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Article(id=1149738725455872825, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149738718707237637, articleNumber=1003-3033(2024)08-0069-09, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2024.08.1360, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1708444800000, receivedDateStr=2024-02-21, revisedDate=1716825600000, revisedDateStr=2024-05-28, acceptedDate=null, acceptedDateStr=null, onlineDate=1752048673260, onlineDateStr=2025-07-09, pubDate=1724774400000, pubDateStr=2024-08-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752048673260, onlineIssueDateStr=2025-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752048673260, creator=13701087609, updateTime=1752048673260, updator=13701087609, issue=Issue{id=1149738718707237637, tenantId=1146029695717560320, journalId=1146031787341344770, year='2024', volume='34', issue='8', pageStart='1', pageEnd='252', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752048671651, creator=13701087609, updateTime=1756376992009, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1167893010143519453, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149738718707237637, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1167893010143519454, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149738718707237637, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=69, endPage=77, ext={EN=ArticleExt(id=1149738725648810815, articleId=1149738725455872825, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Shield attitude prediction and optimization based on CatBoost-NSGA-III algorithm, columnId=1149733269173878863, journalTitle=China Safety Science Journal, columnName=Safety engineering technology, runingTitle=null, highlight=null, articleAbstract=

To solve the problems such as forward tilt deformation，serpentine shape，axis deviation and correction during shield tunneling，which affected the safety and efficiency of shield construction，a multi-objective optimization method of shield attitude combining CatBoost and NSGA-Ⅲ was proposed. Taking Guiyang Metro as the background，22 influencing factors were selected as input parameters，and the nonlinear mapping function relationship between input parameters and shield attitude was established by using CatBoost algorithm. The importance of input parameters was evaluated by random forest (RF) algorithm. A CatBoost-NSGA-Ⅲ multi-objective optimization model was established to minimize the absolute value of the shield attitude，and the applicability and effectiveness of the proposed method were verified by a case study. The results show that the prediction model obtained by using CatBoost algorithm to train engineering measured data has high accuracy，and the R2 range of 5 shield attitude targets is 0.916-0.943. By using the CatBoost-NSGA-Ⅲ multi-objective optimization method，the attitude of the shield can be optimized significantly，and the average value of the overall improvement is 53.34%.

, correspAuthors=Yu LEI, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Xianguo WU, Jun LIU, Yuan CAO, Yu LEI, Shifan LI, Yawei QIN), CN=ArticleExt(id=1149738731097211871, articleId=1149738725455872825, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于CatBoost-NSGA-III算法的盾构姿态预测与优化, columnId=1149733269727526997, journalTitle=中国安全科学学报, columnName=安全工程技术, runingTitle=null, highlight=null, articleAbstract=

为解决盾构掘进过程中因盾构前倾变形、蛇形、轴线偏离及纠偏等影响施工安全性与高效性的问题，提出一种将类别型特征梯度提升(CatBoost)与第三代非支配排序遗传算法(NSGA-Ⅲ)相结合的盾构姿态多目标优化方法；以贵阳地铁为例，选取22个影响因素作为输入参数，利用CatBoost算法建立输入参数与盾构姿态之间的非线性映射函数关系，采用随机森林(RF)算法评价输入参数的重要性；以盾构姿态绝对值最小化为目标，构建CatBoost-NSGA-Ⅲ多目标优化模型，并通过案例分析验证所提方法的适用性和有效性。结果表明：采用CatBoost算法训练工程实测数据得到的预测模型具有较高的精度，5个盾构姿态目标的R2范围为0.916~0.943;所研发的CatBoost-NSGA-Ⅲ盾构姿态多目标优化方法，可使盾构姿态得到显著优化，整体改进的平均值为53.34%。

, correspAuthors=雷宇, authorNote=null, correspAuthorsNote=
** 雷宇(2001—)，男，湖南益阳人，硕士研究生，研究方向为土木工程建造与管理。E-mail：。
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吴贤国 (1964—)，女，湖北武汉人，博士，教授，主要从事数字工程集成建设关键技术及应用、隧道工程施工与运营安全监控等方面的研究。E-mail：。

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吴贤国 (1964—)，女，湖北武汉人，博士，教授，主要从事数字工程集成建设关键技术及应用、隧道工程施工与运营安全监控等方面的研究。E-mail：。

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吴贤国 (1964—)，女，湖北武汉人，博士，教授，主要从事数字工程集成建设关键技术及应用、隧道工程施工与运营安全监控等方面的研究。E-mail：。

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China Safety Science Journal, 2022, 32(8): 45-51., articleTitle=Multi-objective optimization of surface settlement safety control during shield construction based on RF-NSGA-Ⅱ, refAbstract=null)], funds=[Fund(id=1167877561053880762, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, awardId=51378235, language=CN, fundingSource=国家自然科学基金(51378235), fundOrder=null, country=null), Fund(id=1167877561167126971, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, awardId=71571078, language=CN, fundingSource=国家自然科学基金(71571078), fundOrder=null, country=null), Fund(id=1167877561251013052, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, awardId=51308240, language=CN, fundingSource=国家自然科学基金(51308240), fundOrder=null, country=null), Fund(id=1167877561355870653, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, awardId=2016YFC0800208, language=CN, fundingSource=国家重点研发计划项目(2016YFC0800208), fundOrder=null, country=null), Fund(id=1167877561422979518, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, awardId=KJQN202103801, language=CN, fundingSource=2021年度市教委科学技术研究计划青年项目(KJQN202103801), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1167877557782323577, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, xref=1, ext=[AuthorCompanyExt(id=1167877557799100794, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, companyId=1167877557782323577, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1 School of Civil and Hydraulic Engineering，Huazhong University of Science and Technology，Wuhan Hubei 430074，China), AuthorCompanyExt(id=1167877557807489403, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, companyId=1167877557782323577, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1 华中科技大学 土木与水利工程学院，湖北 武汉 430074)]), AuthorCompany(id=1167877557857821052, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, xref=2, ext=[AuthorCompanyExt(id=1167877557866209661, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, companyId=1167877557857821052, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2 Wuhan Huazhong University of Science and Technology Test Technology Co.，Ltd.，Wuhan Hubei 430074，China), AuthorCompanyExt(id=1167877557870403966, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, companyId=1167877557857821052, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2 武汉华中科大检测科技有限公司，湖北 武汉 430074)])], figs=[ArticleFig(id=1167877559762035112, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, language=EN, label=Fig.1, caption=Input parameter importance ranking, figureFileSmall=Lo3Q3snt8kUj+LJ9quPsYQ==, figureFileBig=iEaOZ4j8sl0S+U+KhGQu6w==, tableContent=null), ArticleFig(id=1167877559883669929, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, language=CN, label=图1, caption=输入参数重要性排序, figureFileSmall=Lo3Q3snt8kUj+LJ9quPsYQ==, figureFileBig=iEaOZ4j8sl0S+U+KhGQu6w==, tableContent=null), ArticleFig(id=1167877559942390186, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, language=EN, label=Fig.2, caption=Optimization results of 3 scenarios, figureFileSmall=+326XrOTf5gWMWE0Bj3OKA==, figureFileBig=HsKcyldslni3vxIeuwSQxQ==, tableContent=null), ArticleFig(id=1167877560064025003, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, language=CN, label=图2, caption=3个场景的优化结果, figureFileSmall=+326XrOTf5gWMWE0Bj3OKA==, figureFileBig=HsKcyldslni3vxIeuwSQxQ==, tableContent=null), ArticleFig(id=1167877560114356652, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, language=EN, label=Table 1, caption=

