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Journal of ZheJiang University (Engineering Science)  2019, Vol. 53 Issue (3): 482-491    DOI: 10.3785/j.issn.1008-973X.2019.03.009
Civil Engineering     
Construction group comprehensive bearing capacity analysis of deep cutting under green construction
Xue-ying BAO(),Yu-xi ZHENG,Qi-cai WANG
School of Civil Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
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Abstract  

The bucket model of the bearing mechanism of the construction group was established considering the short-board effect of the pollutant discharge factors in the construction process, for the purpose of seeking the optimal combination system of "green construction-resource- environment-construction activity intensity". This model was based on the multi-dimensional benefits under ideal bearing state, taking the construction group as the research subject. According to the principle of state space method, the bucket bottom, bucket wall and bucket water were taken as the status axis, the bearing status was taken as main body and the state of the bearing capacity of the construction group was modeled. Combining the construction of deep cutting and expert decision-making opinions, thirty-one variables were selected as bearing and pressure indexes. With the Canopy platform, the random forest (RF) algorithm and the short board factor measurement model were used to assign weights, and the comprehensive carrying status of the construction group was determined. Taking the deep cutting of Lanxin high-speed railway as an example, a bucket model considering the short-board effect was constructed, the state of the construction group was determined based on the state space model. And it is believed that the green construction effect is positively correlated with the bearing state of the construction group; the comparison of the radar chart in the previous research confirms the feasibility of the proposed model and method.



Key wordsconstruction group      short-board effect      bucket model      state space      bearing capacity      random forest     
Received: 13 June 2018      Published: 04 March 2019
CLC:  U 215.2  
Cite this article:

Xue-ying BAO,Yu-xi ZHENG,Qi-cai WANG. Construction group comprehensive bearing capacity analysis of deep cutting under green construction. Journal of ZheJiang University (Engineering Science), 2019, 53(3): 482-491.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2019.03.009     OR     http://www.zjujournals.com/eng/Y2019/V53/I3/482


绿色施工下的深路堑施工群综合承载度分析

为寻求最优化“绿色施工—资源—环境—施工活动强度”组合系统,基于理想承载状态下的多维效益,以施工群为研究主体,考虑工程施工中排污因子的短板效应,构建施工群承载机制的木桶模型. 采用状态空间法原理,取木桶模型的桶底、桶壁、桶中水作状态轴,以承载状况为研究对象,建立施工群承载度的状态空间模型. 结合深路堑施工工艺和专家决策意见遴选出31个变量作为承、压指标,基于Canopy平台,运用随机森林(RF)算法和短板因子度量模型进行赋权,确定施工群的综合承载状况. 以兰新高铁的深路堑工程为例,构建考虑短板效应的木桶模型,并基于状态空间模型确定施工群的承载状态,结果显示:绿色施工效果与施工群承载状态的优劣成正相关;针对已有成果中雷达图的对比研究证实了所提出的模型与方法的可行性.


