結合深度學習與房屋街景圖像之機率式地震風險評估

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姓名

吳念穎(Nien-Ying Wu) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

結合深度學習與房屋街景圖像之機率式地震風險評估
(Probabilistic earthquake hazard assessment combining deep learning and street view images of buildings)

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至系統瀏覽論文 (2025-8-1以後開放)

摘要(中)

台灣位於板塊交界處，地震頻繁發生，人民的生命財產安全受到
危害，房屋的耐震能力面臨巨大挑戰。為應對地震所帶來的風險，本
研究提出一結合深度學習與房屋街景圖像之機率式地震風險評估方
法，針對地震高風險區域建築之耐震能力作進一步的分析與探討，減
少地震所帶來的危害。以傳統方法進行建築物耐震分析，成本較高且
相當費時，倘若要進行大範圍風險評估此法不可行。因此本研究嘗試
透過深度學習獲取建築物高度，藉由地震工程模擬軟體建立數值模
型，模擬結構之受震情形。本研究採用機率式建物損害耐震評估架構
進行非線性動力歷時分析，損害判定準則參考 FEMA 技術報告之
Hazus 4.2-SP3 RC 結構損害等級，統計回歸成損害等級易損曲線後計
算建築物損害機率，以此方法量化建築物受震時所造成的損害程度。
最後本研究繪製花蓮縣之地震風險評估地圖，與 0403 花蓮大地震之
實際災情比較與驗證該方法之可行性，期望可做為日後機率式地震風
險評估之建立標準流程與範本。

摘要(英)

Taiwan is located at the intersection of tectonic plates, resulting in
frequent earthquakes that threaten the safety of people′s lives and property.
The seismic resilience of buildings faces significant challenges. To address
the risks posed by earthquakes, this study proposes a probabilistic seismic
risk assessment method that combines deep learning with street view
images of buildings. This method aims to further analyze and evaluate the
seismic resilience of buildings in high-risk earthquake areas, thereby
mitigating the damage caused by earthquakes.Traditional methods for
seismic analysis of buildings are costly and time-consuming, making them
impractical for large-scale risk assessments. Therefore, this study attempts
to use deep learning to obtain building heights and employs seismic
engineering simulation software to create numerical models that simulate
the structural response to earthquakes.The study adopts a probabilistic
seismic damage assessment framework to perform nonlinear dynamic
time-history analysis. The damage assessment criteria refer to the Hazus
4.2-SP3 RC structure damage levels from the FEMA technical report. By
statistically regressing the damage levels into fragility curves, the study
calculates the probability of building damage. This method quantifies the
extent of damage caused by earthquakes to buildings.Finally, this study
maps the seismic risk assessment of Hualien County and compares it with
the actual damage from the April 3 Hualien earthquake to verify the
feasibility of the proposed method. It is hoped that this can serve as a
standard procedure and template for probabilistic seismic risk assessment
in the future.

關鍵字(中)

★ 深度學習
★ 開放式街景地圖
★ 開放式地震工程模擬軟體
★ 機率式評估法

關鍵字(英)

★ Deep Learning
★ OpenStreetMap
★ OpenSees
★ Probabilistic Assessment Method

論文目次

第一章緒論 1
1-1 研究背景 1
1-2 研究動機與目的 2
1-3 文獻回顧 3
1-4 論文架構 8
第二章深度學習模型 10
2-1 深度學習於語意切割之應用 10
2-1-1SegNet 12
2-1-2FCN: 14
2-1-3Unet: 16
2-2 遷移學習 19
2-3 訓練資料集 20
2-4 模型介紹與參數設定 22
2-4-1Sigmoid: 23
2-4-2Tan H: 24
2-4-3ReLU: 25
2-5 語意切割模型驗證 27
第三章基於街景圖之建築物高度預測與評估 28
3-1 針孔成像原理 28
3-2 相機模型 29
3-3 建築物像素高度提取 31
3-4 街景圖拍攝距離計算 32
3-5 建築物高度估計 35
3-6 誤差來源探討 42
第四章基於數據驅動鋼筋混凝土構架模型建模 43
4-1 數據驅動鋼筋混凝土構架 43
4-2 OpenSees 44
4-3 OpenSees 架構 44
4-4 OpenSees 梁柱元素 46
4-5 建模指令介紹 46
4-6 動力分析 49
4-7 ModIMK 衰退模型53
4-8 鋼筋混凝土架構之多自由度簡化系統 55
第五章機率式地震風險評估之應用與探討 58
5-1 地震歷時選擇標準 61
5-2 定義不同程度之損害判斷標準 62
5-3 增量動力分析 63
5-4 結構損傷易損曲線之建立 65
5-5 花蓮地震之區域性機率式地震風險評估 72
第六章結論與未來展望 78
6-1 結論 78
6-2 未來展望 80
參考文獻 82

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指導教授

陳鵬宇(Peng-Yu Chen)

審核日期

2024-7-23

推文