基于改进极限学习机模型的盾构掘进引发地表最大沉降预测

阮永芬; 邱龙; 乔文件; 闫明; 郭宇航

doi:10.16030/j.cnki.issn.1000-3665.202210007

基于改进极限学习机模型的盾构掘进引发地表最大沉降预测

doi: 10.16030/j.cnki.issn.1000-3665.202210007

阮永芬^1,,
邱龙^1, ,,
乔文件²,
闫明²,
郭宇航²

1.
昆明理工大学建筑工程学院，云南昆明　650500
2.
中铁二十局集团第五工程有限公司，云南昆明　650000

基金项目: 云南省重点研发计划（社会发展领域）项目（2018BC008）

详细信息

作者简介:
阮永芬（1964-），女，博士，教授，主要从事工程地质与岩土工程的研究。E-mail：rryy64@163.com

通讯作者:
邱龙（1998-），男，硕士研究生，主要从事工程地质与岩土工程的研究。E-mail： 1034403813@qq.com

中图分类号: TU478
计量
- 文章访问数: 73
- HTML全文浏览量: 24
- PDF下载量: 34
- 被引次数: 0
出版历程
- 收稿日期: 2022-10-08
- 修回日期: 2023-01-07
- 网络出版日期: 2023-08-11
- 刊出日期: 2023-09-19

Prediction of the maximum ground settlement caused by shield tunneling based on the improved limit learning machine model

1.
Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming, Yunnan　650500, China
2.
China Railway 16th Bureau Group Urban Rail Engineering Co. Ltd., Kunmig, Yunnan　650000, China

摘要

摘要: 城市地铁盾构施工引发的地面过大变形会严重影响周边构筑物的正常使用，甚至引发工程事故。针对传统预测方法中的数据维度过大容易导致精度降低、计算复杂等问题，提出了一种基于主成分分析（principal component analysis，PCA）算法和哈里斯鹰优化（Harris Hawks optimization，HHO）算法的极限学习机（extreme learning machine，ELM）预测模型。在地质、几何及盾构参数中初选14个影响因子，利用PCA算法在14维数组中分离和提取5个主成分变量作为模型的输入，利用HHO优化ELM模型的输入层权值和隐含层阈值参数，得到预测模型的最优解。以昆明轨道交通五号线怡心桥站—广福路站隧道区间监测数据进行仿真验证，并将该模型与BP神经网络、RBF、未优化的ELM模型进行对比分析。结果表明：PCA-HHO-ELM预测模型的均方根误差为0.143 5、平均绝对误差为0.026 2、决定系数为0.959 6，相较于其他模型，该模型具有更优的预测性能；与未优化的ELM模型相比，HHO算法能够提高ELM模型的预测精度和泛化能力。PCA-HHO-ELM模型能可靠预测盾构诱发的地表最大沉降，可为类似变形预测提供一种更为可行的新思路。
- 地表最大沉降 /
- 主成分分析 /
- 哈里斯鹰算法 /
- 极限学习机 /
- 沉降预测
Abstract: Excessive ground deformation caused by shield tunneling of urban metro will seriously affect the normal use of surrounding structures, and even cause engineering accidents. In view of the problems that the data dimension in traditional prediction methods is too large, which easily leads to lower accuracy and complex calculation, this study proposes an extreme learning machine (ELM) prediction model based on the principal component analysis (PCA) algorithm and Harris Hawk optimization algorithm (HHO). Ten influence factors are preliminarily selected from the geological, geometric and shield parameters. PCA is used to separate and extract five principal component variables from the 10 dimensional arrays as the input of the model. HHO is used to optimize the input layer weights and hidden layer threshold parameters of the ELM model, and the optimal solution of the prediction model is obtained. The monitoring data of the Yiguang section of Kunming Rail Transit Line 5 are used for simulation verification, and the model is compared with the BP neural network, RBF and non-optimized ELM model. The results show that the root mean square error of the PCA-HHO-ELM prediction model is 0.1435, the average absolute error is 0.0262, and the determination coefficient R² is 0.9596. Compared with other models, this model has better prediction performance. Compared with the non-optimized ELM, HHO can improve the prediction accuracy and generalization ability of ELM. The PCA-HHO-ELM model can reliably predict the maximum ground settlement induced by shield, and can provide a more feasible new idea for similar deformation prediction.
- maximum surface settlement /
- Principal component analysis /
- Harris Hawk Optimization /
- Extreme learning machine /
- settlement prediction

