Please wait a minute...
Journal of ZheJiang University (Engineering Science)  2024, Vol. 58 Issue (9): 1844-1856    DOI: 10.3785/j.issn.1008-973X.2024.09.009
    
Building safety assessment algorithm for self-organizing crowds
Hui WU(),Gaoqi HE*(),Chen LI,Changbo WANG
Department of Computer Science and Technology, East China Normal University, Shanghai 200062, China
Download: HTML     PDF(6000KB) HTML
Export: BibTeX | EndNote (RIS)      

Abstract  

A building safety assessment algorithm for self-organizing crowds was proposed, in order to resolve the problems of unreasonable indoor building nodes and the difficulty in choosing escape routes for crowds. Based on IndoorGML, the horizontal plane structure and vertical hierarchical structure of indoor space were established to form a node-relation graph in the topological space, so as to represent the spatial structure of multi-story buildings. A safe evacuation route selection algorithm based on the A* algorithm was designed, by defining the risk and the traffic rate of unit space. Unreasonable nodes of indoor buildings were found and optimization solutions were proposed, by analyzing the number of people choosing escape routes, the highest risk of nodes on a single escape route, and the average escape time of a single escape route. A simulation scene was constructed for simulation experiments, and the results of different route selection methods were compared to analyze and prove the effectiveness of the proposed assessment algorithm. Results show that the proposed method can be used to find the main factors of building congestion and the degree of influence on evacuation, which is helpful to carry out scientific building safety assessment in the building design stage.



Key wordscrowd simulation      building safety assessment      Indoor GML      self-organized crowd      safe evacuation route selection     
Received: 03 July 2023      Published: 30 August 2024
CLC:  TP 399  
Fund:  国家自然科学基金资助项目(62002121,62072183);重庆市自然科学基金资助项目(CSTB2022NSCQ-MSX0552);上海市科学技术委员会资助项目(21511100700,22511104600);浙江大学CAD&CG国家重点实验室开放课题(A2203)资助项目.
Corresponding Authors: Gaoqi HE     E-mail: 51194501187@stu.ecnu.edu.cn;gqhe@cs.ecnu.edu.cn
Cite this article:

Hui WU,Gaoqi HE,Chen LI,Changbo WANG. Building safety assessment algorithm for self-organizing crowds. Journal of ZheJiang University (Engineering Science), 2024, 58(9): 1844-1856.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2024.09.009     OR     https://www.zjujournals.com/eng/Y2024/V58/I9/1844


面向自组织人群的建筑安全评估算法

为了解决室内建筑节点不合理与人群逃生路线选择困难的问题,提出面向自组织人群的建筑安全评估算法. 基于IndoorGML建立室内空间的水平平面结构和垂直层级结构,形成拓扑空间中的节点关联图,以表示多层建筑物的空间结构. 通过对单元空间的风险、通行率的定义, 设计基于A* 算法的安全疏散路线选择算法. 通过分析逃生路线的选择人数、单条逃生路线上节点的最高风险、单条逃生路线的平均逃生时间, 找出室内建筑不合理节点并提出优化方案. 构建模拟场景进行仿真实验, 对比不同路线选择方法的结果,分析并验证所提评估算法的有效性. 结果表明,所提出的方法能够找出建筑拥堵的主要因素及其对疏散的影响程度,有助于在建筑设计阶段开展科学的建筑安全评估.


