Journal of Shandong University(Engineering Science) ›› 2023, Vol. 53 ›› Issue (5): 1-9, 19.doi: 10.6040/j.issn.1672-3961.0.2022.357

• Machine Learning & Date Mining •     Next Articles

Resilience evaluation system of the old community based on cloud model

Yujiang FAN1,2(),Huanhuan HUANG1,Jiaxiong DING2,Kai LIAO2,Binshan YU3   

  1. 1. School of Architecture, Chang′an University, Xi′an 710061, Shaanxi, China
    2. College of Architectural Engineering, Chang′an University, Xi′an 710061, Shaanxi, China
    3. College of Mechanics and Civil Engineering, Northwestern Polytechnical University, Xi′an 710072, Shaanxi, China
  • Received:2022-10-24 Online:2023-10-20 Published:2023-10-19

Abstract:

In order to accurately and efficiently improve the living environment quality of old urban communities and improve their disaster prevention and mitigation capabilities, a resilience evaluation system of the old community based on cloud model was proposed. Based on the resilience theory, the selected old communities were grouped according to different ages. Through field research, 30 groups of representative old communities characteristic data in Xi′an, Shaanxi Province were obtained. With reference to relevant literature and relevant expert suggestions, the building resilience, facility resilience, environmental resilience, and personnel resilience were determined. A total of 4 first-level indicators, 9 second-level indicators, and 30 third-level indicators constituted the evaluation index system of old communities. Analytic hierarchy process (AHP) was used to determine the subjective weight of each index, entropy weight method (EWN) and method based on the removal effects of criteria (MEREC) were used to determine its objective weight, combined weighting method was used to determine its final weight, and MATLAB was used to construct the resilience evaluation system of the old community based on cloud model. Based on this evaluation system, an old community—Wanqingxiang community in Xi′an was selected for resilience evaluation. The results could accurately reflect the weak links of resilience in the community, and also showed that the evaluation system had certain applicability and effectiveness.

Key words: cloud model, old community, resilience evaluation, combination weight, index evaluation system

CLC Number: 

  • TP391

Table 1

Resilience evaluation index library of old residential areas"

目标层 准测层 子准测层 指标层
老旧小区韧性评价 建筑韧性建筑主体韧性建筑抗震性能
结构破损度
屋面性能
楼梯间性能
建筑附属设备韧性 电气线路现状
给排水管溢现状
燃气设备现状
设施韧性应急设施韧性消防设施
应急指示系统
应急供电设施
应急供水设施
安防设施韧性门禁系统
监控覆盖率
综合活动设施
服务设施韧性便民服务站
室外活动设施
环境韧性室内环境韧性室内声环境
室内光环境
室内热环境
室外环境韧性消防通道
道路材质
道路照度
绿地率
人员韧性居住人员韧性特殊群体人员占比
有自救常识人员占比
灾害保险投保率
管理人员韧性防灾教育与演练
应急预案编制
应急物资管理
设备日常巡检

Table 2

Quantitative indicators quantitative grading score range"

等级 M1 M2 M3 M4 M5
分值范围 [0, 30] (30, 60] (60, 80] (80, 90] (90, 100]

Table 3

Quantitative criteria and basis for qualitative indicators"

评价指标量化范围
等级M1 等级M2 等级M3 等级M4 等级M5
监控覆盖率 [0, 0.40] (0.40, 0.60] (0.60, 0.80] (0.80, 0.90] (0.90, 1.00]
室内声环境 >45 (40, 45] (35, 40] (30, 35] (0, 30]
室内光环境 [0, 1/8] (1/8, 1/7] (1/7, 1/6] (1/6, 1/5] (1/5, 1]
道路照度 [0, 1.0] (1.0, 3.0] (3.0, 5.0] (5.0, 7.5] >7.5
绿地率 [0, 0.25] (0.25, 0.28] (0.28, 0.30] (0.30, 0.35] (0.35, 1.00]
特殊群体人员占比 (0.85, 1.00] (0.80, 0.85] (0.75, 0.80] (0.70, 0.75] ≤0.70
有自救常识人员占比 [0, 0.20] (0.20, 0.30] (0.30, 0.40] (0.40, 0.50] (0.50, 1.00]
灾害保险投保率 [0, 0.06] (0.06, 0.08] (0.08, 0.10] (0.10, 0.12] (0.12, 1.00]