Statistics of preprocessed input and output parameters

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参数
类型		 变量 数据
最小值 最大值 平均值
输入 土仓压力-上 x1/MPa 0.07 0.21 0.14
土仓压力-下x2/MPa 0.04 0.53 0.22
土仓压力-左 x3/MPa 0.05 0.29 0.15
土仓压力-右 x4/MPa 0.03 0.28 0.15
螺旋机转速 x5/
(r·min-1)		 0.91 22.9 12.02
千斤顶推力-上 x6/kN 3.69 209.02 77.77
千斤顶推力-下 x7/kN 60.19 212.56 123.24
千斤顶推力-左 x8/kN 28.84 269.35 132.91
千斤顶推力-右 x9/kN 42.20 235.88 109.87
刀盘转速 x10/(r·min-1) 1.21 2.04 1.66
刀盘扭矩 x11/(kN·m) 719.91 2 998.29 2 058.51
掘进速度 x12/
(mm·min-1)		 7.53 70.29 40.09
注浆压力 x13/MPa 0.12 0.31 0.25
总推力 x14/kN 5 441.28 19 086.34 12 249.35
铰接行程 x15/m 0.87 38.40 13.71
铰接偏差-水平 x16/mm 1.34 86.68 24.85
铰接偏差-垂直 x17/mm 0 59.85 21.15
盾尾间隙-上 x18/mm 29.03 79.03 63.81
盾尾间隙-下 x19/mm 33.81 83.7 67.81
盾尾间隙-左 x20/mm 27.03 83.19 67.37
盾尾间隙-右 x21/mm 11.49 189.87 63.33
切口行程 x22/m 1.4 1.54 1.5
泡沫剂 x23/L 25.19 157.79 47.96
输出 切口水平方向位移
f1/mm		 -39.53 32.03 -0.58
切口垂直方向位移
f2/mm		 -49.47 48.61 -0.32
尾部水平方向位移
f3/mm		 -52.86 42.36 -25.78
尾部垂直方向位移
f4/mm		 -46.66 51.94 16.15
俯仰角 f5/(°) -27.11 26.01 -1.45
), ArticleFig(id=1167877560177271213, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, language=CN, label=表1, caption=

预处理后的输入和输出参数的统计信息

, figureFileSmall=null, figureFileBig=null, tableContent=
参数
类型		 变量 数据
最小值 最大值 平均值
输入 土仓压力-上 x1/MPa 0.07 0.21 0.14
土仓压力-下x2/MPa 0.04 0.53 0.22
土仓压力-左 x3/MPa 0.05 0.29 0.15
土仓压力-右 x4/MPa 0.03 0.28 0.15
螺旋机转速 x5/
(r·min-1)		 0.91 22.9 12.02
千斤顶推力-上 x6/kN 3.69 209.02 77.77
千斤顶推力-下 x7/kN 60.19 212.56 123.24
千斤顶推力-左 x8/kN 28.84 269.35 132.91
千斤顶推力-右 x9/kN 42.20 235.88 109.87
刀盘转速 x10/(r·min-1) 1.21 2.04 1.66
刀盘扭矩 x11/(kN·m) 719.91 2 998.29 2 058.51
掘进速度 x12/
(mm·min-1)		 7.53 70.29 40.09
注浆压力 x13/MPa 0.12 0.31 0.25
总推力 x14/kN 5 441.28 19 086.34 12 249.35
铰接行程 x15/m 0.87 38.40 13.71
铰接偏差-水平 x16/mm 1.34 86.68 24.85
铰接偏差-垂直 x17/mm 0 59.85 21.15
盾尾间隙-上 x18/mm 29.03 79.03 63.81
盾尾间隙-下 x19/mm 33.81 83.7 67.81
盾尾间隙-左 x20/mm 27.03 83.19 67.37
盾尾间隙-右 x21/mm 11.49 189.87 63.33
切口行程 x22/m 1.4 1.54 1.5
泡沫剂 x23/L 25.19 157.79 47.96
输出 切口水平方向位移
f1/mm		 -39.53 32.03 -0.58
切口垂直方向位移
f2/mm		 -49.47 48.61 -0.32
尾部水平方向位移
f3/mm		 -52.86 42.36 -25.78
尾部垂直方向位移
f4/mm		 -46.66 51.94 16.15
俯仰角 f5/(°) -27.11 26.01 -1.45
), ArticleFig(id=1167877560235991470, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, language=EN, label=Table 2, caption=

Hyperparameter optimization results of CatBoost algorithm under different shield attitude prediction targets

