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.

由于城市中需要评估大量老式钢筋混凝土建筑的地震脆弱性，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、针对地震易损性对钢筋混凝土建筑物进行分类的行之有效的方法以及机器学习技术，我们开发了一种自动从图纸中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快速对建筑物进行分类，并获得他们需要的信息来确定大型建筑物清单的优先级。该方法有可能对我们快速做出与社区改造和改善相关的决策的能力产生重大影响。据估计，仅在洛杉矶县就有数千座这种类型的建筑。由于哈桑指数在易损钢筋混凝土建筑物分类过程中应用简单，因此这里采用哈桑指数作为自动化方法。本文将介绍用于自动信息提取以计算大型建筑清单的 Hassan 指数的技术。由于哈桑指数在易损钢筋混凝土建筑物分类过程中应用简单，因此这里采用哈桑指数作为自动化方法。本文将介绍用于自动信息提取以计算大型建筑清单的 Hassan 指数的技术。由于哈桑指数在易损钢筋混凝土建筑物分类过程中应用简单，因此这里采用哈桑指数作为自动化方法。本文将介绍用于自动信息提取以计算大型建筑清单的 Hassan 指数的技术。