In complex and dynamic environments, the degradation of structural systems typically results from multiple factors rather than a single one. This chapter proposes a hybrid physics-model-based and data-driven methodology for RUL prediction in structural systems, considering the influence of multiple degradation causes in dynamic complex environments. Using DBNs, the method models the degradation processes caused by various factors, such as fatigue, corrosion, sand erosion, and internal waves, which contribute to system uncertainty. By integrating theoretical or empirical physical models with DBNs, the approach addresses data insufficiency and builds a comprehensive RUL estimation framework. The RUL is calculated based on the time difference between the detection point and the predicted failure point, determined by a performance failure threshold. The model can be updated with sensor data and expert knowledge to refine RUL estimates in real-time. The methodology is demonstrated through its application to subsea pipelines in offshore oil and gas production systems, highlighting its effectiveness in accounting for multiple degradation factors in complex environments.