论文标题
机器学习在地震脆弱性评估桥梁中的应用
Application of Machine Learning in Seismic Fragility Assessment of Bridges with SMA-Restrained Rocking Columns
论文作者
论文摘要
本文通过机器学习（ML）技术评估了具有形状内存合金（SMA）条件摇摆（SRR）柱的两跨钢筋混凝土（RC）桥的地震脆弱性。 SRR色谱柱结合了可更换超弹性NITI（SMA）链路和低碳钢消耗链路的组合，分别以实现自我核心和能量耗散，而它们的摇摆接头则受到通过钢套的压缩混凝土损害的保护。为了产生地震脆性功能，最初，通过五种不同的ML技术（包括神经网络）为各种工程需求参数生成多参数概率地震需求模型（PSDMS），并考虑了各种不确定性来源，并且选择了最准确的PSDM。然后，使用四种不同的方法来解释所选的PSDM，以研究两个关键的SRR列设计参数（自中心系数和SMA链接初始应变）和环境温度对SRR列的地震性能的影响。随后，使用神经网络，较早开发的PSDM和适当的容量模型，为各种桥梁损坏状态开发了多参数脆性功能。在检查了两个SRR柱设计参数对桥的地震脆弱性的影响之后，将其地震脆弱性与带有整体式RC的同一桥的地震脆弱性进行了比较。结果表明，通常，增加了SMA链接的初始应变并减少自我居中系数（即，而不会损害自我核心）减少了整体桥梁的损害。此外，即使考虑到环境温度的不确定性，SRR柱也至少证明了与PT柱一样有效，可以减轻整体式RC柱的桥梁的地震损伤。
This paper evaluates the seismic fragility of a two-span reinforced concrete (RC) bridge with shape memory alloy (SMA)-restrained rocking (SRR) columns through machine learning (ML) techniques. SRR columns incorporate a combination of replaceable superelastic NiTi (SMA) links and mild steel energy-dissipating links to achieve self-centering and energy dissipation, respectively, while their rocking joints are protected against compressive concrete damage through steel jacketing. To produce seismic fragility functions, initially, multi-parameter probabilistic seismic demand models (PSDMs) are generated for various engineering demand parameters through five different ML techniques (including neural network) and considering various sources of uncertainty, and the most accurate PSDMs are selected. The selected PSDMs are then interpreted using four different methods to investigate the effects of two key SRR column design parameters (self-centering coefficient and SMA link initial strain) and ambient temperature on the seismic performance of SRR columns. Subsequently, using neural networks, the PSDMs developed earlier, and appropriate capacity models, multi-parameter fragility functions are developed for various bridge damage states. After examining the effects of the two SRR column design parameters on the seismic fragility of the bridge, its seismic fragility is compared with those of the same bridge with monolithic RC and posttensioned (PT) rocking columns. It is shown that, in general, increasing the initial strain of the SMA links and decreasing the self-centering coefficient as possible (i.e., without compromising the self-centering) reduce the overall bridge damage. In addition, even considering the ambient temperature's uncertainty, SRR columns are proven, at least, as effective as PT columns in mitigating the seismic damage of the bridges of monolithic RC columns.