关键词: 人群仿真,  建筑安全评估,  Indoor GML,  自组织人群,  安全疏散路线选择 
Fig.1 Overall flowchart of indoor space safety assessment
Fig.2 Process of mapping indoor space structure to dual space
Fig.3 Process of dividing and combining unit spaces into indoor building areas
Fig.4 UML abstraction of indoor space components
Fig.5 Indoor vertical space structure mapping
Fig.6 Overall flow chart of evacuation route selection
Fig.7 Planar structure range division and automatic fusion process
Fig.8 Automatic fusion process diagram of NRGexit
Fig.9 Space modeling of shopping malls
Fig.10 Locations of fire with a single escape route in a horizontal plane structure
逃生路线NuSe 逃生路线NuSe
E11-d105-H12-d12012 R12-d102-H11-d11511
E12-d106-H12-d12011R12-d102-H11-g11-G11-d1171
E12-d106-H12-h12-H11-h13-H13-d1161R12-d102-H11-h13-H13-d11610
G11-d11710R13-d107-H11-d11916
G11-d1185R13-d107-H11-g11-G11-d1171
H11-d1153R13-d107-H11-h12-H12-d1202
H11-d1192R14-d108-H11-d11927
H11-g11-G11-d1175R14-d108-H11-g11-G11-d1171
H11-g11-G11-d1183R14-d108-H11-h12-H12-d1201
H11-h12-H12-d1204R14-d108-H11-h13-H13-d1162
H11-h13-H13-d11612R15-d109-H11-d11915
H12-d12019R15-d109-H11-h13-H13-d1161
H13-d1169R16-d111-H11-d11514
H13-h13-H11-d1155R16-d111-H11-h13-H13-d11610
H13-h13-H11-d1192R17-d114-H11-d11917
L11-d113-H13-d11612R17-d114-H11-g11-G11-d1181
L11-d113-H13-h13-H11-d1158R17-d114-H11-h13-H13-d1161
R11-d101-H11-d11516S11-d103-H12-d12025
R11-d101-H11-h13-H13-d1165总计:37条路径300
Tab.1 Comparison table of number of people choosing escape routes
开始房间逃生路线MAXRTAvT/s
R12R12-d102-H11-d1150.0228.51
R12-d102-H11-g11-G11-d1170.5635.94
R12-d102-H11-h13-H13-d116026.68
R12-d102-H11-d115 (Dijkstra)0.0221.35
R13R13-d107-H11-d119035.12
R13-d107-H11-g11-G11-d1170.3325.13
R13-d107-H11-h12-H12-d1200.4131.53
R13-d107-H11-h12-H12-d120 (Dijkstra)0.4120.30
R14R14-d108-H11-d119025.57
R14-d108-H11-g11-G11-d1170.4635.40
R14-d108-H11-h12-H12-d1200.5542.14
R14-d108-H11-h13-H13-d1160.4234.57
R14-d108-H11-d119 (Dijkstra)07.46
H11H11-d1150.4927.44
H11-d1190.5046.52
H11-g11-G11-d1170.4521.27
H11-g11-G11-d1180.4816.54
H11-h12-H12-d1200.3326.69
H11-h13-H13-d1160.3121.06
H11-d115 (Dijkstra)0.4913.88
H11-d119 (Dijkstra)0.5010.10
H11-h12-H12-d120 (Dijkstra)0.335.86
Tab.2 Escape routes, danger levels and average escape time for rooms R12, R13 and R14
Fig.11 Average evacuation time proportion of each node on escape path
Fig.12 Variation of congestion index of each node of a single escape route with time
逃生路线te/s
优化前优化后
H11-d11527.4426.26
H13-h13-H11-d11530.1325.85
L11-d113-H13-h13-H11-d11535.6334.55
R11-d101-H11-d11515.2016.39
R12-d102-H11-d11528.5125.40
R16-d111-H11-d11523.5617.84
Tab.3 Comparison of average escape schedule of evacuation routes for d115
Fig.13 Path congestion index variation of optimized R12→H11→H13→D116 over time
[1]   WANG X, ZHENG X, CHENG Y Evacuation assistants: an extended model for determining effective locations and optimal numbers[J]. Physical A: Statistical Mechanics and its Applications, 2012, 391 (6): 2245- 2260
doi: 10.1016/j.physa.2011.11.051
[2]   HELBING D, MOLNAR P Social force model for pedestrian dynamics[J]. Physical Review E, 1995, 51 (5): 4282- 4286
doi: 10.1103/PhysRevE.51.4282
[3]   VAN DEN BERG J, GUY S, LIN M, et al Reciprocal n-body collision avoidance[J]. Springer Tracts in Advanced Robotics, 2011, 70: 3- 19
[4]   WU W, LI J, YI W, et al Modeling crowd evacuation via behavioral heterogeneity-based social force model[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23: 1- 11
doi: 10.1109/TITS.2022.3232999
[5]   MAKMUL J A social force model for pedestrians’ movements affected by smoke spreading[J]. Modelling and Simulation in Engineering, 2020, 8819076: 1- 11
[6]   SNAPE J, VAN DEN BERG J, GUY S, et al The hybrid reciprocal velocity obstacle[J]. IEEE Transactions on Robotics, 2011, 27: 696- 706