Table 4

Evaluation index final combination weight"

准则层 子准则层 指标层 ωAj ωKj ωi
建筑韧性建筑主体韧性建筑抗震性能 0.047 6 0.040 7 0.058 7
结构破损度 0.043 2 0.040 6 0.053 1
屋面性能 0.029 7 0.030 0 0.026 9
楼梯间性能 0.032 2 0.024 8 0.024 2
建筑附属设备韧性 电气线路现状 0.044 1 0.027 4 0.036 5
给排水管道现状 0.038 1 0.027 2 0.031 4
燃气设备现状 0.039 3 0.041 1 0.048 9
设施韧性应急设施韧性消防设施 0.031 0 0.046 0 0.043 2
应急指示系统 0.025 0 0.047 9 0.036 3
应急供电设施 0.020 9 0.036 9 0.023 4
应急供水设施 0.020 4 0.035 8 0.022 1
安防设施韧性 门禁系统 0.032 1 0.035 6 0.034 5
监控覆盖率 0.029 8 0.040 6 0.036 6
综合报警系统 0.031 4 0.037 8 0.036 0
服务设施韧性 便民服务站 0.036 6 0.038 5 0.042 6
室外活动设施 0.038 8 0.033 6 0.039 4
环境韧性室内环境韧性室内声环境 0.034 0 0.014 8 0.015 3
室内光环境 0.034 9 0.023 2 0.024 5
室内热环境 0.042 2 0.018 3 0.023 4
室外环境韧性 消防通道 0.039 3 0.041 7 0.049 6
道路材质 0.033 3 0.032 5 0.032 8
道路照度 0.025 8 0.039 4 0.030 7
绿地率 0.032 9 0.026 0 0.026 0
人员韧性居住人员韧性特殊群体人员占比 0.033 4 0.015 2 0.015 4
有自救常识人员占比 0.030 3 0.028 0 0.025 8
灾害保险投保率 0.025 9 0.036 8 0.028 8
管理人员韧性 防灾教育与演练 0.034 3 0.024 0 0.024 9
应急预案编制 0.033 5 0.037 6 0.038 1
应急物资管理 0.030 8 0.040 2 0.037 5
设备日常巡检 0.029 1 0.037 8 0.033 4

Fig.1

Cloud generator diagram"

Table 5

Evaluation grade standard and standard cloud parameters"

评价等级 评分区间 评价标准云模型参数
韧性水平低 [0, 30] (15, 12.74, 0.5)
韧性水平较低 (30, 60] (45, 12.74, 0.5)
韧性水平一般 (60, 80] (70, 8.49, 0.5)
韧性水平良好 (80, 90] (85, 4.25, 0.5)
韧性水平优秀 (90, 100] (95, 4.25, 0.5)

Fig.2

Evaluation standard cloud"

Table 6

Evaluation index cloud digital characteristics"