关键词: 施工群,  短板效应,  木桶模型,  状态空间,  承载度,  随机森林 
Fig.1 Bucket model of construction group bearing mechanism
Fig.2 State space model of construction group bearing capacity
Fig.3 Working principle of random forest (RF) algorithm
Fig.4 Short board effect trend under different parameters
属性 一级指标 二级指标 三级指标
注:标中未标注单位的三级指标拟用专家打分法
压力
指标
施工活动 工程
特性
边坡高度(m) C1
挖方量(m3) C2
路堑长度(km) C3
施工平面布置图 C4
施工机械配置状况 C5
资源
消耗
施工用水(L/s) C6
施工用电(KWh/d) C7
施工用地(%) C8
燃油消耗(t/d) C9
其他材料消耗 C10
劳动力(人/d) C11
水土流失率(%) C12
环境
污染
扬尘(m) C13
振动(mm/s) C14
噪声(dB) C15
土壤污染 C16
水环境污染 C17
固废排放 C18
光污染(Lux) C19
有害气体 C20
承压
指标
环境 环境
治理
污染物处置情况 E1
拦渣率 E2
资源 自然
资源
地形地貌 R1
地层岩性 R2
地质构造 R3
水文地质条件 R4
可利用土地资源 R5
可利用水资源 R6
其他可利用资源 R7
社会
资源
绿色施工管理 R8
绿色技术应用 R9
Tab.1 Evaluation index system for bearing mechnism of deep cutting construction group
指标 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11
注:在表1中标有单位的均为现场监测值,其余为分值
标段1 25 172 194 1.378 90 90 7.46 2 321 85 195.63 85 55
标段2 28 183 289 1.309 85 90 6.98 2 532 85 208.23 83 65
标段3 25 138 438 1.198 88 85 8.53 2 019 78 157.28 90 50
标段4 23 132 653 1.154 85 85 8.67 1 943 70 150.71 75 48
标段5 27 223 834 1.658 75 85 9.73 2 887 74 254.3 80 70
理想值 25.6 170 081.6 1.339 4 84.6 87 8.274 2 340.4 78.4 193.23 82.6 57.6
指标 C12 C13 C14 C15 C16 C17 C18 C19 C20 E1 E2
标段1 10 1.8 4.5 67.5 90 85 75 88 95 80 90
标段2 15 2.3 4.7 65 90 70 75 93 93 80 87
标段3 15 1.5 4.7 60 88 70 70 98 93 75 85
标段4 13 1.9 4.3 65 87 75 70 100 95 75 87
标段5 10 2.5 4.6 62.5 88 60 65 97 90 75 90
理想值 12.6 1.5 5 70 60 60 60 101 60 77 87.8
指标 R1 R2 R3 R4 R5 R6 R7 R8 R9 ? ?
标段1 70 65 75 70 80 65 75 80 75 ? ?
标段2 75 65 70 65 80 70 75 70 65 ? ?
标段3 75 70 70 70 75 70 80 70 65 ? ?
标段4 70 75 70 75 75 70 80 65 70 ? ?
标段5 65 65 70 70 75 75 80 60 60 ? ?
理想值 71 68 71 70 77 70 78 69 67 ? ?
Tab.2 Basic data statistics of five construction sections of Lanxin high speed rail
指标 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11
标段1 0.920 0.770 0.837 1.000 1.000 0.936 0.837 0.824 0.770 0.944 0.873
标段2 0.821 0.724 0.882 0.944 1.000 1.000 0.767 0.824 0.724 0.922 0.738
标段3 0.920 0.958 0.963 0.978 0.944 0.818 0.962 0.897 0.958 1.000 0.960
标段4 1.000 1.000 1.000 0.944 0.944 0.805 1.000 1.000 1.000 0.833 1.000
标段5 0.852 0.593 0.696 0.833 0.944 0.717 0.673 0.946 0.593 0.889 0.686
理想值 0.898 0.780 0.862 0.940 0.967 0.844 0.830 0.893 0.780 0.918 0.833
指标 C12 C13 C14 C15 C16 C17 C18 C19 C20 E1 E2
标段1 1.000 0.833 0.956 0.889 1.000 1.000 1.000 1.000 1.000 1.000 1.000
标段2 0.667 0.652 0.915 0.923 1.000 0.824 1.000 0.946 0.979 1.000 0.967
标段3 0.667 1.000 0.915 1.000 0.978 0.824 0.933 0.898 0.979 0.938 0.944
标段4 0.769 0.789 1.000 0.923 0.967 0.882 0.933 0.880 1.000 0.938 0.967
标段5 1.000 0.600 0.935 0.960 0.978 0.706 0.867 0.907 0.947 0.938 1.000
理想值 0.794 1.000 0.860 0.857 0.667 0.706 0.800 0.871 0.632 0.963 0.976
指标 R1 R2 R3 R4 R5 R6 R7 R8 R9 ? ?
标段1 0.933 0.867 1.000 0.933 1.000 0.867 0.938 1.000 1.000 ? ?
标段2 1.000 0.867 0.933 0.867 1.000 0.933 0.938 0.875 0.867 ? ?
标段3 1.000 0.933 0.933 0.933 0.938 0.933 1.000 0.875 0.867 ? ?
标段4 0.933 1.000 0.933 1.000 0.938 0.933 1.000 0.813 0.933 ? ?
标段5 0.867 0.867 0.933 0.933 0.938 1.000 1.000 0.750 0.800 ? ?
理想值 0.947 0.907 0.947 0.933 0.963 0.933 0.975 0.863 0.893 ? ?
Tab.3 Standardization of data for five construction sections of Lanxin high peed rail
Fig.5 Out of bag(OOB) error between training samples and test samples
%
折号 1 2 3 4 5 精度
训练 14.22 13.54 16.58 12.43 14.89 14.332
测试 13.78 12.32 13.92 8.77 10.39 11.836
Tab.4 Five-fold cross-validation out of bag (OOB) error rate
Fig.6 Random forest weight values of various variables
标段编号 Fac Fic cos θ Fic, ac CBC
标段1 0.935 2 1.023 0 0.991 6 1.014 4 1.084 7
标段2 0.880 8 0.998 8 0.989 7 0.988 6 1.122 4
标段3 0.917 9 0.916 7 0.993 8 0.911 0 0.992 4
标段4 0.931 1 0.863 9 0.931 3 0.804 6 0.864 1
标段5 0.841 8 0.712 4 0.986 6 0.702 9 0.835 0