HTML全文

图 1 ELM算法模型结构图

Figure 1. Structure diagram of ELM algorithm model

下载: 全尺寸图片幻灯片

图 2 PCA-HHO-ELM模型流程图

Figure 2. Flow chart of PCA-HHO-ELM model

下载: 全尺寸图片幻灯片

图 3 PCA主成分提取变量

Figure 3. PCA principal component extraction variables

下载: 全尺寸图片幻灯片

图 4 PCA-HHO-ELM模型预测结果

Figure 4. Prediction results of PCA-HHO-ELM model

下载: 全尺寸图片幻灯片

图 5 各模型预测结果对比

Figure 5. Comparison of prediction results of various models

下载: 全尺寸图片幻灯片

表 1 数据集数据统计信息表

Table 1. Data set statistics table

	参数	最小值	最大值
地质参数	地下水位（A₁）/m	1.50	1.92
	浮密度（A₂）/（g·cm^–3）	0.54	0.77
	压缩模量（A₃）/MPa	3.37	4.42
	泊松比（A₄）	0.28	0.33
	侧压力系数（A₅）	0.49	0.59
几何参数	埋深（A₆）/m	18.4	25.3
盾构参数	土压力（A₇）/MPa	1.8	2.7
	总推力（A₈）/kN	8 760	16 280
	掘进速度（A₉）/（mm·min^–1）	61	94
	出土量（A₁₀）/ m³	45.8	48.0
	刀盘扭矩（A₁₁）/（N·m）	1 200	2 275
	拼装时间（A₁₂）/h	0.35	0.85
	注浆量（A₁₃）/m³	3.60	4.40
	注浆压力（A₁₄）/bar	3.0	3.8
	地表最大沉降/mm	−15.69	10.44

下载: 导出CSV

表 2 主成分系数矩阵

Table 2. Principal component coefficient matrix

影响因子	主成分
影响因子	1	2	3	4	5
A₁	0.897	0.023	0.086	0.077	0.052
A₂	0.937	−0.077	0.043	0.041	0.051
A₃	−0.228	0.586	0.376	−0.460	−0.163
A₄	−0.271	−0.259	−0.724	0.340	0.105
A₅	0.956	−0.068	−0.130	−0.007	−0.028
A₆	0.376	0.335	0.365	0.492	0.315
A₇	0.683	−0.093	−0.116	−0.520	0.060
A₈	−0.078	0.310	0.425	−0.053	0.748
A₉	−0.474	0.530	−0.582	0.019	0.216
A₁₀	−0.227	0.867	−0.247	0.123	−0.128
A₁₁	−0.809	−0.112	−0.056	−0.256	0.188
A₁₂	−0.790	−0.464	0.277	−0.071	0.080
A₁₃	−0.201	−0.636	0.046	0.019	0.247
A₁₄	−0.445	−0.011	0.622	0.384	−0.365

下载: 导出CSV

表 3 4类模型预测结果

Table 3. Prediction results of four types of models

组号	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
实测值/mm	2.89	9.86	5.03	4.45	2.07	10.44	0.82	3.64	0.44	13.42	9.69	4.96	2.54	1.93	1.43
BP预测值/mm	1.62	−8.49	3.61	−8.92	−0.56	12.08	−0.97	3.51	0.12	−13.78	10.10	−3.74	2.53	2.24	0.70
RBF预测值/mm	5.04	0.49	2.12	2.00	−2.86	2.81	0.31	4.48	0.50	−11.03	5.07	−11.39	2.03	2.01	0.50
ELM预测值/mm	−0.24	−6.17	4.21	0.11	−0.94	12.90	0.27	−0.51	−1.51	−11.13	11.95	−4.05	2.63	2.12	0.26
PCA-HHO-ELM预测值/mm	2.11	−7.83	4.93	−5.25	0.14	9.38	−1.90	7.34	−1.23	−12.66	12.93	−4.72	3.83	3.46	−0.82

