This paper discusses the adoption of Artificial Intelligence-based techniques to estimate seismic damage, not with the goal of replacing existing approaches, but as a mean to improve the precision of empirical methods. For such, damage data collected in the aftermath of the 1998 Azores earthquake (Portugal) is used to develop a comparative analysis between damage grades obtained resorting to a classic damage formulation and an innovative approach based on Artificial Neural Networks (ANNs). The analysis is carried out on the basis of a vulnerability index computed with a hybrid seismic vulnerability assessment methodology, which is subsequently used as input to both approaches. The results obtained are then compared with real post-earthquake damage observation and critically discussed taking into account the level of adjustment achieved by each approach. Finally, a computer routine that uses the ANN as an approximation function is developed and applied to derive a new vulnerability curve expression. In general terms, the ANN developed in this study allowed to obtain much better approximations than those achieved with the original vulnerability approach, which has revealed to be quite non-conservative. Similarly, the proposed vulnerability curve expression was found to provide a more accurate damage prediction than the traditional analytical expressions.

中文翻译：

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使用人工神经网络估算地震破坏并导出传统砌体的脆弱性函数

本文讨论了采用基于人工智能的技术来估计地震破坏，不是为了取代现有方法，而是为了提高经验方法的精度。为此，在1998年亚速尔群岛地震（葡萄牙）之后收集的破坏数据被用于对采用经典破坏公式和基于人工神经网络（ANN）的创新方法获得的破坏等级进行比较分析。该分析是基于使用混合地震易损性评估方法计算出的易损性指数进行的，随后将其用作两种方法的输入。然后将获得的结果与真实的震后损害观察结果进行比较，并认真讨论每种方法所达到的调整水平。最后，开发了一种使用ANN作为逼近函数的计算机例程，并将其应用于导出新的脆弱性曲线表达式。总的来说，本研究开发的人工神经网络能够获得比原始脆弱性方法更好的近似值，后者已被证明是非常不保守的。同样，发现拟议的脆弱性曲线表达式比传统的分析表达式可提供更准确的破坏预测。这项研究开发的ANN所获得的近似值比原始漏洞方法所获得的近似值更好，后者已被证明是非常不保守的。同样，发现拟议的脆弱性曲线表达式比传统的分析表达式可提供更准确的破坏预测。这项研究开发的ANN所获得的近似值比原始漏洞方法所获得的近似值好得多，后者已被证明是非常不保守的。同样，发现拟议的脆弱性曲线表达式比传统的分析表达式可提供更准确的破坏预测。