## 3 Deterioration Features and Data Description for East Japan
### Data Arrangement and Description
For each panel, if the event did not happen in the inspection period (all of the data are censored), the period between the construction time and latest inspection time was the survival time (properly speaking, the censoring time) of this object. For case two, if the event occurred, the period between the construction year and the time the event first occurred was the survival time. Figure 4: Previous survival time calculation (from [PERSON] and [PERSON] (2015)). Figure 5: Calculation of survival time. Multiple survival times or censoring times for one object lead to redundant data, which could lead to imprecision in the KM curve and incorrect multivariate analysis results. Figure 6 shows the result of comparing KM curves; the red line presents the KM with redundant data, and the green line presents the KM curve after data cleaning. The dotted line shows the 95% confidence interval. It can be clearly seen that after data cleaning, the declination of the KM curve changed, indicating that the data cleaning process was necessary. Before data cleaning and sorting, the dataset included inspection data for 82,016 panels in 525 steel girder bridges. After selection and cleaning, our dataset included 17,557 panel inspection records for 183 bridges. The construction year distribution is shown in Figure 7.  
Multiple variables relating to bridge slab deterioration were used as explanatory variables. Structural features and environmental conditions including precipitation over four seasons, winter temperature, etc. were considered and selected as variables. National Land Numerical Information published by the Ministry of Land, Infrastructure, Transport, and Tourism was used for weather data accumulation. These data were generated from observations by the Japan Meteorological Agency between 1981-2010. The resolution of these covariates is a 1 km mesh, and they were numerically analyzed to correct for the effects of topography and elevation. Using Rstudio Version 1.0.143, and referring to the bridge coordinates added by [PERSON] et al. (2013), this National Land Numerical Information was combined with inspection records [10]. Figure 6: Comparison of the Kaplan–Meier (KM) curve with and without redundant data. Figure 7: Construction year of panels in East Japan. However, precise multivariate analysis results are difficult to obtain due to high correlation between some variables. In our dataset, environmental data such as precipitation over four seasons and winter temperature were highly correlated; therefore, without appropriately selected variables, univariate and multivariate analysis led to paradoxical results. For example, a univariate analysis of precipitation over four seasons showed an increase in the hazard ratio, while multivariate analysis showed that the risk of some variates tended to decrease. This unexpected result implies that the selection of an appropriate explanatory variable was a precondition for obtaining good results. In this analysis, the correlations of all of the variables below 0.5 were selected as explanatory variates. The covariates used in analysis are shown in Table 2. They were recorded as numerical and categorical covariates. All of the numerical covariates were standardized and are presented in the methodology section. Additionally, the unit, mean value, and standard deviation of all of the numeric variables are shown in the table. For categorical covariates, the crossing condition, panel position, slope, de-icing salt usage, and waterproof layer installation situation were separated into different groups. The definition for each group is also shown in the table. List-wise case deletion was executed for records with insufficient information.