Toward city-scale structural health monitoring: Architectural gaps, a modular plug-and-play framework, and research directions

Structural health monitoring (SHM) has achieved substantial technological maturity at the individual-asset level, yet its extension to heterogeneous multi-asset and city-scale environments remains constrained by project-specific configuration, weak interoperability, expert-dependent integration, and limited lifecycle adaptability. This study presents MPnP-SHM, a Modular Plug-and-Play architectural framework intended to address these systems-level barriers. A structured review of structure-centric SHM, wireless sensor network and Internet of Things approaches, edge–cloud and cyber-physical systems, digital twins, and Population-Based Structural Health Monitoring is first used to identify persistent architectural gaps. MPnP-SHM subsequently introduces metadata-driven constrained runtime orchestration, in which sensing and analytical components expose machine-readable capabilities, structural context, operational state, and processing requirements. Metadata are validated and assessed against predefined engineering constraints before compatible analytical services are selected, parameterized, deployed, or reconfigured across sensing, edge, and cloud resources. The framework additionally incorporates data-quality and provenance management, multi-fidelity digital-twin representations, legacy-system integration, graceful analytical degradation, and interfaces for cross-asset and population-level analytics. Importantly, plug-and-play operation is not interpreted as autonomous structural engineering: sensor placement, monitoring objectives, synchronization tolerances, environmental-normalization models, analytical assumptions, and final structural interpretation remain subject to engineering validation. Illustrative deployment scenarios demonstrate how the proposed architecture can organize incremental monitoring, post-event screening, lifecycle-aware operation, and population-level analysis. The present contribution is conceptual and architectural rather than experimentally validated; future work should quantify onboarding, reconfiguration, computational, communication, fault-recovery, scalability, and analytical-performance metrics through proof-of-concept and multi-asset implementations.