组号	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30
实测值/mm	0.01	6.33	5.22	14.19	13.74	1.14	4.17	5.31	1.61	0.08	9.90	1.24	10.84	2.24	12.35
BP预测值/mm	−0.06	4.85	4.89	−14.13	−14.25	0.49	4.10	5.93	1.73	−0.05	−9.73	−0.01	−10.46	1.30	−11.83
RBF预测值/mm	0.50	3.59	2.56	−11.32	−7.45	0.50	4.47	5.07	0.50	0.50	−9.67	0.50	−8.02	0.58	−7.45
ELM预测值/mm	−0.53	5.72	5.79	−11.76	−15.91	1.34	2.90	5.71	1.65	−0.58	−9.78	0.70	−8.43	−0.12	−14.09
PCA-HHO-ELM预测值/mm	−1.33	6.50	6.89	−13.27	−14.07	2.16	4.35	2.43	4.00	−0.99	−9.01	0.13	−10.76	1.18	−12.41

下载: 导出CSV

表 4 模型性能评价结果

Table 4. Performance evaluation results of the model

评价指标	RMSE	MAE	R²	最大误差/mm	平均误差/mm
PCA-HHO-ELM模型	0.143 5	0.026 2	0.959 6	3.70	1.29
BP预测模型	0.232 3	0.042 4	0.894 3	4.48	0.82
RBF模型	0.392 1	0.071 6	0.698 6	10.35	2.52
ELM预测模型	0.571 8	0.104 4	0.359 2	4.56	1.48

下载: 导出CSV

参考文献(31)

