The paper presents a machine-learning based framework, named VULMA (VULnerability analysis using MAchine-learning), for vulnerability analysis of existing buildings. The underlying idea is to provide an indication of the seismic vulnerability by exploiting available photographs, which can be properly processed to provide some input data for empirical vulnerability algorithms. To this scope, a complete processing pipeline has been defined, which consists in four consecutive modules offering different and specific services. The first module, Street VULMA, performs the image gathering starting from the raw data; the second module, Data VULMA, provides a mean for the data labelling and storage; the third module, Bi VULMA, uses the collected data to train several machine-learning models for image classification; the fourth module, In VULMA, performs a ranking of the images, their analysis and consequently assigns the vulnerability index. The proposed procedure has been employed on the existing building portfolio in an extended area of the municipality of Bisceglie, Puglia, Southern Italy, for which all the modules have been tested and, above all, the machine-learning models of Bi VULMA have been trained. After, in order to test the efficiency and the reliability of the proposed tools, the entire procedure has been applied on five case study buildings. The results in terms of vulnerability index have been compared with the manual computations performed by the authors applying the same algorithm. Despite the proposed tool could be improved or modified in some of its modules, the obtained results show a good effectiveness of VULMA, which opens new scenarios in the field of vulnerability assessment procedures and risk mitigation strategies.

本文提出了一个基于机器学习的框架，名为VULMA（使用机器学习的漏洞分析），用于现有建筑物的漏洞分析。基本思想是通过利用可用的照片来提供地震脆弱性的指示，可以适当地处理这些照片以为经验脆弱性算法提供一些输入数据。为此，定义了一个完整的处理管道，它由提供不同和特定服务的四个连续模块组成。第一个模块Street VULMA从原始数据开始执行图像收集；第二个模块Data VULMA为数据标记和存储提供了一种手段；第三个模块，Bi VULMA, 使用收集到的数据来训练多个机器学习模型进行图像分类；第四个模块，在 VULMA 中，对图像进行排序，对其进行分析，并因此分配漏洞指数。提议的程序已用于意大利南部普利亚大区 Bisceglie 市扩展区域的现有建筑组合，其中所有模块都经过测试，最重要的是Bi VULMA的机器学习模型受过培训。之后，为了测试所提出工具的效率和可靠性，整个程序已应用于五个案例研究建筑物。脆弱性指数方面的结果已与作者应用相同算法执行的手动计算进行了比较。尽管所提出的工具可以在其某些模块中进行改进或修改，但所获得的结果显示了VULMA的良好有效性，它在漏洞评估程序和风险缓解策略领域开辟了新的场景。