, figureFileSmall=null, figureFileBig=null, tableContent=
预测目标 l2_leaf_reg learning_rate max_depth
f1/mm 9 0.026 57 8
f2/mm 7 0.399 37 8
f3/mm 4 0.167 00 6
f4/mm 6 0.034 56 8
f5/(°) 3 0.065 53 8
), ArticleFig(id=1167877560298906031, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, language=CN, label=表2, caption=

CatBoost算法在不同盾构姿态预测目标下超参数优化结果

, figureFileSmall=null, figureFileBig=null, tableContent=
预测目标 l2_leaf_reg learning_rate max_depth
f1/mm 9 0.026 57 8
f2/mm 7 0.399 37 8
f3/mm 4 0.167 00 6
f4/mm 6 0.034 56 8
f5/(°) 3 0.065 53 8
), ArticleFig(id=1167877560357626288, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, language=EN, label=Table 3, caption=

Five-folds cross verification results of five shield attitude targets

, figureFileSmall=null, figureFileBig=null, tableContent=
折叠
序号		 f1/mm f2/mm f3/mm f4/mm f5/(°)
R2 RMSE MAE R2 RMSE MAE R2 RMSE MAE R2 RMSE MAE R2 RMSE MAE
1 0.934 3.741 2.922 0.943 3.418 2.705 0.917 3.594 2.827 0.915 4.372 3.429 0.909 0.986 0.833
2 0.943 3.456 2.717 0.949 3.247 2.575 0.923 3.466 2.739 0.920 4.217 3.291 0.920 0.922 0.756
3 0.949 3.292 2.611 0.950 3.216 2.555 0.925 3.431 2.721 0.927 4.037 3.145 0.915 0.953 0.781
4 0.925 3.976 3.198 0.939 3.546 2.851 0.914 3.674 2.876 0.911 4.473 3.555 0.919 0.930 0.766
5 0.922 4.069 3.297 0.933 3.715 3.051 0.912 3.576 2.906 0.912 4.442 3.521 0.920 0.926 0.760
平均值 0.934 3.707 2.950 0.943 3.428 2.748 0.918 3.576 2.814 0.917 4.308 3.388 0.916 0.943 0.779
), ArticleFig(id=1167877560428929457, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, language=CN, label=表3, caption=

5个盾构姿态目标的五折交叉验证结果

, figureFileSmall=null, figureFileBig=null, tableContent=
折叠
序号		 f1/mm f2/mm f3/mm f4/mm f5/(°)
R2 RMSE MAE R2 RMSE MAE R2 RMSE MAE R2 RMSE MAE R2 RMSE MAE
1 0.934 3.741 2.922 0.943 3.418 2.705 0.917 3.594 2.827 0.915 4.372 3.429 0.909 0.986 0.833
2 0.943 3.456 2.717 0.949 3.247 2.575 0.923 3.466 2.739 0.920 4.217 3.291 0.920 0.922 0.756
3 0.949 3.292 2.611 0.950 3.216 2.555 0.925 3.431 2.721 0.927 4.037 3.145 0.915 0.953 0.781
4 0.925 3.976 3.198 0.939 3.546 2.851 0.914 3.674 2.876 0.911 4.473 3.555 0.919 0.930 0.766
5 0.922 4.069 3.297 0.933 3.715 3.051 0.912 3.576 2.906 0.912 4.442 3.521 0.920 0.926 0.760
平均值 0.934 3.707 2.950 0.943 3.428 2.748 0.918 3.576 2.814 0.917 4.308 3.388 0.916 0.943 0.779
), ArticleFig(id=1167877560487649714, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, language=EN, label=Table 4, caption=

Prediction results of five methods

, figureFileSmall=null, figureFileBig=null, tableContent=
算法 f1/mm f2/mm f3/mm f4/mm f5/(°)
R2 RMSE MAE R2 RMSE MAE R2 RMSE MAE R2 RMSE MAE R2 RMSE MAE
CatBoost 0.934 3.707 2.950 0.943 3.428 2.748 0.918 3.576 2.814 0.917 4.308 3.388 0.916 0.943 0.779
XGBoost 0.918 4.155 3.321 0.928 3.847 3.257 0.903 3.888 3.514 0.903 4.667 3.757 0.901 1.024 0.820
LightGBM 0.926 3.933 3.047 0.937 3.582 2.872 0.913 3.683 3.092 0.913 4.405 3.530 0.911 0.972 0.794
GRU 0.922 4.047 3.154 0.933 3.699 2.940 0.908 3.787 3.196 0.908 4.546 3.659 0.907 0.994 0.796
LSTM 0.920 4.095 3.228 0.931 3.773 3.094 0.907 3.813 3.263 0.906 4.583 3.697 0.903 1.014 0.813
), ArticleFig(id=1167877560563147187, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, language=CN, label=表4, caption=

5种方法的预测结果

, figureFileSmall=null, figureFileBig=null, tableContent=
算法 f1/mm f2/mm f3/mm f4/mm f5/(°)
R2 RMSE MAE R2 RMSE MAE R2 RMSE MAE R2 RMSE MAE R2 RMSE MAE
CatBoost 0.934 3.707 2.950 0.943 3.428 2.748 0.918 3.576 2.814 0.917 4.308 3.388 0.916 0.943 0.779
XGBoost 0.918 4.155 3.321 0.928 3.847 3.257 0.903 3.888 3.514 0.903 4.667 3.757 0.901 1.024 0.820
LightGBM 0.926 3.933 3.047 0.937 3.582 2.872 0.913 3.683 3.092 0.913 4.405 3.530 0.911 0.972 0.794
GRU 0.922 4.047 3.154 0.933 3.699 2.940 0.908 3.787 3.196 0.908 4.546 3.659 0.907 0.994 0.796
LSTM 0.920 4.095 3.228 0.931 3.773 3.094 0.907 3.813 3.263 0.906 4.583 3.697 0.903 1.014 0.813
), ArticleFig(id=1167877560638644660, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, language=EN, label=Table 5, caption=

Adjustment parameters in each scenario

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场景编号 调整参数
1 x1~x4，x6~x9
2 x1~x4，x6~x9，x12
3 x1~x4，x6~x9，x12，x11
), ArticleFig(id=1167877560726725045, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, language=CN, label=表5, caption=