doi: 10.1109/TRO.2011.2120810
[7]   GONON D, PAEZ GRANADOS D, BILLARD A Robots’ motion planning in human crowds by acceleration obstacles[J]. IEEE Robotics and Automation Letters, 2022, 7 (4): 11236- 11243
doi: 10.1109/LRA.2022.3199818
[8]   WANG H, LIU Y, ZHOU L Research on an improved cellular automata model[J]. Applied Mechanics and Materials, 2012, 160: 109- 114
doi: 10.4028/www.scientific.net/AMM.160.109
[9]   ANIFOWOSE M, SAID I, ISMAIL R Assessment of building security cost determinants effects[J]. ARPN Journal of Engineering and Applied Sciences, 2015, 10: 6710- 6718
[10]   ADELSBERGER Z, GRUBOR G, NAD I Methodological approach to risk assessment in building security[J]. Collegium Antropologicum, 2014, 38: 215- 227
[11]   ÖKSüZ N K, TANYER A M, PEKERIçLI M K Fuzzy-based escape route fire-vulnerability assessment model for indoor built environment[J]. Indoor and Built Environment, 2022, 32 (1): 116- 132
[12]   MIKOLAI I, TKáč J Escape route typology[J]. Applied Mechanics and Materials, 2016, 820: 402- 407
doi: 10.4028/www.scientific.net/AMM.820.402
[13]   ZHANG L, WANG X, FANG H, et al Numerical study of the effect of gender composition and partitioning boards on evacuation in a two-line transfer transit rail subway station[J]. Indoor and Built Environment, 2022, 31 (7): 1858- 1873
doi: 10.1177/1420326X221079215
[14]   SONG Y, NIU L, LIU P, et al Fire hazard assessment with indoor spaces for evacuation route selection in building fire scenarios[J]. Indoor and Built Environment, 2021, 31 (2): 452- 465
[15]   MALHOTRA A, RAMING S, FRISCH J, et al Open-source tool for transforming citygml levels of detail[J]. Energies, 2021, 14 (24): 8250
doi: 10.3390/en14248250
[16]   BLANC N, CANNATA M, MAXIME C, et al. Ogc api state of play: a practical testbed for the national spatial data infrastructure in switzerland [EB/OL]. [2023-06-01]. https://api.semanticscholar.org/CorpusID:251416090.
[17]   KONDE A, TAUSCHER H, BILJECKI F, et al Floor plans in citygml[J]. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018, 4: 25- 32
[18]   LI K J Indoorgml: a standard for indoor spatial modeling[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, 41 (B4): 701- 704
[19]   DIAKITé A, ZLATANOVA S, LI K J About the subdivision of indoor spaces in indoorgml[J]. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017, 4: 41- 48
[20]   ALATTAS A, ZLATANOVA S, OOSTEROM P, et al Supporting indoor navigation using access rights to spaces based on combined use of indoorgml and ladm models[J]. ISPRS International Journal of Geo-Information, 2017, 6 (12): 384
doi: 10.3390/ijgi6120384
[21]   KIM S H, LI K J, CHO H G A flexible framework for covering and partitioning problems in indoor spaces[J]. ISPRS International Journal of Geo-Information, 2020, 9 (11): 618
doi: 10.3390/ijgi9110618
[22]   CURTIS S, BEST A, MANOCHA D. Menge: a modular framework for simulating crowd movement [J]. Collective Dynamics , 2016, 1: 1−40.
[23]   KIM S, BERA A, BEST A, et al. Interactive and adaptive data-driven crowd simulation [C]// 2016 IEEE Virtual Reality . Greenville: [s.n.], 2016: 29−38.
[24]   GOLAS A, NARAIN R, CURTIS S, et al Hybrid long-range collision avoidance for crowd simulation[J]. IEEE Transactions on Visualization and Computer Graphics, 2014, 20 (7): 1022- 1034
[25]   NARANG S, BEST A, CURTIS S, et al Generating pedestrian trajectories consistent with the fundamental diagram based on physiological and psychological factors[J]. PLOS ONE, 2015, 10: e0117856
doi: 10.1371/journal.pone.0117856
[26]   BEST A, CURTIS S, KASIK D, et al Ped-air: a simulator for loading, unloading, and evacuating aircraft[J]. Transportation Research Procedia, 2014, 2: 273- 281
doi: 10.1016/j.trpro.2014.09.052
