Towards rapid and automated vulnerability classification of concrete buildings
Towards rapid and automated vulnerability classification of concrete buildings
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DOI：
10.1007/s11803-023-2171-2
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发表时间：
2023-04-25
影响因子：
2.8
通讯作者：
中科院分区：
文献类型：
--
作者：
由于城市中有大量的旧钢筋混凝土建筑需要进行地震易损性评估，地方政府面临着如何评估其建筑库存的问题。通过利用以数字格式存储的工程图纸，一种用于对钢筋混凝土建筑物进行地震易损性分类的成熟方法，以及机器学习技术，我们开发了一种从图纸中自动提取定量信息以对易损性进行分类的技术。使用这种技术，利益相关者将能够根据建筑物的地震脆弱性对其进行快速分类，并获得他们所需的信息，以确定大型建筑物清单的优先级。这种方法有可能对我们迅速做出与社区改造和改善有关的决策的能力产生重大影响。据估计，仅在洛杉矶县就有几千座这种类型的建筑。由于哈桑指数在钢筋混凝土建筑物易损性分类中应用简单，本文采用该指数作为自动化方法。本文将介绍用于自动信息提取的技术来计算哈桑指数的大型建筑库存。
With the overwhelming number of older reinforced concrete buildings that need to be assessed for seismic vulnerability in a city, local governments face the question of how to assess their building inventory. By leveraging engineering drawings that are stored in a digital format, a well-established method for classification reinforced concrete buildings with respect to seismic vulnerability, and machine learning techniques, we have developed a technique to automatically extract quantitative information from the drawings to classify vulnerability. Using this technique, stakeholders will be able to rapidly classify buildings according to their seismic vulnerability and have access to information they need to prioritize a large building inventory. The approach has the potential to have significant impact on our ability to rapidly make decisions related to retrofit and improvements in our communities. In the Los Angeles County alone it is estimated that several thousand buildings of this type exist. The Hassan index is adopted here as the method for automation due to its simple application during the classification of the vulnerable reinforced concrete buildings. This paper will present the technique used for automating information extraction to compute the Hassan index for a large building inventory.
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影响因子：
3.1
作者：
Buslaev, Alexander;Iglovikov, Vladimir I.;Kalinin, Alexandr A.
通讯作者：
Kalinin, Alexandr A.
DOI：
10.1061/(asce)cp.1943-5487.0000942
发表时间：
2021-03-01
影响因子：
6.9
作者：
Kim, Seongyong;Park, Seula;Yu, Kiyun
通讯作者：
Yu, Kiyun
影响因子：
6.4
作者：
Gimenez, Lucile;Hippolyte, Jean-Laurent;Zreik, Khaldoun
通讯作者：
Zreik, Khaldoun
DOI：
10.1016/j.engappai.2018.09.011
发表时间：
2019-01-01
影响因子：
8
作者：
Kestur, Ramesh;Meduri, Avadesh;Narasipura, Omkar
通讯作者：
Narasipura, Omkar
影响因子：
2.7
作者：
Gehan MA;Fahlgren N;Abbasi A;Berry JC;Callen ST;Chavez L;Doust AN;Feldman MJ;Gilbert KB;Hodge JG;Hoyer JS;Lin A;Liu S;Lizárraga C;Lorence A;Miller M;Platon E;Tessman M;Sax T
通讯作者：
Sax T