各场景的调整参数

, figureFileSmall=null, figureFileBig=null, tableContent=
场景编号 调整参数
1 x1~x4，x6~x9
2 x1~x4，x6~x9，x12
3 x1~x4，x6~x9，x12，x11
), ArticleFig(id=1167877560772862390, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, language=EN, label=Table 6, caption=

Range of shield construction parameters

, figureFileSmall=null, figureFileBig=null, tableContent=
施工参数 取值范围 施工参数 取值范围
x1/MPa 0~0.3 x7/kN 0~300
x2/MPa 0~0.3 x8/kN 0~300
x3/MPa 0~0.3 x9/kN 0~300
x4/MPa 0~0.3 x11/(kN·m) 0~3 000
x6/kN 0~300 x12/(mm·min-1) 0~90
), ArticleFig(id=1167877560827388343, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, language=CN, label=表6, caption=

盾构施工参数范围

, figureFileSmall=null, figureFileBig=null, tableContent=
施工参数 取值范围 施工参数 取值范围
x1/MPa 0~0.3 x7/kN 0~300
x2/MPa 0~0.3 x8/kN 0~300
x3/MPa 0~0.3 x9/kN 0~300
x4/MPa 0~0.3 x11/(kN·m) 0~3 000
x6/kN 0~300 x12/(mm·min-1) 0~90
), ArticleFig(id=1167877560886108600, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, language=EN, label=Table 7, caption=

Improvement degree of shield attitude in different scenarios

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盾构姿态目标 场景
1 2 3 平均值
f1/mm 平均值 14.83
(22.85%)		 13.39
(30.33%)		 12.80
(33.40%)		 13.67
(28.86%)
f2/mm 平均值 17.20
(30.94%)		 16.79
(32.55%)		 16.41
(34.11%)		 16.80
(32.53%)
f3/mm 平均值 29.13
(18.23%)		 28.41
(20.25%)		 27.13
(23.85%)		 28.22
(20.78%)
f4/mm 平均值 25.66
(12.83%)		 25.22
(14.35%)		 24.72
(16.03%)		 25.20
(14.40%)
f5/
(mm·m-1)		 平均值 4.66
(12.27%)		 4.33
(18.44%)		 4.11
(22.59%)		 4.37
(17.77%)
Pi/% 平均值 46.11 54.18 60.12 53.35
f1/mm 最优解 15.19
(20.94%)		 12.35
(35.74%)		 13.47
(29.92%)		 13.67
(28.87%)
f2/mm 最优解 10.08
(59.51%)		 11.31
(54.58%)		 12.12
(51.31%)		 11.17
(55.13%)
f3/mm 最优解 24.91
(30.07%)		 23.51
(34.01%)		 20.01
(43.83%)		 22.81
(35.97%)
f4/mm 最优解 21.51
(26.93%)		 24.80
(15.75%)		 23.05
(21.71%)		 23.12
(21.46%)
f5/
(mm·m-1)		 最优解 4.97
(6.43%)		 4.17
(21.51%)		 3.83
(27.87%)		 4.32
(18.60%)
Pi/% 最优解 75.17 78.25 81.84 77.29
), ArticleFig(id=1167877560940634553, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738725455872825, language=CN, label=表7, caption=

盾构姿态在不同场景下的改进程度

, figureFileSmall=null, figureFileBig=null, tableContent=
盾构姿态目标 场景
1 2 3 平均值
f1/mm 平均值 14.83
(22.85%)		 13.39
(30.33%)		 12.80
(33.40%)		 13.67
(28.86%)
f2/mm 平均值 17.20
(30.94%)		 16.79
(32.55%)		 16.41
(34.11%)		 16.80
(32.53%)
f3/mm 平均值 29.13
(18.23%)		 28.41
(20.25%)		 27.13
(23.85%)		 28.22
(20.78%)
f4/mm 平均值 25.66
(12.83%)		 25.22
(14.35%)		 24.72
(16.03%)		 25.20
(14.40%)
f5/
(mm·m-1)		 平均值 4.66
(12.27%)		 4.33
(18.44%)		 4.11
(22.59%)		 4.37
(17.77%)
Pi/% 平均值 46.11 54.18 60.12 53.35
f1/mm 最优解 15.19
(20.94%)		 12.35
(35.74%)		 13.47
(29.92%)		 13.67
(28.87%)
f2/mm 最优解 10.08
(59.51%)		 11.31
(54.58%)		 12.12
(51.31%)		 11.17
(55.13%)
f3/mm 最优解 24.91
(30.07%)		 23.51
(34.01%)		 20.01
(43.83%)		 22.81
(35.97%)
f4/mm 最优解 21.51
(26.93%)		 24.80
(15.75%)		 23.05
(21.71%)		 23.12
(21.46%)
f5/
(mm·m-1)		 最优解 4.97
(6.43%)		 4.17
(21.51%)		 3.83
(27.87%)		 4.32
(18.60%)
Pi/% 最优解 75.17 78.25 81.84 77.29
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基于CatBoost-NSGA-III算法的盾构姿态预测与优化
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吴贤国 1 , 刘俊 1 , 曹源 1 , 雷宇 1, ** , 李士范 1 , 覃亚伟 1, 2
中国安全科学学报 | 安全工程技术 2024,34(8): 69-77
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中国安全科学学报 | 安全工程技术 2024, 34(8): 69-77
基于CatBoost-NSGA-III算法的盾构姿态预测与优化
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吴贤国1 , 刘俊1, 曹源1, 雷宇1, ** , 李士范1, 覃亚伟1, 2
作者信息
  • 1 华中科技大学 土木与水利工程学院，湖北 武汉 430074
  • 2 武汉华中科大检测科技有限公司，湖北 武汉 430074

    • 吴贤国 (1964—)，女，湖北武汉人，博士，教授，主要从事数字工程集成建设关键技术及应用、隧道工程施工与运营安全监控等方面的研究。E-mail：。

通讯作者:

