| 研究生: |
朱峻平 Chun-Ping Chu |
|---|---|
| 論文名稱: |
3D嚴肅遊戲式災害決策輔助模型:以TELES模擬台北市某區域地震災害事件之演練腳本產生與災情回報 3D Serious Game-based Decision Support Model for Disaster Response: Generation of Earthquake Drills and Status Quo Reporting for an Area in the City of Taipei |
| 指導教授: |
周建成
Chien-Cheng Chou |
| 口試委員: | |
| 學位類別: |
博士 Doctor |
| 系所名稱: |
工學院 - 土木工程學系 Department of Civil Engineering |
| 論文出版年: | 2017 |
| 畢業學年度: | 105 |
| 語文別: | 中文 |
| 論文頁數: | 157 |
| 中文關鍵詞: | 減災 、演練腳本產生器 、Unity 、TELES 、HAZUS |
| 外文關鍵詞: | Disasters mitigation, Generation of drill scripts, Unity, TELES, HAZUS |
| 相關次數: | 點閱:10 下載:0 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
依據世界銀行「天然災害熱點:全球風險分析(Natural Disaster Hotspots: A Global Risk Analysis)」報告指出,台灣是全球最易遭受天然災害威脅的地區之一。事實上,台灣的公私部門每年皆會共同籌備並實施各項的災害防救演練計畫,希望以此能減輕災害的衝擊與影響,因此,為增進防救災單位的應變能力,策劃妥適的災害防救演練活動將扮演著至關重要的角色,然而,目前對於演練策劃而言,設計出一個合理的災害演練腳本卻是一件費時與費力的工作,同時在災害情境想定上亦容易出錯,極需仰賴演練策劃人員的經驗,以及災害管理人員、第一線救災人員的積極參與。此外,根據日本311地震之受災經驗,社區的耐災程度往往與平時的防災演練工作確實與否息息相關。
本研究以地震災害為例發展一套災害決策輔助模型,開發示範系統稱之為地震災害演練腳本產生與模擬系統(EDSS, Earthquake Drills Generation and Simulation System),並選擇台北市信義區為測試範例,模擬地震事件下能自動化產生合理的災害境況,提供輔助防災演練使用。EDSS產生的災害境況援引自災害損失評估的結果,其可由台灣地震損失評估系統(TELES)所提供,同時利用Unity遊戲引擎建構3D嚴肅遊戲,以呈現出不同於以往純文本腳本型式的災害境況。由於EDSS整合災害損失評估以及3D災害境況的生成,因此在真實地震事件下,可將已蒐集之建物災情訊息輸入至EDSS做進一步的分析,經由比對震損評估結果,即可提早發覺可能被忽略未發現之災情,提供災害決策輔助之用。最後,本研究藉由邀請多位資深的災害演練策劃人員、災害指揮官以及現場搶救人員進行專家問卷調查,以驗證與評鑑EDSS的有效性,評鑑結果顯示EDSS對於輔助災害防救演練確實有所幫助,並且具有未來發展潛力。
Taiwan is one of the world’s most disaster-prone regions, as indicated in the World Bank’s report entitled “Natural Disaster Hotspots: A Global Risk Analysis.” In fact, every year Taiwan public and private organizations need to work together to prepare various training programs in hope of mitigating disaster impact. Design of an appropriate disaster drill, hence, plays an important role of enhancing the capability of their emergency response units. However, developing a reasonable scenario for the disaster drill is a time-consuming, error-prone task, and experienced drill designers, disaster management officers and/or first responders are required to join the development work. The level of a community’s disaster preparedness and readiness are also highly dependent on whether such work is performed well.
In this research, an earthquake disaster and an area in the city of Taipei were selected for the demonstration of the proposed decision support model and its system, called EDSS (Earthquake Drills Generation and Simulation System). EDSS requires an input data set containing the impact assessments of an earthquake disaster, which can be obtained from Taiwan Earthquake Loss Estimation System (TELES) and are similar to the results of using HAZUS (Hazards in the U.S., a tool created by FEMA and the base version of TELES). EDSS utilizes Unity, a 3D serious game engine, to actually display the events of an earthquake disaster in accordance with the drill script, which is traditionally shown as plain text. In the designated area, real building geometry and simulated earthquake event are rendered inside Unity; hence, after the occurrence of a real earthquake, building damage information can be collected and entered into EDSS for further analysis, in order to show the remaining, disaster-related events not currently discovered by first responders.
In model validation, senior drill designers, disaster management officers and first responders were invited to evaluate the effectiveness of EDSS, and the results show that EDSS has the potential of improving first responders’ awareness of field conditions as well as helps first responders better understand how a disaster evolves over time.
1. 中国国务院新闻办公室, (2008). 国新办就四川汶川地震及灾损评估情况举行发布会, Online
http://www.scio.gov.cn/photo/5/Document/795733/795733.htm, Last Accessed November, 2015.