[27]   CUESTA A, ABREU O, BALBOA A, et al Real-time evacuation route selection methodology for complex buildings[J]. Fire Safety Journal, 2017, 91: 947- 954
doi: 10.1016/j.firesaf.2017.04.011
[28]   NIU L, WANG Z, SONG Y, et al An evaluation model for analyzing robustness and spatial closeness of 3d indoor evacuation networks[J]. ISPRS International Journal of Geo-Information, 2021, 10: 331
doi: 10.3390/ijgi10050331
[29]   SINPAN N, SASITHONG P, CHAUDHARY S, et al. Simulative investigations of crowd evacuation by incorporating reinforcement learning scheme [C]// Proceedings of the 6th International Conference on Algorithms, Computing and Systems . [s.l.]: Association for Computing Machinery, 2022: 1−5.
[1] Jinye LI,Yongqiang LI. Spatial-temporal multi-graph convolution for traffic flow prediction by integrating knowledge graphs[J]. Journal of ZheJiang University (Engineering Science), 2024, 58(7): 1366-1376.
[2] Qing-lin AI,Jia-hao YANG,Jing-rui CUI. Small target pedestrian detection based on adaptive proliferation data enhancement and global feature fusion[J]. Journal of ZheJiang University (Engineering Science), 2023, 57(10): 1933-1944.
[3] An-teng LI,Peng-jie CUI,Ye YUAN,Guo-ren WANG. Research on subgraph matching optimization based on GPU[J]. Journal of ZheJiang University (Engineering Science), 2023, 57(9): 1856-1864.
[4] Jian-ping HUANG,Ke CHEN,Jian-song ZHANG,Si-qi SHEN. Time-series gene driven feature representation model[J]. Journal of ZheJiang University (Engineering Science), 2023, 57(7): 1354-1364.
[5] Qing-lin AI,Jing-rui CUI,Bing-hai LV,Tong TONG. Surface defect detection method for bearing drum-shaped rollers based on fusion transformation of defective area[J]. Journal of ZheJiang University (Engineering Science), 2023, 57(5): 1009-1020.
[6] Yong-sheng WANG,Shi-jie GUAN,Li-min LIU,Jing GAO,Zhi-wei XU,Guang-wen LIU. Wind power prediction method based on XGBoost extended financial factor[J]. Journal of ZheJiang University (Engineering Science), 2023, 57(5): 1038-1049.
[7] Jing-jing ZHANG,Zhao-gong ZHANG,Xin XU. Graph convolution collaborative filtering model combining graph enhancement and sampling strategies[J]. Journal of ZheJiang University (Engineering Science), 2023, 57(2): 243-251.
[8] Jian-sha LU,Qin BAO,Hong-tao TANG,Yi-ping SHAO,Wen-bin ZHAO. Optimal tag selection method for device-free human tracking system[J]. Journal of ZheJiang University (Engineering Science), 2023, 57(2): 415-425.
[9] Xue-jiao LIU,Hui-min WANG,Ying-jie XIA,Si-wei ZHAO. Task allocation method for Internet of vehicles spatial crowdsourcing with privacy protection[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(7): 1267-1275.
[10] Yu CHEN,Hua DAI,Bo-han LI,Geng YANG. Loose infection pattern mining algorithms over moving objects[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(2): 280-287.
[11] He-xiang LIN,Lian-peng QIAO,Ye YUAN,Guo-ren WANG. Keyword search algorithm of large graph based on GPU[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(2): 271-279.
[12] Xin-yu HUANG,Fan YOU,Pei ZHANG,Zhao ZHANG,Bai-li ZHANG,Jian-hua LV,Li-zhen XU. Silent liveness detection algorithm based on multi classification and feature fusion network[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(2): 263-270.
[13] Xiu-bo LIANG,Jun-han WU,Yu ZHAO,Ke-ting YIN. Review of blockchain data security management and privacy protection technology research[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(1): 1-15.
[14] Jing HUANG,Shu-yuan ZHONG,Yuan-qiao WEN,Kun LUO. Adaptive graph generation jump network for traffic flow prediction[J]. Journal of ZheJiang University (Engineering Science), 2021, 55(10): 1825-1833.
[15] Zhi-chao CHEN,Hai-ning JIAO,Jie YANG,Hua-fu ZENG. Garbage image classification algorithm based on improved MobileNet v2[J]. Journal of ZheJiang University (Engineering Science), 2021, 55(8): 1490-1499.