** 雷宇(2001—)，男，湖南益阳人，硕士研究生，研究方向为土木工程建造与管理。E-mail：。
Shield attitude prediction and optimization based on CatBoost-NSGA-III algorithm
Xianguo WU1 , Jun LIU1, Yuan CAO1, Yu LEI1, ** , Shifan LI1, Yawei QIN1, 2
Affiliations
  • 1 School of Civil and Hydraulic Engineering，Huazhong University of Science and Technology，Wuhan Hubei 430074，China
  • 2 Wuhan Huazhong University of Science and Technology Test Technology Co.，Ltd.，Wuhan Hubei 430074，China
出版时间: 2024-08-28 doi: 10.16265/j.cnki.issn1003-3033.2024.08.1360
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摘要
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为解决盾构掘进过程中因盾构前倾变形、蛇形、轴线偏离及纠偏等影响施工安全性与高效性的问题，提出一种将类别型特征梯度提升(CatBoost)与第三代非支配排序遗传算法(NSGA-Ⅲ)相结合的盾构姿态多目标优化方法；以贵阳地铁为例，选取22个影响因素作为输入参数，利用CatBoost算法建立输入参数与盾构姿态之间的非线性映射函数关系，采用随机森林(RF)算法评价输入参数的重要性；以盾构姿态绝对值最小化为目标，构建CatBoost-NSGA-Ⅲ多目标优化模型，并通过案例分析验证所提方法的适用性和有效性。结果表明：采用CatBoost算法训练工程实测数据得到的预测模型具有较高的精度，5个盾构姿态目标的R2范围为0.916~0.943;所研发的CatBoost-NSGA-Ⅲ盾构姿态多目标优化方法，可使盾构姿态得到显著优化，整体改进的平均值为53.34%。

类别型特征梯度提升(CatBoost)  /  第三代非支配排序遗传算法(NSGA-Ⅲ)  /  盾构姿态  /  多目标优化  /  重要性排序

To solve the problems such as forward tilt deformation，serpentine shape，axis deviation and correction during shield tunneling，which affected the safety and efficiency of shield construction，a multi-objective optimization method of shield attitude combining CatBoost and NSGA-Ⅲ was proposed. Taking Guiyang Metro as the background，22 influencing factors were selected as input parameters，and the nonlinear mapping function relationship between input parameters and shield attitude was established by using CatBoost algorithm. The importance of input parameters was evaluated by random forest (RF) algorithm. A CatBoost-NSGA-Ⅲ multi-objective optimization model was established to minimize the absolute value of the shield attitude，and the applicability and effectiveness of the proposed method were verified by a case study. The results show that the prediction model obtained by using CatBoost algorithm to train engineering measured data has high accuracy，and the R2 range of 5 shield attitude targets is 0.916-0.943. By using the CatBoost-NSGA-Ⅲ multi-objective optimization method，the attitude of the shield can be optimized significantly，and the average value of the overall improvement is 53.34%.

