Estimating the resilience of civil infrastructures is crucial for disaster prevention (i.e. earthquakes), encompassing both above- and underground constructions. However, while below-ground infrastructures are generally acknowledged as less vulnerable than their over-ground counterparts, this aspect has not yet garnered widespread attention. Thus, noting the limited number of seismic response comparisons for underground structures and the virtual absence of comparative analysis between above- and below-ground infrastructures in the scientific literature, this work aims to address this research gap. Nevertheless, data scarcity strongly hampers this endeavour. Not only do very few tunnels have permanent dynamic monitoring systems installed, but even fewer recorded major earthquakes are in proximity to similarly instrumented bridges and viaducts. This study focuses on three infrastructures of the San Francisco Bay Area: the Bay Bridge, the Caldecott Tunnel and the Transbay Tube. The chosen infrastructures represent a unique combination of nearby, continuously monitored case studies in a seismic zone. Yet, even for these selected infrastructures, few comparable data are available – e.g., only one earthquake was recorded for all three. Hence, a Conditional Generative Adversarial Network (CGAN) technique is put forward as a strategy to build a hybrid dataset, thereby incrementing the available data and overcoming the data scarcity issue. The CGAN can generate new data that resemble the real ones while simultaneously comparing different datasets via binary classification. With this dual objective in mind, the CGAN algorithm has been applied to various cases, varying the input given in terms of selected acquisition channels, infrastructure pairs, and selected strong motions. In conclusion, each pair underwent a postprocessing phase to analyse the results. This research’s outcomes show that the classifications performed with the Support Vector Machine reached excellent results, with an average of 91.6% accuracy, 93.1% precision, 93.3% recall, and 92.9% F1 score. The comparison in the time and frequency domain confirms the resemblance.

中文翻译：

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用于地上和地下基础设施的数据生成和地震分析的条件生成对抗网络


估计民用基础设施的弹性对于防灾（即地震）至关重要，包括地上和地下建筑。然而，虽然人们普遍认为地下基础设施比地上基础设施更不脆弱，但这方面尚未引起广泛关注。因此，注意到地下结构的地震响应比较数量有限，并且科学文献中实际上缺乏地上和地下基础设施之间的比较分析，这项工作旨在解决这一研究差距。然而，数据稀缺严重阻碍了这项工作。不仅很少有隧道安装了永久性动态监测系统，而且在类似仪表的桥梁和高架桥附近记录的大地震就更少了。本研究侧重于旧金山湾区的三个基础设施：海湾大桥、凯迪克隧道和 Transbay 管道。所选的基础设施代表了地震带中附近持续监测案例研究的独特组合。然而，即使对于这些选定的基础设施，也很少有可比数据可用——例如，所有三个基础设施都只记录了一次地震。因此，提出了一种条件生成对抗网络 （CGAN） 技术作为构建混合数据集的策略，从而增加可用数据并克服数据稀缺问题。CGAN 可以生成类似于真实数据的新数据，同时通过二进制分类比较不同的数据集。考虑到这一双重目标，CGAN 算法已应用于各种情况，根据选定的采集通道、基础设施对和选定的强运动来改变给定的输入。 总之，每对都经过了后处理阶段以分析结果。本研究结果表明，使用支持向量机进行的分类达到了优异的结果，平均准确率为 91.6%，准确率为 93.1%，召回率为 93.3%，F1 评分为 92.9%。时域和频域中的比较证实了相似性。