准则层(一级指标) 子准则层(二级指标) 指标层(三级指标) ωi CU
建筑韧性 建筑主体韧性 建筑抗震性能 0.058 7 (84.2, 3.058 1, 0.251 0)
结构破损度 0.053 1 (81.8, 3.358 9, 0.939 2)
屋面性能 0.026 9 (65.8, 4.161 0, 1.879 0)
楼梯间性能 0.024 2 (66.7, 4.135 9, 0.806 5)
建筑附属设备韧性 电气线路现状 0.036 5 (60.4, 2.757 3, 0.944 2)
给排水管道现状 0.031 4 (46.9, 1.654 4, 0.172 4)
燃气设备现状 0.048 9 (41.8, 2.005 3, 0.536 6)
设施韧性应急设施韧性消防设施 0.043 2 (59.3, 1.704 5, 0.477 0)
应急指示系统 0.036 3 (25.4, 2.356 2, 0.219 5)
应急供电设施 0.023 4 (55.6, 2.155 7, 0.776 3)
应急供水设施 0.022 1 (44.5, 2.632 0, 0.933 6)
安防设施韧性 门禁系统 0.034 5 (24.1, 2.130 6, 0.466 2)
监控覆盖率 0.036 6 (51.8, 2.055 4, 0.418 6)
综合报警系统 0.036 0 (23.1, 2.381 3, 0.825 6)
服务设施韧性 便民服务站 0.042 6 (14.2, 2.005 3, 0.256 3)
室外活动设施 0.039 4 (6.6, 1.253 3, 0.170 9)
环境韧性室内环境韧性室内声环境 0.015 3 (84.4, 2.155 7, 0.535 1)
室内光环境 0.024 5 (68.8, 2.556 8, 1.191 0)
室内热环境 0.023 4 (51.2, 1.804 8, 0.503 9)
室外环境韧性 消防通道 0.049 6 (24.7, 1.880 0, 0.641 9)
道路材质 0.032 8 (45.8, 1.804 8, 0.835 7)
道路照度 0.030 7 (48.8, 2.506 6, 0.582 3)
绿地率 0.026 0 (63.5, 1.880 0, 0.768 0)
人员韧性居住人员韧性特殊群体人员占比 0.015 4 (71.3, 1.880 0, 0.179 9)
有自救常识人员占比 0.025 8 (63.9, 1.403 7, 0.304 4)
灾害保险投保率 0.028 8 (45.6, 1.504 0, 0.068 7)
管理人员韧性 防灾教育与演练 0.024 9 (45.3, 2.130 6, 0.289 9)
应急预案编制 0.038 1 (33.6, 2.606 9, 0.986 7)
应急物资管理 0.037 5 (7.2, 1.052 8, 0.424 9)
设备日常巡检 0.033 4 (33.1, 1.629 3, 0.334 7)

Table 7

Evaluation index cloud digital characteristics of criterion layer and sub-criterion layer"

准则层子准则层
指标 CU 指标 CU
建筑韧性 (67.068 4, 3.473 5, 0.877 1)建筑主体韧性 (77.780 5, 3.358 7, 0.705 8)
建筑附属设备韧性 (52.133 4, 3.696 8, 1.210 2)
设施韧性 (39.513 4, 6.552 0, 1.040 5)应急设施韧性 (53.083 9, 6.037 8, 1.174 3)
安防设施韧性 (38.503 0, 6.510 3, 1.075 3)
服务设施韧性 (20.169 0, 7.815 1, 0.671 1)
环境韧性 (55.545 0, 5.894 2, 0.711 5)室内环境韧性 (72.229 9, 5.113 4, 0.882 1)
室外环境韧性 (47.975 3, 6.054 9, 0.676 4)
人员韧性 (43.856 1, 7.108 9, 0.731 1)居住人员韧性 (61.721 3, 6.124 1, 0.584 6)
管理人员韧性 (34.520 9, 5.795 4, 0.605 7)

Fig.3

Comprehensivecloud map"