categorical boosting (CatBoost)  /  non-dominated sorting genetic algorithm-Ⅲ (NSGA-Ⅲ)  /  shield attitude  /  multi-objective optimization  /  importance ranking
吴贤国, 刘俊, 曹源, 雷宇, 李士范, 覃亚伟. 基于CatBoost-NSGA-III算法的盾构姿态预测与优化. 中国安全科学学报, 2024 , 34 (8) : 69 -77 . DOI: 10.16265/j.cnki.issn1003-3033.2024.08.1360
Xianguo WU, Jun LIU, Yuan CAO, Yu LEI, Shifan LI, Yawei QIN. Shield attitude prediction and optimization based on CatBoost-NSGA-III algorithm[J]. China Safety Science Journal, 2024 , 34 (8) : 69 -77 . DOI: 10.16265/j.cnki.issn1003-3033.2024.08.1360
正文
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0 引言
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盾构施工环境复杂多变，为确保按照既定路线进行推进，控制开挖隧道变形，保证盾构施工过程的安全，需要及时调整盾构姿态。因此，控制盾构姿态是隧道盾构施工的重要问题，应重点关注。
诸多研究者采用经验公式、理论模型和数值分析方法，预测盾构姿态和研究控制机制。在经验公式法上，沈翔等[1]基于地基反力曲线及改进的太沙基松动土压力计算方法，得到盾构俯仰角理论预测公式；在理论模型法方面，黄威等[2]综合考虑地层参数、油缸推进力、地层反力、现场施工措施，将盾构姿态引入盾构水平受力平衡分析中，建立关于盾构水平姿态的理论模型；在数值模拟法上，苏栋等[3]建立大直径盾构掘进数值仿真分析模型，通过改变推进系统荷载分布来施加偏转力矩，研究偏转力矩与盾构机俯仰角之间的关系以及盾构机姿态变化对地层变形的影响。上述研究方法虽然在应用时简单、快捷，但是，由于模型过于简化，得到预测结果需要作出很多假设，并存在考虑因素有限、模型预测精度低等缺点。
在大量监测数据中寻找隐藏的规律方面，机器学习方法具有巨大优势，已被广泛应用于盾构姿态预测和控制上。在盾构姿态预测方面，夏汉庸等[4]采用极端梯度提升树(Extreme Gradient Boosting，XGBoost)算法拟合训练数据集，得到盾构姿态参数回归模型；曾铁梅等[5]提出一种基于光梯度助推机(Light Gradient Boosting Machine，LightGBM)模型的盾构姿态智能预测方法；吴坚等[6]提出基于长短期记忆神经网络(Long Short Term Memory，LSTM)和门控循环单元(Gated Recurrent Unit，GRU)模型的盾构水平姿态和盾构总推进力预测方法。上述研究通过机器学习算法预测分析了盾构姿态，但对盾构姿态的优化控制研究较少，并且优化目标相互矛盾，很难同时实现最优。
鉴于此，笔者拟结合类别型特征梯度提升(Categorical Boosting，CatBoost)和第三代非支配排序遗传算法(Non-dominated Sorting Genetic Algorithm-Ⅲ，NSGA-Ⅲ)，提出一种基于CatBoost-NSGA-Ⅲ盾构姿态多目标优化方法。确定最佳盾构隧道施工参数方案，控制盾构姿态，以期为实际盾构施工控制提供参考。
1 盾构姿态预测与多目标优化方法
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1.1 CatBoost算法
CatBoost是由YANDEX提出的新型梯度提升算法，可解决梯度偏差和预测偏移问题，减少过度拟合，提高算法的准确率和泛化能力，不需要过多训练数据，即可获得较高的模型精度，并且不需要对超参数进行调优，避免过拟合的发生[7]。
CatBoost是梯度提升的一种应用，基于二元决策树进行预测。决策树是一个由特征空间的递归分割建立的模型根据一些分割属性a的值，将特征空间划分为几个互不相干的区域(树节点)来建立模型。这个属性通常是二进制变量，用于识别某个特征mk超过某个阈值t，即a=I{mk>t}，其中，mk是数字或二进制特征，在后一种情况下t=0.5。每个最终区域(树的叶子)都被分配到一个值，这是对该区域内的反应y的估计，用于回归任务，或是回归任务的估计。在回归任务中，该值是对区域内反应y的估计，在分类问题中，则是预测的类标签。这样，一棵决策树d(m)被写成：
d ( m ) = ∑ j = 1 J b j I { m ∈ R j }
式中：J为叶子节点的数量；Rj为决策树的第j个叶子节点对应的区域；bj为分配给区域Rj的预测值；I{m∈Rj}为指示函数，当m属于区域Rj时取值为1，否则为0。
1.2 NSGA-III算法
NSGA-III算法作为NSGA-II算法的改进版，是一种用于超多维目标优化问题的进化算法[8]。NSGA-Ⅲ的基本框架与NSGA-II一致，最大的区别是它通过提供和自适应更新一些分布良好的参考点来帮助维持种群成员之间的多样性。具体来说，DEB等[8]设计了基于参考点的精英保留策略，保留那些非支配且接近参考点的种群个体，提高了优良个体搜索率且维持了种群的多样性，使算法在进行超多维优化时具有良好的搜索Pareto前沿的能力。NSGA-III算法的核心包括参考点的设置、种群的自适应标准化、关联操作和个体保留操作。
NSGA-III构造的参考点在一个(M-1)维的超平面上，M是目标空间的维度，即优化目标的个数。如果将每个目标划分为H份，那么参考点的数量为 C M + H - 1 H。接着NSGA-III引入目标函数的归一化，首先，将每个目标函数最小值组合起来设置成理想点；然后，将种群所有个体的目标值都减去理想点；最后，通过成就标量函数(Achievement Scalarizing Function，ASF)运算找到每个维度的极值点。
将每个维度的ASF最小值定义为极值点，获得M极值点集合，利用此集合构造(M-1)维与每个坐标轴相较的超平面。该超平面与每个坐标轴的截距记为ei，目标函数 g i n(x)可通过下式被归一化：
g i n ( P ) = g ' i ( p ) e i - Z m i n i = g i ( p ) - Z m i n i e i - Z m i n i
式中：p为种群个体；gi(p)为p在第i目标上的取值；g'i (p) 为gi(p)减去在该目标理想值后的取值； Z i m i n为所有目标坐标的的最小值；ei为超平面与每个坐标轴的截距。
创建种群个体与参考点之间的映射关系。连接超平面内原点与各参考点，构建一条条射线，计算种群个体到射线之间的垂直距离，距离最小的个体互相构成关联参考点。计算每个参考点相关联的个体数量，根据数量选择K个体进入最后一前沿种群。
2 工程案例概况
