Language model enhanced surface chloride concentration determination for concrete within splash environment based on limited field records

Chloride ion is severely harmful to reinforced concrete (RC) structures in marine environments. For maintaining the durability and safety of the designed RC structures, the determination of chloride ion concentration on concrete surfaces is critical. Currently, surface chloride ion concentration can be determined using empirical formulas and machine learning (ML) approaches. However, these approaches only rely on the numerical information within field records, disregarding valuable semantic and background information in the records, leading to low accuracy. Meanwhile, in splash environments, it presents a significant challenge to obtain chloride ion concentration records due to the complex environment and high costs involved. Therefore, based on limited field records of surface ion concentrations in splash environments, and utilizing a stateof-the-art language model (LM), this study proposes an LM-based information generation (LMIG) model to improve the accuracy of determination of surface chloride concentrations on RC structures. This paper utilizes the numerical and semantic information in 70 sets of field records to fine-tune the LMIG model and generates 200 sets of high-quality records. These records are then used to train ML algorithms for predicting chloride ion concentrations on concrete surfaces. After conducting comparative research, it was found that incorporating records generated by the LMIG model significantly enhances the accuracy of the ML algorithm. Specifically, the predictive accuracy using the random forest algorithm increased by 33.1%. Furthermore, this paper also conducts a comparative study on the LMIG model and the generative adversarial network (GAN)assisted data-driven method. The results demonstrate that integrating semantic and numerical information into the LMIG model shows significant advantages in enhancing ML algorithms' accuracy.