1 李辉山, 司尚怡, 白莲. 基于ANP和FCE的老旧小区改造综合效益评价[J]. 工程管理学报, 2021, 35 (3): 76- 81.
LI Huishan , SI Shangyi , BAI Lian . Evaluation on the comprehensive benefit for the reconstruct of old residential area based on AHP and FCE[J]. Journal of Engineering Management, 2021, 35 (3): 76- 81.
2 中华人民共和国国务院办公厅. 政府工作报告[R/OL]. (2019-03-05)[2022-10-24]. http://www.gov.cn/guowuyuan/20919zfgzbg.htm.
3 中华人民共和国国务院办公厅. 国务院办公厅关于全面推进城镇老旧小区改造工作的指导意见[R/OL]. (2020-07-10)[2022-10-24]. http://www.gov.cn/zhengce/content/2020-07/20/content_5528320.htm.
4 张晓东, 胡俊成, 杨青, 等. 基于AHM模糊综合评价法的老旧小区更新评价系统[J]. 城市发展研究, 2017, 24 (12): 20- 22.
ZHANG Xiaodong , HU Juncheng , YANG Qing , et al. Research on the evaluation system of renewal for old residential district based on AHM and comprehensive assessment method[J]. Research on Urban Development, 2017, 24 (12): 20- 22.
5 杨毕红. 突发公共卫生事件下城市社区韧性测度及其影响因素研究: 基于西安市地实证[D]. 西安: 西北大学, 2021.
YANG Bihong. Study on the measurement of urban community resilience and its influence factor under public health emergency—an empirical evidence of Xi'an city[D]. Xi'an: Northwest University, 2021.
6 姜宇逍. 雨洪防涝视角下韧性社区评价体系及优化策略研究[D]. 天津: 天津大学, 2018.
JIANG Yuxiao. Studies on resilience community evaluation systm and improvement strategies on an approach of flood disasters[D]. Tianjin: Tianjin University, 2018.
7 郭小东, 费智涛, 王志涛. 城市灾害应对的刚性、弹性与韧性[J]. 城乡规划, 2021, (3): 35- 42.
GUO Xiaodong , FEI Zhitao , WANG Zhitao . Robustness, flexibility and resilience for urban disaster management[J]. Urban and Rural Planning, 2021, (3): 35- 42.
8 LIU S , WANG Y , LI Z X , et al. Community resilience monitoring and evaluation under the prevention and control of public health emergencies[J]. E3S Web of Confer-ences, 2021, 251 (5): 3013- 3020.
9 XIANG M M , ZHAO W , CHEN J . A comparison of different reconstruction modes and adaptive evaluation systems for community recovery following the Wenchuan earth-quake[J]. Sustainability, 2018, 10 (11): 4115- 4137.
doi: 10.3390/su10114115
10 DMITRY L , MOOLI L , ODEYA C , et al. Conjoint community resiliency assessment measure-28/10 items: a self-report tool for assessing community resilience[J]. American Journal of Community Psycholog, 2013, 52 (3/4): 313- 323.
11 MASKREY S , CUI P , LI D . Measuring the disaster resilience of an urban community using ANP-FCE method from the perspective of capitals[J]. Social Science Quarterly, 2019, 100 (6): 2059- 2077.
doi: 10.1111/ssqu.12699
12 DANIEL L , MAZUMDER R , ENDERAMI A , et al. Community capitals framework for linking buildings and organizations for enhancing community resilience through the built environment[J]. Journal of Infrastructure Systems, 2022, 28 (1): 04021053.
doi: 10.1061/(ASCE)IS.1943-555X.0000668
13 住房和城乡建设部. 民用建筑修缮工程施工标准: JGJ/T 112—2019[S]. 北京: 中国建筑工业出版社, 2019.
14 中关村乐家智慧居住区产业技术联盟. 智慧住区建设评价标准: T/CECS 526—2018[S]. 北京: 中国计划出版社, 2018.
15 李刚, 李建平, 孙晓蕾, 等. 主客观权重的组合方式及其合理性研究[J]. 管理评论, 2017, 29 (12): 17- 26.
LI Gang , LI Jianping , SUN Xiaolei , et al. Research on a combined method of subjective-objective weights and the its rationality[J]. Manage Comments, 2017, 29 (12): 17- 26.
16 李娇利. 基于AHP-熵权法和云模型的绿色建筑可持性评价研究[D]. 南昌: 江西财经大学, 2018.
LI Jiaoli. Study on sustainability evaluation of green building based on AHP-Entropy weight method and cloud model[D]. Nanchang: Jiangxi University of Finance Economics, 2018.
17 魏东泉. 基于熵权TOPSIS城镇老旧小区改造绩效评价研究[J]. 建筑经济, 2022, 43 (增刊1): 606- 609.
