Structural health monitoring (SHM) plays a pivotal role in ensuring the safety, reliability and service life of engineering structures. In smart structures, networks of active-response materials (e.g. piezoelectric films, magnetostrictive patches, or fibre-optic cables), which convert mechanical and thermal stimuli into electrical or optical signals and act as the primary interface for continuous condition assessment. A persistent challenge is the influence of environmental and operational variability (EOV), particularly temperature changes, which can distort sensor measurements and either obscure or mimic genuine indicators of structural damage. Although numerous methodologies have been proposed to address this issue across various sensing platforms, a comprehensive comparative assessment across methodological categories remains lacking. This review critically examines 3 principal approaches developed to mitigate EOV: direct baseline compensation, adaptive and multi-baseline strategies, and reference-free techniques, including recent advances in transfer learning and hybrid physics-informed machine learning frameworks. A structured literature search spanning Scopus, Web of Science, IEEE Xplore and ScienceDirect underpins the analysis. Each approach is systematically evaluated, highlighting key benefits, limitations and suitability for varying operational scenarios. In addition, emerging trends, gaps and future research directions are identified, emphasising the need for hybrid models, real-time reference-free methodologies, robust uncertainty quantification and scalable population-based SHM solutions. The synthesis is intended to inform the design of next-generation smart, adaptable SHM systems regardless of sensing modality and their seamless integration into intelligent structures operating under complex real-world environmental conditions.

Artificial Neural Networks (ANNs) have rapidly emerged as a promising tool to solve damage identification and localization problem, according to a Structural Health Monitoring approach. Finite Element (FE) Analysis can be extremely helpful, especially for reducing the laborious experimental campaign costs for the ANN development and training phases. The aim of the present work is to propose a guided wave-based ANN, developed through the use of the Finite Element Method, to determine the position of damages. The paper first addresses the development and assessment of the modeling technique. The FE model accuracy was proven through the comparison of the predicted results with experimental and analytical data. Then, the ANN was developed and trained on an aluminum plate and subsequently verified in a composite plate, as well as under different damage configurations. According to the results herein proposed, the ANN allowed to detect and localize damages with a high level of accuracy in all cases of study.