Structural Health Monitoring of Gas Networks via Acoustic Emission and Fuzzy Pattern Recognition.

Structural durability and reliability are fundamental imperatives for the safe operation of engineering infrastructure, profoundly influencing both lifecycle asset economics and socio-environmental safety. A critical component of structural integrity engineering involves the precise spatial localization of defects, the continuous monitoring of their propagation kinetics, and the high-fidelity quantification of their impact on global structural health. To achieve this, advanced diagnostic methodologies are required to detect the earliest indicators of material degradation and track its evolution throughout the operational lifespan of the asset. Crucially, these non-destructive techniques must transcend reliance on subjective, localized visual inspections or unverified numerical models. The Acoustic Emission (AE) method represents a highly effective Non-Destructive Testing (NDT) paradigm that satisfies these requirements by performing real-time analysis of active degradation mechanisms coupled with specialized reference signal databases. This paper presents an integrated assessment framework developed within the SILDIG research initiative conducted between 2020 and 2025, introducing a novel framework founded on the methodological and technological fusion of two previously independent systems: the SONG diagnostic paradigm and the AE wave propagation methodology. The integrated framework uses a five-class anomaly classification scheme developed in accordance with the technical assessment framework applied in the investigated gas-network infrastructure. By operationalizing this unified framework within a fuzzy pattern recognition database-the Identifying Gas Network Anomalies (IGNA) methodology- the framework was evaluated on selected steel and PE100 pipeline sections representing different material and operational conditions. The proposed approach was deployed to evaluate the structural condition of 20 critical gas infrastructure components, specifically comprising 15 steel and 5 polyethylene (PE) pipelines. Laboratory tests and field monitoring campaigns were used to assess the capability of the AE framework to detect, classify, and localize anomalous signal patterns under controlled and operational loading conditions. The results indicate the potential of AE-based pattern recognition to support condition assessment and maintenance prioritization in gas pipeline infrastructure.