2. 內政部統計處, (2002). 淺析臺灣天然災害變動趨勢, Online http://sowf.moi.gov.tw/stat/topic/topic325.htm, Last Accessed June, 2016.
3. 日本警察庁緊急災害警備本部, (2015). 平成23年東北地方太平洋沖地震の被害状況と警察措置, Online
http://www.npa.go.jp/archive/keibi/biki/higaijokyo.pdf, Last Accessed June, 2016.
4. 台北市政府資訊局, (2012). Google Earth與市政結合, Online http://gis.taipei/ct.asp?xItem=996149&ctNode=52159&mp=100056, Last Accessed June, 2016.
5. 尚少華, (2015). 大臺北地區地震後火災風險評估, 博士論文, 國立臺灣科技大學建築系, 台北市, 台灣。
6. 洪泰昌, (2009). 天然氣管線震害災損量化推估與對策, 碩士論文, 國立臺北科技大學土木與防災研究所, 台北市, 台灣。
7. 馬士元, (2013). 應用TELES模擬於國家防災日地震推演規劃之經驗, 2013台灣地震損失評估系統講習會, 國家地震工程研究中心, 台北市, 台灣。
8. 國家地震工程研究中心, (2015). 台灣地震損失評估系統(TELES)使用手冊, 技術文件, 國家地震工程研究中心, 台北市, 台灣。
9. 張申武, (2015). 應用臺灣地震風險評估系統TELES以建立地震保險分級制度之研究-以台南市為例, 碩士論文, 國立嘉義大學土木與水資源工程學系研究所, 嘉義市, 台灣。
10. 郭榮欽, 謝尚賢, (2011). BIM技術與公共工程, 行政院公共工程委員會公共工程電子報, Online http://www.pcc.gov.tw/epaper/10009/bim.htm, Last Accessed June, 2016.
11. 陳明湖, (2005). 地震災損評估系統應用於地區災害防救計劃之研究, 碩士論文, 國立臺北科技大學土木與防災研究所, 台北市, 台灣。
12. 陳道平, (2004). 地震災害區域聯防救災機制之研究, 碩士論文, 國立臺北科技大學土木與防災技術研究所, 台北市, 台灣。
13. 曾一嵐, (2007). 防災生活圈規劃之研究:以竹東鎮為例, 碩士論文, 國立交通大學工學院碩士在職專班產業安全與防災組, 新竹市, 台灣。
14. 黃文思, (2007). 應用臺灣地震損失評估系統加強嘉義市防救災作業能力之研究, 碩士論文, 國立雲林科技大學營建工程系, 雲林縣, 台灣。
15. 黃歆宜, (2010). 應用1941年中埔地震探討台灣地震災害損失評估系統, 碩士論文, 清雲科技大學空間資訊與防災科技研究所, 桃園市, 台灣。
16. 黃麒然, (2010). 考量風險於地震後火災搶救與緊急醫療規劃之研究, 博士論文, 國立臺北科技大學工程科技研究所, 台北市, 台灣。
17. 葉錦勳, (2003). 台灣地震損失評估系統—TELES, 報告編號:NCREE-03-002, 國家地震工程研究中心, 台北市, 台灣。
18. 葉錦勳, (2006). 地震危害度分析與震災境況模擬技術整合研究(I), 報告編號:NCREE-06-015, 國家地震工程研究中心, 台北市, 台灣。
19. 葉錦勳, (2014). 震損評估模式近期研發成果與應用, 2014台灣地震損失評估系統講習會, 國家地震工程研究中心, 台北市, 台灣。
20. 葉錦勳、簡文郁、鍾立來, (2004). 台灣震災早期評估系統之研發與應用, 中國土木水利工程學刊, 16(4), pp. 609-620。
21. 蕭稚燕, (2007). 應用台灣地震損失評估系統於都市土地使用防災策略之研究, 碩士論文, 國立臺北科技大學建築與都市設計研究所, 台北市, 台灣。
22. Anil, E.B., Akinci, B., Kurc, O., and Garrett, J.H., (2015). Building-Information-Modeling–Based Earthquake Damage Assessment for Reinforced Concrete Walls, Journal of Computing in Civil Engineering, 30(4).
23. Charalambos, G., Dimitrios, V., and Symeon, C., (2014). Damage Assessment, Cost Estimating, and Scheduling for Post-Earthquake Building Rehabilitation Using BIM, 2014 International Conference on Computing in Civil and Building Engineering, pp. 398-405, Orlando, USA.
24. Comber, M.V. and Poland, C.D., (2013). Disaster Resilience and Sustainable Design: Quantifying the Benefits of a Holistic Design Approach, Structures Congress 2013, pp. 2717-2728, Pittsburgh, Pennsylvania, USA.