WEI Dongquan . Research on performance evaluation of old town community reconstruction based on entropy weight TOPSIS[J]. Construction Economy, 2022, 43 (Suppl. 1): 606- 609.
18 KESHAVARZ-GHORABAEE M , AMIRI M , ZAVADSKAS E K , et al. Determination of objective weights using a new method based on the removal effects of criteria (MEREC)[J]. Symmetry, 2021, 13 (4): 525.
19 陈贵林. 一种定性定量信息转换的不确定性模型-云模型[J]. 计算机应用研究, 2010, 27 (6): 2006- 2010.
CHEN Guilin . Uncertain in model of qualitative/quantitative in formation transformation-cloud model[J]. Computer Application Research, 2010, 27 (6): 2006- 2010.
20 丁华. 基于云模型的装配式混凝土结构施工质量管理评价研究[D]. 湖南: 湖南大学, 2020.
DING Hua. Research on construction quality management evaluation of prefabrication concrete structure based on cloud model[D]. Hunan: Hunan University, 2020.
21 康珅. 基于云模型的装配式建筑施工安全评价研究[D]. 西安: 西安科技大学, 2018.
KANG Shen. Research on construction safety evaluation of prefabrication based on cloud model[D]. Xi'an: Xi'an University of Science and Technology, 2018.
[1] HU Quanguang, CHEN Fangming, NING Guangzhong. Rockburst evaluation model and application of CW-TOPSIS [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(2): 20-25.
[2] LI Xiu-hai1, YU Shao-wei2*. Cloud droplets obtaining algorithm based on normal interval number [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2012, 42(5): 130-134.
[3] YU Shao-wei,CAO Kai,ZHAO Mo . Traffic information forecast algorithm based on the one-dimension cloud model [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2007, 37(2): 121-126 .
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] WANG Su-yu,<\sup>,AI Xing<\sup>,ZHAO Jun<\sup>,LI Zuo-li<\sup>,LIU Zeng-wen<\sup> . Milling force prediction model for highspeed end milling 3Cr2Mo steel[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(1): 1 -5 .
[2] ZHANG Yong-hua,WANG An-ling,LIU Fu-ping . The reflected phase angle of low frequent inhomogeneous[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(2): 22 -25 .
[3] LI Kan . Empolder and implement of the embedded weld control system[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(4): 37 -41 .
[4] SHI Lai-shun,WAN Zhong-yi . Synthesis and performance evaluation of a novel betaine-type asphalt emulsifier[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(4): 112 -115 .
[5] KONG Xiang-zhen,LIU Yan-jun,WANG Yong,ZHAO Xiu-hua . Compensation and simulation for the deadband of the pneumatic proportional valve[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(1): 99 -102 .
[6] LAI Xiang . The global domain of attraction for a kind of MKdV equations[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(1): 87 -92 .
[7] LI Liang, LUO Qiming, CHEN Enhong. Graph-based ranking model for object-level search
[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(1): 15 -21 .
[8] CHEN Rui, LI Hongwei, TIAN Jing. The relationship between the number of magnetic poles and the bearing capacity of radial magnetic bearing[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(2): 81 -85 .
[9] WANG Bo,WANG Ning-sheng . Automatic generation and combinatory optimization of disassembly sequence for mechanical-electric assembly[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(2): 52 -57 .
[10] LI Ke,LIU Chang-chun,LI Tong-lei . Medical registration approach using improved maximization of mutual information[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(2): 107 -110 .