Tab.5 Comprehensive bearing capacity of each tender section construction group
Fig.7 Radar plot of comprehensive evaluation of green construction in existing researches
[1]   廖慧璇, 籍永丽, 彭少麟 资源环境承载力与区域可持续发展[J]. 生态环境学报, 2016, 25 (7): 1253- 1258
LIAO Hui-xuan, JI Yong-li, PENG Shao-lin Resources and environment carrying capacity and regional sustainable development[J]. Journal of Eco-environment, 2016, 25 (7): 1253- 1258
[2]   封志明, 杨艳昭, 闫慧敏, 等 百年来的资源环境承载力研究: 从理论到实践[J]. 资源科学, 2017, 39 (3): 379- 395
FENG Zhi-ming, YANG Yan-zhao, YAN Hui-min, et al Research on bearing capacity of resources and environment in the past 100 years: from theory to practice[J]. Resources Science, 2017, 39 (3): 379- 395
[3]   KALUWIN C Takes different approach in pacific islands[J]. Intercostal Network, 1996, 27: 4- 10
[4]   屈小娥 陕西省水资源承载力综合评价研究[J]. 干旱区资源与环境, 2017, 31 (2): 91- 97
QU Xiao-e Comprehensive evaluation of water resources carrying capacity in shaanxi province[J]. Journal of Arid Land Resources and Environment, 2017, 31 (2): 91- 97
[5]   KEI GOMI, YUKI OCHI, YUZURU MATSUOKA A systematic quantitative backcasting on low?carbon society policy in case of Kyoto city[J]. Technological Forecasting and Social Change, 2011, (78): 852- 871
[6]   郑亚光, 蒋瑛, 王琨岚 四川省城市承载力评价[J]. 国土资源科技管理, 2017, 34 (4): 18- 26
ZHENG Ya-guang, JIANG Yu, WANG Wei Evaluation of urban bearing capacity in Sichuan province[J]. Land and Resources Science and Technology Management, 2017, 34 (4): 18- 26
[7]   LANE M The carrying capacity imperative: assessing regional carrying capacity methodologies for sustainable land-use planning[J]. Land Use Policy, 2010, 27 (4): 1038- 1045
doi: 10.1016/j.landusepol.2010.01.006
[8]   孔繁玉 绿色施工理念下铁路工程施工安全风险预警[J]. 山西建筑, 2016, 42 (11): 248- 250
KONG Fan-yu Early warning of railway engineering construction safety risk under the concept of green construction[J]. Shanxi Architecture, 2016, 42 (11): 248- 250
doi: 10.3969/j.issn.1009-6825.2016.11.137
[9]   屈云帅, 李家春, 齐洪亮, 等 深路堑的生态影响及防治措施[J]. 交通企业管理, 2012, 27 (7): 53- 55
QU Yun-shuai, LI Jia-chun, QI Hong-liang, et al Ecological impact and prevention measures of deep roller[J]. Transport Enterprise Management, 2012, 27 (7): 53- 55
doi: 10.3963/j.issn.1006-8864.2012.4.027
[10]   郑南翔, 丛卓红 黄土地区深路堑、高路堤合理横断面的评价方法[J]. 长安大学学报: 自然科学版, 2003, 23 (5): 14- 17
ZHENG Nan-xiang, CONG Zhuo-hong A method for appraising rational cross sections of deep cut and high embankment in loess area[J]. Journal of Chang’an University: Natural Science Edition, 2003, 23 (5): 14- 17
[11]   李健, 杨丹丹, 高杨 基于状态空间模型的天津市环境承载力动态测评[J]. 干旱区资源与环境, 2014, 28 (11): 25- 30
LI Jian, YANG Dan-dan, GAO Yang Dynamic assessment of environmental carrying capacity in Tianjin based on state-space model[J]. Journal of Arid Land Resources and Environment, 2014, 28 (11): 25- 30
[12]   赵铜铁钢, 杨大文, 蔡喜明, 等 基于随机森林模型的长江上游枯水期径流预报研究[J]. 水力发电学报, 2012, 31 (3): 18- 24
ZHAO Tong-tie-gang, YANG Da-wen, CAI Xi-ming, et al Runoff forecasting during the dry season in the upper reaches of the Yangtze River based on the random forest model[J]. Journal of Hydroelectric Engineering, 2012, 31 (3): 18- 24
[13]   杨光宇, 曾东方, 罗平 考虑短板效应的一种度量模型及其在软件可信性中的应用[J]. 计算机应用研究, 2012, 29 (1): 165- 167
YANG Guang-yu, ZENG Dong-fang, LUO Ping A measurement model considering the short board effect and its application in software reliability[J]. Research on Application of Computer, 2012, 29 (1): 165- 167
[14]   吕晶. 绿色施工量化评价研究[D]. 重庆: 重庆大学, 2015.
LV Jing. Research on quantitative evaluation of green construction [D]. Chongqing: Chongqing University, 2015.
[15]   徐晓亮, 许学芬 资源税改革与我国区域" 资源诅咒”困境[J]. 系统工程理论与实践, 2015, 35 (9): 2232- 2241
XU Xiao-liang, XU Xue-fen Resource tax reform and the dilemma of regional resource curses in China[J]. Systems Engineering-Theory and Practice, 2015, 35 (9): 2232- 2241
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