The development of a safety monitoring model utilizing dam deformation monitoring data is a crucial method for the quantitative analysis of deformation patterns

but previous deformation monitoring models often suffer from severe shortcomings in the selection of influencing factors and resisting outlier interference. This study develops an adaptive identification method for deformation outliers in concrete dams based on optimized statistical models. This method not only identifies outliers during regression modeling

but also prevents the monitoring model from distortion caused by erroneous data cleansing. First

we use a Bayesian model selection technique to reduce redundancy among the deformation’s influencing factors

helping identify significant explanatory variables in the statistical modeling phase. Then

we use the least trimmed squares estimation for robust regression analysis of the deformation monitoring data

and construct a monitoring model that adaptively identifies various types of anomalies in the deformation data series. Finally

we design and implement a visualization strategy for different types of anomalies to generate an intuitive representation of their locations and potential impacts. A case study demonstrates that this new method identifies key deformation factors effectively

and adaptively reduces the interference of different anomaly types to regression analysis. It leads to improvement on the significance of regression results and the goodness of fit and prediction accuracy

manifesting satisfactory applicability for detecting anomalies in monitoring data and conducting quantitative analyses of dam safety behaviors.