Safety-Oriented Credibility-Based Fuzzy Incremental Learning for Predicting Dependent Outputs

Guaranteeing the safety of nearby buildings is essential in tunnel construction. In practice, it is implemented by closely monitoring the daily and accumulated settlements, which are dependent outputs. To accurately predict such outputs, a new approach using two features is proposed. First, a new concept of data credibility is proposed to represent the different levels of consistency among the multiple dependent outputs. Second, the training dataset is developed using data gathered from multiple phases based on the fuzzy number. The new approach is named credibility-based fuzzy incremental learning approach using the belief rule base (BRB), CI-BRB. The key contributions of the proposed CI-BRB approach are: 1) data credibility is defined and calculated rather than blindly assuming all data are accurate by default and 2) the training dataset is more representative as it includes both the current phase and more prior phases. Subsequently, a numerical case with three dependent outputs is designed to provide a detailed illustration, and a practical case is studied in a more comprehensive fashion. The case study results show that the proposed approach can produce superior results for modeling that adopts a none strategy. Additionally, further investigations validate the effectiveness of the strategy over incremental learning.