# Safety Evaluation of a Water-Immersed Bridge Against Multiple Hazards via Machine Learning
## 1 Introduction  
In Taiwan, interweaving rivers and creeks are spread over the entire island. To achieve smooth southbound and northbound traffic flow, river-crossing bridges are prerequisite infrastructure facilities. Because these infrastructure facilities are usually designed with a very long lifespan, it is also required to consider the effects from all aspects when designing these structures. As Taiwan is situated in a subtropical monsoon area, river-related hazards should not be treated lightly because of the abundant rainfall and the striking of typhoons during June-August each year. In the meantime, Taiwan is also located at the interfacing area between the Eurasian and Philippine Sea Plates. Therefore, it sits on the seismic zone. When these plates drift and squeeze with each other, Taiwan will be shaken by earthquakes. As such, impacts to structures as caused by earthquakes should not be ignored, and this is the key point that will be dealt with by each engineer. Many researchers have focused on building the relationship between earthquake intensities and bridge/building damages. For example, [PERSON] et al. [1] suggested that fragility curves are a practical tool. Based on the observation at bridge sites, [PERSON] and [PERSON] [2] found bridges were damaged in different ways in the Chi-Chi earthquake such as unseating span failure, abutment failure, joint failure, substructure damage, footing settlement, and so on. [PERSON] et al. [3] developed site-specific ground motions to analyze several typical failure bridge modes and concluded that displacement is a good indicator to measure bridge safety. In addition to the seismic hazard, 44 bridges were damaged from Hurricane Katrina [4]. [PERSON]' and [PERSON] [5] reported that flood-induced damage was the primary damage for bridges in the United States. Similarly, Taiwan faces the same challenge. [PERSON] et al. [6] proposed a bridge safety evaluation process against seismic and flood hazards, in which a scour prediction equation was constructed to measure the flood risk, and the probabilistic seismic-hazardwas adopted to analyze the seismic hazard. To obtain bridge performance, the displacement ductility was used, and nonlinear pushover and time-history analyses were conducted. The goal of this study is to investigate bridge performance when earthquakes occur during the bridge reinforcing period after being scoured by water. Then the performance of the bridge, including the immersed effect, should be carefully computed. In this case, the fragility curves, obtained from both original and surrogate models, will be employed to learn the hazard-resistant capacity of the bridge. Construction of fragility curves often requires many efforts; to save time, machine learning is often adopted [7]. In addition, due to the superior prediction performance of the least-squares support-vector machine (LSSVM) and several successful studies in safety evaluation or other fields [8; 9; 10; 11], this research proposes to use the LSSVM for estimating the ductility capacity of a bridge. LSSVMlab toolbox is an open source program with possibility of having any extension in applications. The formulation of LSSVM is flexible and can be easily incorporated with existing/known information/data due to the parametric model in the primal domain. The aforementioned prior knowledge, such as water level and scouring depth considered here, can be automatically embedded in the dual formulation. Therefore, LSSVM is adopted here. Please note that there are many available machine learning algorithms; investigation or comparison with other algorithms is beyond the scope of the current study. In this article, a bridge located on the QingShui River in Central Taiwan is taken as the target for simulation analysis. The learned displacement ductility from LSSVM will be employed in a bridge safety analysis under all types of scenarios, and then the fragility curves under multiple hazards will be established for calculating the multihazard damage probability.