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以贵阳市轨道交通3号线为例，其四方河路站—皂角井站区间为贵阳市轨道交通3号线7标部分工程。该区间隧道的里程为YDK24+511.010—YDK25+628.283，全长1 117.209m。该区间选用复合式土压平衡盾构机施工，开挖直径为6 470mm。隧道衬砌采用环宽为1.5m的钢筋混凝土管片，内径为5 500mm，厚度为350mm。区间隧道最大纵坡为25.144‰，埋深为12.69~21.72m。盾构机配有数百个传感器，实时记录机器运行状态，数据采集系统收集和记录盾构运行参数的所有数据。选取大约200个环的数据，主要穿越地层为中风化白云岩，抗压强度45~78MPa，土壤黏聚力约为200 kPa，内摩擦角约为30°。该区间白云岩局部隔水又局部含水从而形成复杂水文地质。在这样的局部含水和复合地层中，盾构机的掘进变得异常复杂，容易出现上升、偏离和蛇行等现象，严重时甚至可能导致地面塌陷，引发安全问题。因此，对控制盾构机的姿态提出很高的要求。
3 盾构姿态预测与优化
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3.1 基于CatBoost算法的盾构姿态预测
3.1.1 输入参数选取及数据预处理
根据盾构机原理、专家经验和大量工程实践可知[9]:在盾构施工中，土仓压力、千斤顶推力、刀盘扭矩、刀盘转速以及注浆压力等主要盾构参数都会对盾构机在掘进中的姿态产生影响。土仓压力的不平衡是造成地下隧道工程施工中地面变形和严重事故的直接因素，因此，土仓压力是控制盾构姿态的关键因素。千斤顶推力作为盾构机运行的重要参数，随着千斤顶推力的增大，盾构机对土体的扰动也随之增加，因此，盾构机施工时的千斤顶推力对姿态控制至关重要。刀盘转速和刀盘扭矩决定了盾构对地层的扰动程度，地层受到的扰动程度不同，作用在盾构体上的压力也会不同，造成盾体的不当运动，影响盾构机施工时的姿态。尾部空隙注浆是盾构隧道施工的关键步骤，合理的注浆压力可以保持周围土体的稳定，减少盾构姿态的改变。除此之外，还有诸多因素会影响盾构姿态。
由于搭建在掘进系统上的传感器与数据采集仪是持续工作的，所采集的数据不仅包含盾构机正常工作状态下的数据，也包含许多盾构机停机状态下的数据。停机状态下的数据主要是在盾构机更换刀盘、停机休息、维护等情况下收集的。对于盾构机非工作状态的判定可根据它自身的施工参数来识别，选取推进速度、刀盘转速、总推进力和刀盘扭矩4个主要的施工参数作为判别指标，当收集到的这4个施工参数中有一个为0时，认为盾构机处于非工作状态，将停机数据直接剔除。
在训练模型之前，归一化处理样本集的数据，消除不同样本的特征值维数对预测效率和精度的影响。在采用机器学习算法建立模型时，将不同目标的数据样本归一化到[-1，1]，使输入参数之间可以直接进行比较，避免不同目标的不同量级对后续优化造成影响。通过现场实时数据记录和监控，得到盾构机的747组施工数据，预处理后的输入和输出参数的统计信息见表1。
3.1.2 确定模型超参数
CatBoost预测模型需要调节的参数主要有3个： L2正则化参数(l2_leaf_reg)、学习率(learning_rate)和树的最大深度(max_depth)[10]。超参数的取值对CatBoost模型的精度和训练速度都会产生影响，取值越大，模型的精度就越高，同时，模型的训练时间越长。为保证模型的准确性和高效率，需要参数调优选择一个合理的数值以达到模型的最佳预测性能。采用贝叶斯优化方法优化CatBoost超参数。
为确定精度最高的参数组合为CatBoost预测模型的最佳参数，将训练模型的均方误差(Mean Square Error，MSE)损失函数作为目标函数。在优选参数前，需要给定超参数优选的初始范围，l2_leaf_reg、learning_rate和max_depth设定的初始范围分别为[3，9]，[0，0.5]和[4，8]。超参数寻优结果见表2。
3.1.3 预测结果分析
判断模型预测结果是否精确，模型性能表现是否良好，需要分析模型预测结果与实际测量数据之间的误差。选择决定系数R2、均方根误差(Root Mean Square Error，RMSE)和平均绝对误差(Mean Absolute Error，MAE)作为分析指标，评价所提出的CatBoost预测模型的性能。
为充分利用数据集评估模型性能，采用5倍交叉验证方法，以减少由不合理的数据划分而引起的波动。具体而言，整个数据集被分为5份，其中，4份用于模型训练，1份用于评估模型性能。这一过程重复进行5次，确保整个数据集都参与了模型性能的评估。使用R2、RMSE和MAE这3个指标评估模型的预测性能。基于CatBoost预测模型得到的5个盾构姿态目标的五折交叉验证结果见表3。
从表3可以看出，所建立的CatBoost模型能够准确预测盾构姿态。预测模型的所有折叠的预测误差均较低。5个盾构姿态目标的R2分别为0.934、0.943、0.918、0.917和0.916，RMSE分别为3.707mm、3.428mm、3.576mm、4.308mm和0.943mm/m，MAE分别为2.95mm、2.748mm、2.814mm、3.388mm和0.779 mm/m。这些较高的R2以及较低的MAE和RMSE表明预测值和实际值之间的差异很小。这些结果证明建立的盾构姿态预测模型的性能是准确的，能够有效映射输入和输出之间的关系，不存在过度拟合或拟合不足的情况。
由于开发的预测模型作为多目标优化的目标映射函数，因此，预测模型的可靠性对所提框架的性能至关重要。为进一步验证CatBoost预测模型的有效性，将其与LightGBM、XGBoost、GRU和LSTM这4种最新方法进行比较。这些算法使用相同的数据集，并且使用BO算法寻找它们的最优超参数。在确定各算法超参数的基础上，建立预测模型，预测5个盾构姿态目标。5种方法的预测结果见表4。
表4中，CatBoost在预测5个盾构姿态目标的精度最高，R2最大，MAE和RMSE最小。以切口水平方向位移为例，CatBoost的R2比XGBoost、LightGBM、GRU和LSTM高1.71% ((0.934-0.918)/0.918)、0.86%、1.28%和1.50%，RMSE比XGBoost、LightGBM、GRU和LSTM低12.09% ((4.155-3.707)/3.707)、6.10%、9.17%和10.47%，MAE比XGBoost、LightGBM、GRU和LSTM低12.58% ((3.321-2.95)/2.95)、3.29%、6.92%和9.42%。综上，CatBoost优于目前最先进的方法，能够提供高精度的预测。
3.2 基于RF的参数重要性排序
在实际施工中，对盾构掘进施工参数的控制并非相互独立，而是处于一种动态的相互影响状态。因此，无法单纯地定性分析某一参数对目标的影响[11]。基于RF算法分析23个盾构参数的重要性，重要性排序结果如图1所示。选择对5个盾构姿态目标具有显著影响的盾构施工参数作为主要优化参数，优化重要性排序较高的5个盾构施工参数，以便提出具有可执行性的施工方案。
由图1可知：x6~x9、x18~x21和x1~x4是对盾构姿态参数影响最显著的3个因素。其中，x8主要影响f1和f3，而x6和x7主要影响f2、f4和f5。而x13、x23和x22等因素对盾构姿态参数的影响较小。x11、x1~x4和x12 3个参数的影响程度相近，相对于x13和x23等参数，它们对盾构姿态参数的影响更为显著。
结合前人研究[12]与工程实际结果可知：x1~x4、x6~x9是影响盾构姿态的决定性参数。此外，为更好地控制盾构姿态，合理的x11以及x12至关重要。在对盾构姿态影响较大的参数中，x18~x21具有显著的影响，但因其为盾构机响应参数，无法主动调整，因此，不对其进行控制。综上所述，在控制优化时着重调整x1~x4、x6~x9、x11以及x12等这些盾构施工参数。
3.3 基于NSGA-Ⅲ的盾构姿态多目标优化
基于重要性分析和相关研究，选择调整x1~x4、x6~x9、x12以及x11，形成3个场景，见表5。
3.3.1 目标函数和约束条件的设定
进行多目标优化之前，采用CatBoost算法拟合出盾构施工参数与盾构姿态之间的关系，将引入训练好的非线性映射函数作为优化的适应度函数。基于CatBoost算法盾构姿态的目标函数为：