[1]	胡群芳,李敏,王飞. 我国城市地铁隧道盾构施工刀具磨损性分区研究[J]. 现代隧道技术,2016,53(2):26 − 34. [HU Qunfang,LI Min,WANG Fei. Research on regionalization of cutting-tool wear conditions in shield tunnels of urban subways in China[J]. Modern Tunnelling Technology,2016,53(2):26 − 34. (in Chinese with English abstract) HU Qunfang, LI Min, WANG Fei. Research on regionalization of cutting-tool wear conditions in shield tunnels of urban subways in China[J]. Modern Tunnelling Technology, 2016, 53（ 2）: 26 − 34. （in Chinese with English abstract）
[2]	路德春,马一丁,王国盛. 近接隧道列车运行时地表振动响应数值模拟[J]. 吉林大学学报(地球科学版),2021,51(5):1452 − 1462. [LU Dechun,MA Yiding,WANG Guosheng. Numerical study on ground surface vibration response under train load in multi adjacent tunnels[J]. Journal of Jilin University (Earth Science Edition),2021,51(5):1452 − 1462. (in Chinese with English abstract) LU Dechun, MA Yiding, WANG Guosheng. Numerical study on ground surface vibration response under train load in multi adjacent tunnels[J]. Journal of Jilin University （Earth Science Edition）, 2021, 51（ 5）: 1452 − 1462. （in Chinese with English abstract）
[3]	XU Qianwei,ZHU Hehua,MA Xianfeng,et al. A case history of shield tunnel crossing through group pile foundation of a road bridge with pile underpinning technologies in Shanghai[J]. Tunnelling and Underground Space Technology,2015,45:20 − 33. doi: 10.1016/j.tust.2014.09.002
[4]	蒋彪,皮圣,阳军生,等. 长沙地铁典型地层盾构施工地表沉降分析与预测[J]. 地下空间与工程学报,2016,12(1):181 − 187. [JIANG Biao,PI Sheng,YANG Junsheng,et al. Analysis and prediction of ground surface settlements due to EPB shield tunneling of Changsha metro[J]. Chinese Journal of Underground Space and Engineering,2016,12(1):181 − 187. (in Chinese with English abstract) JIANG Biao, PI Sheng, YANG Junsheng, et al. Analysis and prediction of ground surface settlements due to EPB shield tunneling of Changsha metro[J]. Chinese Journal of Underground Space and Engineering, 2016, 12（ 1）: 181 − 187. （in Chinese with English abstract）
[5]	PARK K H. Analytical solution for tunnelling-induced ground movement in clays[J]. Tunnelling and Underground Space Technology,2005,20(3):249 − 261. doi: 10.1016/j.tust.2004.08.009
[6]	赖文杰,齐昌广,郑金辉,等. 含分数阶的灰色模型及其在地基沉降预测中的应用[J]. 水文地质工程地质,2019,46(3):124 − 128. [LAI Wenjie,QI Changguang,ZHENG Jinhui,et al. Gray model with fractional order and its application to settlement prediction[J]. Hydrogeology & Engineering Geology,2019,46(3):124 − 128. (in Chinese with English abstract) LAI Wenjie, QI Changguang, ZHENG Jinhui, et al. Gray model with fractional order and its application to settlement prediction[J]. Hydrogeology & Engineering Geology, 2019, 46（ 3）: 124 − 128. （in Chinese with English abstract）
[7]	郭社军,郝晓龙,舒国明. Peck公式在小半径曲线隧道沉降分析的应用[J]. 公路,2020,65(8):405 − 408. [GUO Shejun,HAO Xiaolong,SHU Guoming. Application of Peck formula in settlement analysis of small radius curved tunnel[J]. Highway,2020,65(8):405 − 408. (in Chinese). GUO Shejun, HAO Xiaolong, SHU Guoming. Application of Peck formula in settlement analysis of small radius curved tunnel[J]. Highway, 2020, 65（ 8）: 405 − 408. （in Chinese）.
[8]	PECK R B. Deep excavations and tunneling in soft ground[C]//Mexico:Proceeding of 7th ICSMFE,1969:225 − 290.
[9]	范珊珊,郭海朋,朱菊艳,等. 线性回归模型在北京平原地面沉降预测中的应用[J]. 中国地质灾害与防治学报,2013,24(1):70 − 74. [FAN Shanshan,GUO Haipeng,ZHU Juyan,et al. Application of linear regression model for land subsidence prediction in Beijing plain[J]. The Chinese Journal of Geological Hazard and Control,2013,24(1):70 − 74. (in Chinese with English abstract) FAN Shanshan, GUO Haipeng, ZHU Juyan, et al. Application of linear regression model for land subsidence prediction in Beijing plain[J]. The Chinese Journal of Geological Hazard and Control, 2013, 24（1）: 70 − 74. （in Chinese with English abstract）
[10]	KIM C Y,BAE G J,HONG S W,et al. Neural network based prediction of ground surface settlements due to tunnelling[J]. Computers and Geotechnics,2001,28(6/7):517 − 547.
[11]	DIAS D,KASTNER R. Movements caused by the excavation of tunnels using face pressurized shields:Analysis of monitoring and numerical modeling results[J]. Engineering Geology,2013,152(1):17 − 25. doi: 10.1016/j.enggeo.2012.10.002
