- 著者
- 鳥澤 一晃 松岡 昌志 堀江 啓 井ノ口 宗成 山崎 文雄
- 出版者
- 公益社団法人 日本地震工学会
- 雑誌
- 日本地震工学会論文集 (ISSN:18846246)
- 巻号頁・発行日
- vol.21, no.5, pp.5_98-5_118, 2021 (Released:2021-11-30)
- 参考文献数
- 52
本研究では,2016年熊本地震の熊本県益城町および宇城市における罹災証明データを統合し,推定地震動分布と組み合わせて,構造別・建築年代別の建物被害関数を構築した.相関係数はすべての分類で0.9前後の強い正の相関を示し,広範囲の地震動で熊本地震の実被害率を説明可能である高精度な被害関数が得られた.木造建物を対象として,既往の被害関数と比較を行ない,被害関数構築に使われた被害調査データの違いや地震が発生した地域の違いなどに基づき,予測結果の傾向の違いやその要因を考察して,本研究で構築した建物被害関数の妥当性について検証した.
- 著者
- 高島 正典 林 春男 田中 聡 重川 希志依 牧 紀男 田村 圭子 堀江 啓 吉富 望 浦川 豪 藤春 兼久 佐藤 翔輔 木村 玲欧
- 出版者
- 一般社団法人 地域安全学会
- 雑誌
- 地域安全学会論文集 = Journal of social safety science (ISSN:13452088)
- 巻号頁・発行日
- vol.7, pp.151-160, 2005-11-01
- 参考文献数
- 16
- 被引用文献数
- 3
<p>We examined the effectiveness of Service Management Framework in designing counter operations in disaster victim support through the hands-on support activity for Ojiya city's victim certification after Niigata-ken Chuetsu Earthquake, Oct. 23, 2004. The service package and the Service Delivery System for the counter operation of victim certificate issuance was designed and implemented on the basis of Service Management Framework. As a result of customer satisfaction survey on Ojiya city and Kawaguchi town, a neighbouring town also affected in the event, it was clarified that the counter operation of Ojiya city was evaluated higher in terms of simplicity of the procedure by the victims than that of Kawaguchi town.</p>
- 著者
- 石井 友 松岡 昌志 牧 紀男 堀江 啓 田中 聡
- 出版者
- 日本建築学会
- 雑誌
- 日本建築学会構造系論文集 (ISSN:13404202)
- 巻号頁・発行日
- no.751, pp.1391-1400, 2018-09
- 被引用文献数
- 9
If a disaster such as an earthquake occurs, buildings will suffer damages, including residential houses and public facilities. An investigation of damaged buildings is very important in disaster areas because we use such data to make decisions for the implementation of disaster management and restoration plans. However, in the event of a large-scale disaster, conducting a detailed survey has several problems. The number of buildings to be covered will increase, manpower will be insuffficient, the burden on workers will increase, restoration will take time and will be delayed. Therefore, there is a need for a quick and accurate method of investigating building damages.<br><br> In this study, we allowed a CNN (convolutional neural network) to learn the local and aerial photographs of the 1995 Kobe earthquake and verified the possibility of assessing building damages in the CNN based on the learning curve and discrimination accuracy. The Nishinomiya Built Environment Database, which contained damage certificate data, aerial and field photographs, and their shooting points, was used for analysis. In the Nishinomiya city's damage certificate data, the damaged buildings were classified into four classes: “severe,” “moderate,” “slight,” and “undamaged.” However, in the present study, three classes—moderate, slight, and undamaged—were merged into a single class for simplicity, such that we had a two class classification problem, that is, “severe” and “others.”<br><br> First, when we created a data set using the damage certificate data, and aerial and field photographs, and allowed the CNN to learn them, a state called over-fitting was created, which made normal learning more difficult. However, as a result of countermeasures called data incrimination, we were able to obtain a estimation accuracy of approximately 63.6% in the aerial photographs and 73.6% in the field photographs. Since the decrease in the accuracy is due to building internal damages, we should also include the possibility of such damages that could not be assessed from the appearance alone, and of the images of damaged buildings from outside the target building; therefore, we investigated and verified the damaged buildings again based on the “images of damaged buildings evaluated by visual interpretation.” Then, it became clear that the damaged buildings can be identified with an accuracy of 86.0% in the aerial photographs and 83.0% in the field photographs. Furthermore, in the field photographs, it became clear that collapsed buildings can be distinguished with a high accuracy of 98.5%.<br><br> From the above results, it was found that it is possible to assess the condition of damaged buildings by deep learning using field and aerial photographs taken in the affected area after the earthquake; however, the damage that can be identified with the highest accuracy is limited to the photographs of collapsed buildings. In our future research, we plan to correctly identify the difference between “moderate” and “slight” damaged buildings.
- 著者
- 越智 祐子 堀江 啓 立木 茂雄
- 出版者
- 一般社団法人 地域安全学会
- 雑誌
- 地域安全学会論文集 (ISSN:13452088)
- 巻号頁・発行日
- vol.7, pp.79-86, 2005-11-11 (Released:2019-01-19)
- 参考文献数
- 22
Monument construction was proposed as a new index on community recovery from the Hanshin-Awaji earthquake disaster. Three facilitating and two suppressing factors of monument construction were identified by using GIS and multiple regression analysis method. Three facilitating factors were that there was earthquake caused death, evacuation shelters and urban redeveloped. Two suppressing factors were that there was urban redevelopment where was earthquake caused death and temporary housing. The clue of community recovery can be obtained by paying attention to whether monument was constructed or not.