Today's engineers are faced with the challenge of ensuring the resilience of critical infrastructure as it continues to grow in size, number and complexity as modern societies progress. In this context, tools for life cycle planning with limited resources are becoming increasingly important to enable authorities and stakeholders to pursue long-term goals in the face of increasingly adverse and unpredictable environmental impacts. In this context, adaptability is a key factor in providing resilient technical systems [1]. The optimization and simulation framework itself, which is used to evaluate optimal action strategies in terms of resilience and cost and based on Python codes, is complemented by linked data models and the hierarchical data format (HDF5) that serve as an embedding meta environment. Ontologies are utilized to create semantic knowledge graphs and enable the retrieval and analysis of metadata of the overall modeling process. Seven fundamental topics related to semantics, numerics and algorithms have been identified that are involved in the modeling process of a critical engineering structure: 1. System topology including sub-structures, elements their functional dependency and semantic hierarchies; 2. Design and physical quantities and their relations, 3. According prediction and conversion models; 4. design criteria from codes and standards that are utilized to assess the performance state of elements and sub-structures; 5. Algorithmic procedures, such as the resilience optimization and stochastic simulation [2], that form the backbone of the adaptive assessment framework as they act as outer loops and interact with the knowledge graphs, models and data frames; 6. Parameters, input and output data is represented via HDF5 and linked with Web Ontology Language classes; 7. The resilience framework is based on the findings in [2] including a resilience metric and a systemic risk measure incorporating cost but extends the consideration to a time-dependent life cycle strategy optimization problem. The proposed work is intended to be a further step towards an overarching perspective on holistic and adaptive linked data modeling environments for resilient life cycle engineering. As part of the SPP 2388, funded by the German Research Foundation (501624329), the bridge “Nibelungenbrücke Worms” and its digital twin were considered in the case study.