## 1 Introduction  
Chloride-induced rebar corrosion, often resulting from marine exposure and the widespread use of deicing salts. It is a common cause of degradation in reinforced concrete structures [1]. The durability of concrete structures in chloride environments seriously affects their long-term performance. The durability of concrete structures refers to the ability to maintain their applicability and safety under normal use and maintenance conditions [2]. Usually, the durability of concrete structures in chloride environments, especially permeability and diffusion, is influenced by their characteristics, such as concrete quality, environmental exposure conditions, and the presence of stress or cracks [3; 4; 5; 6; 7]. Many studies developed durability models that link corrosion and service life [2]. In practical environments, the actual durability of concrete is different from the theoretical durability [8; 9]. In addition, due to the different experimental methods, parameters, and modeling assumptions used by different researchers, the prediction results of different theoretical degradation models vary greatly. In order to minimize the variability of these predictions and improve the confidence of the results, on-site durability inspection information is usually used to calibrate and modify these durability models, which requires the consideration and proper handling of uncertainties related to various measurements and the development of methods that can appropriately use on-site inspection information [10].  
Updating durability models for concrete structures is an important research direction in the field of civil engineering, aiming at continuously optimizing and updating the durability prediction models for concrete structures by means of monitoring or inspection data so as to more accurately assess the remaining life of the structures [10]. In Europe and the United States, research on the update of durability models for concrete structures is relatively mature, with many studies focusing on the integration of sensor technology and data-driven methods to achieve real-time monitoring and model updates. For example, [PERSON] et al. [11] proposed a concrete durability model based on the Bayesian updating method, which can adjust model parameters according to real-time monitoring data to improve prediction accuracy. [PERSON] et al. [12] studied the update method of concrete structure durability models based on reliability theory. By considering the uncertainties of environmental and material properties, they proposed a more flexible and reliable model update framework. Research on the update of durability models for concrete structures in China started late but has made some progress in recent years. Many universities and research institutions have begun to focus on this field, especially in the application of combining Internet of Things technology and big data analysis. [PERSON] et al. [13] proposed a concrete structure durability monitoring and model update system based on the reliability method in their research. It can realize the real-time data collection and processing of large-scale structures, providing effective technical support for model updates. [PERSON] et al. [14] explored the update method of concrete durability models based on machine learning algorithms. By analyzing historical monitoring data, the model parameters are automatically adjusted, improving the predictive performance and adaptability of the model.  
To narrow the gap between theoretical predictions and observed structural durability, this paper proved an updating method for durability models with inspection data through Bayesian updating. It is organized as follows: In Section 2, the durability models in chlorine environment with [PERSON]'s second law and probabilistic lifetime prediction model are presented. Section 3 introduces Bayesian updating and the actual numerical method used. Finally, a case study is presented in Section 4, where the different detection information is applied, evaluated, and compared.