25. El-Anwar, O., El-Rayes, K. and Elnashai, A., (2009). An Automated System for Optimizing Temporary Housing Arrangements after Natural Disasters, Automation in Construction, 18(7), pp. 983-993.
26. Ellingwood, B.R., Rosowsky, D.V. and Pang, W., (2008). Performance of Light-Frame Wood Residential Construction Subjected to Earthquakes in Regions of Moderate Seismicity, Journal of Structural Engineering, 134(8), pp. 1353-1363.
27. Erberik, M.A. and Elnashai, A.S., (2006). Loss Estimation Analysis of Flat-Slab Structures, Natural Hazards Review, 7(1), pp. 26-37.
28. Federal Emergency Management Agency (FEMA), (2013a). Hazus-MH 2.1 Earthquake Model Technical Manual, FEMA, Washington, D.C., USA.
29. Federal Emergency Management Agency (FEMA), (2013b). Hazus-MH 2.1 Advanced Engineering Building Module (AEBM) Technical and User’s Manual, FEMA, Washington, D.C., USA.
30. Federal Emergency Management Agency (FEMA), (2013c). Hazus-MH 2.1 User Manual, FEMA, Washington, D.C., USA.
31. Federal Emergency Management Agency (FEMA), (2013d). HAZUS-MH Used to Support San Francisco Bay Area Earthquake Exercise, FEMA, Washington, D.C., USA.
32. Federal Emergency Management Agency (FEMA), (2015). Hazus-MH Overview, Online https://www.fema.gov/hazus-mh-overview, Last Accessed November, 2015.
33. Frankie, T., Gencturk, B. and Elnashai, A., (2013). Simulation-Based Fragility Relationships for Unreinforced Masonry Buildings, Journal of Structural Engineering, 139(3), pp. 400-410.
34. Gröger, G., Kolbe, T.H., Nagel, C., and Häfele, K., (2012). OGC City Geography Markup Language (CityGML) En-coding Standard. (Version: 2.0.0), Project Document Number: OGC 12-019, Open Geospatial Consortium, Wayland, Massachusetts, USA.
35. Guven, G., Ergen, E., Erberik, M.A., Kurc, O., and Birgonul, M.T., (2012). Providing Guidance for Evacuation during an Emergency Based on a Real-Time Damage and Vulnerability Assessment of Facilities, International Conference on Computing in Civil Engineering, pp. 586-593, Clearwater Beach, USA.
36. Guven, G., Ergen, E., Ozbas, B., Erberik, M.A., Kurc, O., and Birgonul, M.T., (2013). Multisensor Data Fusion for Determining Hallway Blockages in a Building during Evacuation, ASCE International Workshop on Computing in Civil Engineering, pp. 717-724, Los Angeles, USA.
37. Kircher, C.A., Whitman, R.V., and Holmes, W.T., (2006). HAZUS Earthquake Loss Estimation Methods, Natural Hazards Review, 7(2), pp. 45-59.
38. Kolbe, T.H., (2007). CityGML Tutorial, 1st Joint Workshop on the Sino-Germany Bundle Project: Interoperation of 3D Urban Geoinformation, Urumqi, China.
39. Kongar, I., Rossetto, T. and Giovinazzi, S., (2014). The Effectiveness of Existing Methodologies for Predicting Electrical Substation Damage Due to Earthquakes in New Zealand, Second International Conference on Vulnerability and Risk Analysis and Management (ICVRAM) and the Sixth International Symposium on Uncertainty, Modeling, and Analysis (ISUMA), pp. 752-761, Liverpool, UK.
40. Li, Q.J., O’Hara, M. and Wang, K., (2016). Assessment of the Seismic Vulnerability of Transportation Infrastructure in Central Oklahoma, International Conference on Transportation and Development 2016, pp. 706-717, Houston, Texas, USA.
41. Luna, R., Hoffman, D. and Lawrence, W.T., (2008). Estimation of Earthquake Loss due to Bridge Damage in the St. Louis Metropolitan Area. I: Direct Losses, Natural Hazards Review, 9(1), pp. 1-11.
42. Luna, R., Hoffman, D.J. and Lawrence, W.T., (2006). Earthquake Hazard Input for Loss Estimation Study: St. Louis Highway System. GeoCongress 2006, The Geo-Institute of the ASCE, pp. 1-6, Atlanta, Georgia, USA.
43. Ma, L., Sacks, R., Zeibak-Shini, R., Aryal, A., and Filin, S., (2016). Preparation of Synthetic As-Damaged Models for Post-Earthquake BIM Reconstruction Research, Journal of Computing in Civil Engineering, 30(3).
44. Mahaney, J.A., Paret, T.F., Kehoe, B.E. and Freeman, S.A., (1993). The Capacity Spectrum Method for Evaluating Structural Response during the Loma Prieta Earthquake, Proceedings of the 1993 United States National Earthquake Conference, Memphis, Tennessee, 2, pp. 501-510.
