [26] 280~570 3~5 7.67~11.00 0 0 0 123.8 10.0~14.5 8.00 14~25 725.0~839.0 113~415 1(14) [27] 280~460 3~5 7.67~9.67 0~2 0~0.400 0 123.8 10.0~10.5 8.00 14~25 598.0~616.0 92~304 0(2),1(20) [28] 190~440 3~8 10.80 0 0 0 95.0 9.5 3.00~7.00 25 650.0 190~265 0(3),1(3) [29] 190~440 3~9 10.80~12.00 0 0 0 75.0~115.0 9.5~12.5 3.00~8.00 25 608.0 181~264 0(16),1(17) [30] 310~450 4~5 11.14 0 0 0 83.9 12.5 6.00~8.00 22 846.0 278~308 0(9),1(3) [31] 280~460 3~5 7.67~9.67 0~2 0~0.400 0 123.8 10.0~10.5 8.00 14~25 598.0~616.0 92~304 0(7),1(14) [32] 370 4 8.67 0 0 0 75.2~125.9 10.0 8.00 20 615.0 91~304 0(3),1(9) [33] 280 3 12.67 0~3 0~0.350 0 88.1 9.0 8.50 14 608.0 80~92 0(5),1(2) [34] 368 3 9.78 0~1 0~0.375 0 85.0 7.5 8.00 20 599.0 171~189 0(1),1(3) [35] 640 6 8.40 0~3 0~0.375 0 89.0 8.0 8.00 40 605.0 769~964 0(7),1(4) [36] 330 3 11.14 0 0 0 73.1~83.7 8.0 8.00 16 623.0 80~118 0(5),1(3) [37] 310~330 3 11.50~12.00 0 0 0 80.0 12.0 5.50~8.00 16 618.0 126~131 0(4),1(4) [38] 650 7 7.00 0~2 0~0.300 0 127.0 11.5 10.00 32 621.0 460~481 0(2),1(2) [39] 275 3 7.67 0~3 0~0.375 0.05~0.14 74.3 10.0 4.00~8.00 14 582.0 68~90 0(7),1(5) [40] 310 4 8.00 0~1 0~0.500 0 30.9~89.8 10.0 8.00 16 655.0 77~133 0(1),1(5) [41] 370 4 11.80 0 0 0 58.9~73.6 11.0~17.0 4.20~6.20 19~25 593.0 130~288 0(14),1(1) [42] 348~460 2 9.58~12.00 0, 4 0 0~0.50 88.5 11.0 8.00 18~25 575.0~620.0 79~288 0(9),1(7) [43] 280 4 7.66 0, 4 0 0.18~0.30 85.0 10.0 8.00 14 630.0 18~97 0(4),1(1) [44] 380 4 11.33 0, 4 0 0~0.50 122.2 11.0 9.25 20 569.2 23~184 0(12),1(6) [45] 300 1 12.75 2, 4 0~0.300 0~0.30 128.8 10.5 7.00 20 625.0 154~204 0(3),1(3) 注：L为套筒长度；套筒径厚比D/T为套筒外径D与壁厚T之比；套筒肋个数n为套筒长度一半的环肋数量；G为灌浆缺陷类型，其中0为无灌浆缺陷，1为竖向灌浆端部黏结缺陷(简称端部缺陷)，2为竖向灌浆中部黏结缺陷(简称中部缺陷)，3为竖向灌浆均布黏结缺陷(简称均布缺陷)，4为水平灌浆质量缺陷(简称水平缺陷)；竖向灌浆黏结缺陷率Z为黏结缺陷长度与钢筋锚固长度之比；水平灌浆质量缺陷率Q为水平灌浆时灌浆料缺失质量与满灌的灌浆料质量之比；fc为灌浆料抗压强度；灌浆料厚度t=(D−2T−d)/2；Ld为钢筋锚固长度；d为钢筋直径；fu为钢筋材性试验得到的钢筋极限抗拉强度；F为套筒灌浆连接的钢筋受拉承载力；M为失效模式，其中0代表钢筋拔出，1代表钢筋拉断，括号里数字代表试件数量。 
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表 3 机器学习回归算法预测结果
Table 3 Prediction result of machine learning regression algorithm
算法 决定系数R2 平均绝对值误差MAE SVM 0.82 44.03 KNN 0.97 17.62 DT 0.98 11.60 RF 0.99 9.76 XGBoost 0.98 13.59 LightGBM 0.87 35.99
下载: 导出CSV
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