[12]	于德海,舒娇娇,秦凯凯. 盾构地铁隧道穿越既有铁路桥的沉降分析[J]. 水文地质工程地质,2020,47(2):148 − 152. [YU Dehai,SHU Jiaojiao,QIN Kaikai. An analysis of the settlement of a shield tunnel passing under the operating railway bridge[J]. Hydrogeology & Engineering Geology,2020,47(2):148 − 152. (in Chinese with English abstract) YU Dehai, SHU Jiaojiao, QIN Kaikai. An analysis of the settlement of a shield tunnel passing under the operating railway bridge[J]. Hydrogeology & Engineering Geology, 2020, 47（ 2）: 148 − 152. （in Chinese with English abstract）
[13]	潘涛. 软土地区双线区间盾构隧道施工对周边地表以及建筑物沉降的影响[J]. 水文地质工程地质,2022,49(1):101 − 108. [PAN Tao. Influences of double-track shield tunnel construction on settlements of adjacent ground and buildings in a soft soil area[J]. Hydrogeology & Engineering Geology,2022,49(1):101 − 108. (in Chinese with English abstract) PAN Tao. Influences of double-track shield tunnel construction on settlements of adjacent ground and buildings in a soft soil area[J]. Hydrogeology & Engineering Geology, 2022, 49（ 1）: 101 − 108. （in Chinese with English abstract）
[14]	徐明祥,黄强兵,王庆兵,等. 西安地裂缝地段浅埋暗挖地铁隧道施工沉降规律[J]. 水文地质工程地质,2020,47(1):161 − 170. [XU Mingxiang,HUANG Qiangbing,WANG Qingbing,et al. Settlement rules of shallow-buried metro tunnel construction in the Xi’an ground fissure section[J]. Hydrogeology & Engineering Geology,2020,47(1):161 − 170. (in Chinese with English abstract) XU Mingxiang, HUANG Qiangbing, WANG Qingbing, et al. Settlement rules of shallow-buried metro tunnel construction in the Xi’an ground fissure section[J]. Hydrogeology & Engineering Geology, 2020, 47（ 1）: 161 − 170. （in Chinese with English abstract）
[15]	ZHANG Pin,CHEN Renpeng,WU Huaina. Real-time analysis and regulation of EPB shield steering using Random Forest[J]. Automation in Construction,2019,106:102860. doi: 10.1016/j.autcon.2019.102860
[16]	赵楠,李洁. 基于LSTM-SVM的隧道围岩位移预测[J]. 公路,2021,66(6):404 − 407. [ZHAO Nan,LI Jie. Displacement prediction of tunnel surrounding rock based on LSTM-SVM[J]. Highway,2021,66(6):404 − 407. (in Chinese) ZHAO Nan, LI Jie. Displacement prediction of tunnel surrounding rock based on LSTM-SVM[J]. Highway, 2021, 66（6）: 404 − 407. （in Chinese）
[17]	李建生,阳军生,杨铠,等. 改进遗传算法在浅埋隧道施工倾斜地表沉降预测中的应用[J]. 公路工程,2008,33(6):46 − 49. [LI Jiansheng,YANG Junsheng,YANG Kai,et al. Application of improved genetic algorithm in prediction of inclined ground settlement in shallow tunnel construction[J]. Highway Engineering,2008,33(6):46 − 49. (in Chinese) LI Jiansheng, YANG Junsheng, YANG Kai, et al. Application of improved genetic algorithm in prediction of inclined ground settlement in shallow tunnel construction[J]. Highway Engineering, 2008, 33（ 6）: 46 − 49. （in Chinese）
[18]	CHEN Renpeng,ZHANG Pin,KANG Xin,et al. Prediction of maximum surface settlement caused by earth pressure balance (EPB) shield tunneling with ANN methods[J]. Soils and Foundations,2019,59(2):284 − 295. doi: 10.1016/j.sandf.2018.11.005
[19]	乔金丽,范永利,刘波,等. 基于改进BP网络的盾构隧道开挖地表沉降预测[J]. 地下空间与工程学报,2012,8(2):352 − 357. [QIAO Jinli,FAN Yongli,LIU Bo,et al. Predicting the surface settlement by shield tunneling based on modified BP network[J]. Chinese Journal of Underground Space and Engineering,2012,8(2):352 − 357. (in Chinese with English abstract) QIAO Jinli, FAN Yongli, LIU Bo, et al. Predicting the surface settlement by shield tunneling based on modified BP network[J]. Chinese Journal of Underground Space and Engineering, 2012, 8（ 2）: 352 − 357. （in Chinese with English abstract）
[20]	WANG Fan,GOU Biancai,QIN Yawei. Modeling tunneling-induced ground surface settlement development using a wavelet smooth relevance vector machine[J]. Computers and Geotechnics,2013,54:125 − 132. doi: 10.1016/j.compgeo.2013.07.004
[21]	陈仁朋,戴田,张品,等. 基于机器学习算法的盾构掘进地表沉降预测方法[J]. 湖南大学学报(自然科学版),2021,48(7):111 − 118. [CHEN Renpeng,DAI Tian,ZHANG Pin,et al. Prediction method of tunneling-induced ground settlement using machine learning algorithms[J]. Journal of Hunan University (Natural Sciences),2021,48(7):111 − 118. (in Chinese with English abstract) CHEN Renpeng, DAI Tian, ZHANG Pin, et al. Prediction method of tunneling-induced ground settlement using machine learning algorithms[J]. Journal of Hunan University （Natural Sciences）, 2021, 48（ 7）: 111 − 118. （in Chinese with English abstract）