45. Margaret, A., Maxx, D., Uwe, D., Robert, S.C., and Arthur, L.L., (2005). National Disaster Hotspots- A Global Risk Analysis, World Bank, Washington, D.C., USA.
46. MASA Group, (2016a). MASA SWORD, Paris, France. Online https://masa-group.biz/products/sword, Last Accessed June, 2016.
47. MASA Group, (2016b). Emergency Preparedness, Paris, France. Online https://masa-group.biz/markets/emergency-preparedness, Last Accessed June, 2016.
48. Menezes, G.B. and Inyang, H.I., (2012). Linking HAZUS-MH Risk-Analysis Methodology to Contaminant-Release Models, Natural Hazards Review, 13(1), pp. 74-81.
49. Merriam-Webster, (2003). Merriam-Webster’s Eleventh Collegiate Dictionary, Springfield, Massachusetts, USA.
50. National Institute of Building Sciences (NIBS), (2007). National Building Information Modeling Standard Version 1 – Part 1: Overview, Principles, and Methodologies, NIBS, Washington, D.C., USA.
51. Neighbors, C.J., Cochran, E.S., Caras, Y. and Noriega, G.R., (2013). Sensitivity Analysis of FEMA HAZUS Earthquake Model: Case Study from King County, Washington, Natural Hazards Review, 14(2), pp. 134-146.
52. Otto, S., (2009). Applications and Challenges to Using HAZUS-MH for Building Seismic Risk Awareness, ATC and SEI Conference on Improving the Seismic Performance of Existing Buildings and Other Structures, pp. 1241-1246, San Francisco, California, USA.
53. Ploeger, S.K., Sawada, M., Elsabbagh, A., Saatcioglu, M., Nastev, M. and Rosetti, E., (2015). Urban RAT: New Tool for Virtual and Site-Specific Mobile Rapid Data Collection for Seismic Risk Assessment, Journal of Computing in Civil Engineering, 30(2), pp. 1-11.
54. Porter, K. and Cobeen, K., (2009). Loss Estimates for Large Soft-Story Woodframe Buildings in San Francisco, ATC and SEI Conference on Improving the Seismic Performance of Existing Buildings and Other Structures, pp. 1191-1203, San Francisco, California, USA.
55. Porter, K., Hellman, S., McLane, T. and Carlisle, C., (2009). End-to-End Seismic Risk Management Software, ATC and SEI Conference on Improving the Seismic Performance of Existing Buildings and Other Structures, pp. 1247-1257, San Francisco, California, USA.
56. Robin, D., (2013). BIM Support for Disaster Response, Proceedings of the 9th Annual International Conference of the International Institute for Infrastructure Renewal and Reconstruction, pp. 391-405, Brisbane, Australia.
57. Rojas, H.A., Pezeshk, S. and Foley, C.M., (2008). Automated Risk-Based Seismic Design Method for Optimal Structural and Non-Structural System Performance, 18th Analysis and Computation Specialty Conference at Structures Congress, pp. 1-11, Vancouver, British Columbia, Canada.
58. Schneider, P.J. and Schauer, B.A., (2006). HAZUS—Its Development and Its Future, Natural Hazards Review, 7(2), pp. 40-44.
59. Tanya B., Rafael S., and Oded R., (2016). Interior models of earthquake damaged buildings for search and rescue, Advanced Engineering Informatics, 30, pp. 65-76.
60. Tokas, C. and Lobo, R., (2009). Risk Based Seismic Evaluation of Pre-1973 Hospital Buildings Using the HAZUS Methodology, ATC and SEI Conference on Improving the Seismic Performance of Existing Buildings and Other Structures, pp. 137-152, San Francisco, California, USA.
61. Tygron, (2010). Tygron Serious Game, Online http://www.tygron.com, Last Accessed November, 2015.
62. United Nations Office for Disaster Risk Reduction (UNISDR), (2015). Global Assessment Report on Disaster Risk Reduction (GAR15), UNISDR, Geneva, Switzerland.
63. Wuensch, Karl L., (2015). What is a Likert Scale? and How Do You Pronounce 'Likert?', Online http://core.ecu.edu/psyc/wuenschk/StatHelp/Likert.htm, Last Accessed June, 2016.
64. Yeh, C.H., Loh, C.H. and Tsai, K.H., (2006). Overview of Taiwan Earthquake Loss Estimation System, Natural Hazards, 37, pp. 23-37.
65. Zeng, X., Xu, Z. and Lu, X., (2015). Regional Seismic Damage Simulation of Buildings: A Case Study of the Tsinghua Campus in China, Computing in Civil Engineering 2015, pp. 507-514, Austin, Texas, USA.