Analytical and diagnostic control blocks form the basis of modern digital systems for structural reliability assessment, integrating the processes of data collection, processing, and interpretation in real-time. The digital control environment creates a multi-layered architecture that combines sensor technologies, analytical algorithms, machine learning methods, and cognitive forecasting models. Its key task is to develop an integrated monitoring system capable of providing rapid structural diagnostics and determining degradation trends based on large-scale data analysis. The functioning of analytical blocks is based on the principle of coherent information exchange between sensor nodes, analytical modules, and management subsystems. Each level of the digital system performs a specific function: the sensory level records load and deformation parameters; the analytical level performs mathematical processing and anomaly detection; the diagnostic level determines the causes of defects; and the predictive level models the future behavior of structures. In the context of the industry's digital transformation, the use of structural digital twins, which combine real-time sensor data with virtual analytical models, is of particular importance. This approach ensures continuous updates of the object's technical condition and creates a basis for preventive risk management. The integration of trigger algorithms allows the system to react automatically to critical changes, increasing the accuracy and speed of management decision-making. The system's analytical efficiency is determined by the quality of information processing, diagnostic reliability, and self-learning capability. The development of digital technologies creates prerequisites for a new paradigm of technical monitoring, where structural reliability control is based not only on recording parameters but on their deep cognitive and analytical interpretation. In this context, analytical and diagnostic blocks serve as the core of the digital engineering infrastructure, combining artificial intelligence, digital twins, and systemic diagnostics to achieve maximum accuracy in assessing the structural stability and safety of construction objects.