m i n f ( x ) = [ f 1 ( x ) ， f 2 ( x ) ， … ， f 5 ( x ) ]
f i ( x ) = C a t B o o s t ( x 1 ， x 2 ， … ， x 10 )
为使掘进参数的优化具有实际意义，根据工程要求和项目已有数据对方案生成时各个参数的取值设定限制范围，形成变量约束条件。各施工参数的范围见表6。
3.3.2 获取Pareto前沿解集
以5个盾构姿态目标绝对值最小化为目标，采用NSGA-Ⅲ算法进行全局寻优，确定盾构施工参数的最优解。在多目标优化之前，需要确定NSGA-Ⅲ算法参数的取值[13]。遗传算法的目标数量定为5，种群规模设定为100，交叉算子为0.9，变异算子为0.01，最大进化代数和停止代数为60。确定以上算法参数设置后，按照NSGA-Ⅲ算法的步骤进行多目标优化，得到Pareto最优解集。
基于NSGA-Ⅲ算法能够获得多个盾构参数优化方案，然而实际工程中，只需一个优化方案来指导现场施工。为能够达到最佳优化效果，从众多Pareto解集中选择一个最优方案。采用逼近理想解排序法(Technique for Order Preference by Similarity to Ideal Solution，TOPSIS)从Pareto解集中选取最优解，核心是根据各方案到理想解和负理想解间的距离进行评分。各方案的评分计算公式为：
S i = q i - q i - + q i +
式中：Si为第i个帕累托解对应的分数； q i -为第i个帕累托解到负理想解的距离； q i +为第i个帕累托解到正理想解的距离。 q i -和 q i +的计算公式：
q i - = ∑ j = 1 o r i j - s j - 2
q i + = ∑ j = 1 m r i j - s j + 2
式中：o为优化目标的数量；rij为在第j个目标下第i个Pareto解； s j -和 s j +为在第j个目标下的最大值和最小值。计算公式：
s j - = m i n r 1 j ， r 2 j ， … ， r i j
s j + = m a x r 1 j ， r 2 j ， … ， r i j
为更好地呈现优化方案的改进程度，计算每个Pareto解的改进占比。其中，每个目标的改进和每个方案的整体改进如下式计算：
P i j = u j - u i j ， o u j
P i = ∑ j = 1 o P i j 2
式中：Pij为每i个Pareto解第j个目标的改进占比，%; uj为第j个目标的原始平均值；uij，o为每i个Pareto解第j个目标的优化值；Pi为每i个Pareto解的整体改进占比，%。
3.3.3 结果分析
场景1—3的优化结果如图2所示。图2a为各目标的改进；图2b为5个目标在不同场景下的整体改进结果对比。不同场景下5个盾构姿态目标的优化结果见表7。
该方法可有效解决多目标问题，3种场景下5个盾构姿态目标均得到较好的优化和控制。CatBoost-NSGA-Ⅲ算法有效地构建了所有情况下的帕累托前沿，并生成不同场景下每种情况的最优解。由表7可知：相比于原始数据绝对值的平均值，3个场景的5个盾构姿态目标分别平均减少28.86%、 32.53%、20.78%、14.40%和17.77%，整体改进的平均值为53.34%。3个场景最优解的5个盾构姿态目标分别平均减少28.87%、55.13%、35.97%、21.46%和18.60%，整体改进的平均值为77.29%。实例验证结果表明：基于CatBoost-NSGA-Ⅲ框架得到的解决方案可以同时实现盾构姿态的多重目标优化。
调整更多盾构施工参数，可更大程度地提高优化程度，通过调整4个影响变量实现最高的优化改进。图2a中，场景3每个盾构姿态目标的改进高于场景1和场景2，尽管不同目标的改进程度不一样。由图2b和表7可知：在场景1到场景3中分别调整2~4个盾构施工参数，整体改进的平均值分别为46.11%、54.18%和60.12%，场景3的整体改进最高。此外，从表7最优解结果可以看出，场景3产生的Pareto解集最优解的整体改进比场景1和场景2产生的Pareto解集最优解高，表明调整4个盾构施工操作参数能够达到最佳的优化效果。
4 结论
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1) CatBoost算法可准确得到输入参数与盾构姿态之间的非线性回归映射关系。5个盾构姿态目标的R2范围为0.916~0.943，RMSE范围为0.943~4.308，MAE范围为0.779~3.388。
2) 利用RF算法进行重要性分析，可确定对盾构姿态影响较大的是千斤顶推力、土仓压力、掘进速度、刀盘扭矩等。在进行盾构姿态优化时，需要严格管控这4个盾构参数。
3) 通过NSGA-Ⅲ算法多目标优化盾构姿态，得到盾构施工参数最优解集，采用TOPSIS法确定最优方案，与原始样本的平均值相比，盾构姿态整体改进的平均值为53.34%。调节4个盾构施工参数时，其整体改进达到最高的60.12%，可优化和控制盾构姿态。
4) 通过调整优化盾构施工参数本能优化盾构姿态，但所采用的样本数据来源仅限于一个工程项目。下一步需要基于更多的工程样本，增加样本量，以扩大应用范围。此外，不同的工程项目可能对目标的要求存在差异，当优先考虑某个目标时，需要进一步完善目标优化模型。
基金
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  • 国家自然科学基金(51378235)
  • 国家自然科学基金(71571078)
  • 国家自然科学基金(51308240)
  • 国家重点研发计划项目(2016YFC0800208)
  • 2021年度市教委科学技术研究计划青年项目(KJQN202103801)
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2024年第34卷第8期
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doi: 10.16265/j.cnki.issn1003-3033.2024.08.1360
  • 接收时间：2024-02-21
  • 首发时间：2025-07-09
  • 出版时间：2024-08-28
补充材料
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出版历史
  • 收稿日期：2024-02-21
  • 修回日期：2024-05-28
基金
国家自然科学基金(51378235)
国家自然科学基金(71571078)
国家自然科学基金(51308240)
国家重点研发计划项目(2016YFC0800208)
2021年度市教委科学技术研究计划青年项目(KJQN202103801)
作者信息
    1 华中科技大学 土木与水利工程学院，湖北 武汉 430074
    2 武汉华中科大检测科技有限公司，湖北 武汉 430074

通讯作者:

** 雷宇(2001—)，男，湖南益阳人，硕士研究生，研究方向为土木工程建造与管理。E-mail：。
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https://castjournals.cast.org.cn/joweb/zgaqkxxb/CN/10.16265/j.cnki.issn1003-3033.2024.08.1360
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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