[22]	宫思艺,孔宪光,刘丹,等. 融入复杂地层动态识别的盾构施工地表沉降预测方法研究[J]. 仪器仪表学报,2019,40(6):228 − 236. [GONG Siyi,KONG Xianguang,LIU Dan,et al. An approach for predicting shield construction ground surface settlement of complex stratum using dynamical strata identification[J]. Chinese Journal of Scientific Instrument,2019,40(6):228 − 236. (in Chinese with English abstract) GONG Siyi, KONG Xianguang, LIU Dan, et al. An approach for predicting shield construction ground surface settlement of complex stratum using dynamical strata identification[J]. Chinese Journal of Scientific Instrument, 2019, 40（ 6）: 228 − 236. （in Chinese with English abstract）
[23]	林荣安,孙钰丰,戴振华,等. 基于RS-SVR的上软下硬地层盾构施工地表沉降预测[J]. 中国公路学报,2018,31(11):130 − 137. [LIN Rongan,SUN Yufeng,DAI Zhenhua,et al. Predicting for ground surface settlement induced by shield tunneling in upper-soft and lower-hard ground based on RS-SVR[J]. China Journal of Highway and Transport,2018,31(11):130 − 137. (in Chinese with English abstract) LIN Rongan, SUN Yufeng, DAI Zhenhua, et al. Predicting for ground surface settlement induced by shield tunneling in upper-soft and lower-hard ground based on RS-SVR[J]. China Journal of Highway and Transport, 2018, 31（ 11）: 130 − 137. （in Chinese with English abstract）
[24]	李洛宾,龚晓南,甘晓露,等. 基于循环神经网络的盾构隧道引发地面最大沉降预测[J]. 土木工程学报,2020,53(增刊1):13 − 19. [LI Luobin,GONG Xiaonan,GAN Xiaolu,et al. Prediction of maximum ground settlement induced by shield tunneling based on recurrent neural network[J]. China Civil Engineering Journal,2020,53(Sup 1):13 − 19. (in Chinese with English abstract) LI Luobin, GONG Xiaonan, GAN Xiaolu, et al. Prediction of maximum ground settlement induced by shield tunneling based on recurrent neural network[J]. China Civil Engineering Journal, 2020, 53（Sup 1）: 13 − 19. （in Chinese with English abstract）
[25]	LIU Peng,LI Zhengtian,ZHUO Yixin,et al. Design of wind turbine dynamic trip-off risk alarming mechanism for large-scale wind farms[J]. IEEE Transactions on Sustainable Energy,2017,8(4):1668 − 1678. doi: 10.1109/TSTE.2017.2701348
[26]	TANG Jiexiong,DENG Chenwei,HUANG Guangbin. Extreme learning machine for multilayer perceptron[J]. IEEE Transactions on Neural Networks and Learning Systems,2016,27(4):809 − 821. doi: 10.1109/TNNLS.2015.2424995
[27]	HUANG Guangbin,ZHU Qinyu,SIEW C K. Extreme learning machine:A new learning scheme of feedforward neural networks[C]//Budapest,Hungary:2004 IEEE International Joint Conference on Neural Networks,2005:985 − 990.
[28]	HEIDARI A A,MIRJALILI S,FARIS H,et al. Harris Hawks optimization:Algorithm and applications[J]. Future Generation Computer Systems,2019,97:849 − 872. doi: 10.1016/j.future.2019.02.028
[29]	BOUAYAD D,EMERIAULT F. Modeling the relationship between ground surface settlements induced by shield tunneling and the operational and geological parameters based on the hybrid PCA/ANFIS method[J]. Tunnelling and Underground Space Technology,2017,68:142 − 152. doi: 10.1016/j.tust.2017.03.011
[30]	BOUVEYRON C,LATOUCHE P,MATTEI P A. Exact dimensionality selection for Bayesian PCA[J]. Scandinavian Journal of Statistics,2020,47(1):196 − 211. doi: 10.1111/sjos.12424
[31]	苗凤娟,孙同日,陶佰睿,等. PSO与PCA融合优化核极限学习机说话人识别算法仿真[J]. 科学技术与工程,2019,19(21):195 − 199. [MIAO Fengjuan,SUN Tongri,TAO Bairui,et al. Algorithmic research on kernel extreme learning machine for speaker recognition based on PSO and PCA optimization[J]. Science Technology and Engineering,2019,19(21):195 − 199. (in Chinese with English abstract) doi: 10.3969/j.issn.1671-1815.2019.21.029 MIAO Fengjuan, SUN Tongri, TAO Bairui, et al. Algorithmic research on kernel extreme learning machine for speaker recognition based on PSO and PCA optimization[J]. Science Technology and Engineering, 2019, 19（ 21）: 195 − 199. （in Chinese with English abstract） doi: 10.3969/j.issn.1671-1815.2019.21.029