Over the past decade, unmanned aerial vehicles (UAVs) have received a significant attention due to their diverse capabilities for non-combatant and military applications. The primary aim of this study is to unveil a clear categorization overview for more than a decade worth of substantial progress in UAVs. The paper will begin with a general overview of the advancements, followed by an up-to-date explanation of the different mechanical structures and technical elements that have been included. The paper will then explore and examine various vertical take-off and landing (VTOL) configurations, followed by expressing the dynamics, applicable simulation tools and control strategies for a Quadrotor. In conclusion to this review, the dynamic system presented will always face limitations such as internal and/or external disturbances. Hence, this can be minimised by the choice of introducing appropriate control techniques or mechanical enhancements.

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.

Ageing structures located in moderate to high seismicity regions are exposed to multiple natural stressors during their lifetime. Large earthquake events coupled with environmental aggressive agents increase the progressive failure probability of these structures. The accumulated damage during the main earthquake event might be exacerbated by its following aftershocks. This might result in catastrophic failure of these structures, and consequently, result in several socio-economic losses. Taking such progressive deterioration mechanism into consideration, the current study presents a framework to assess the vulnerability of ageing Reinforced Concrete (RC) frames subject to real Mainshock-Aftershock (MS-AS) ground motion sequences. Employing an advanced fibre-based finite element modelling technique, the nonlinear static and dynamic behaviour of a case-study RC frame with various ages is simulated under 48 real MS-AS record pairs. Quantifying corrosion-variant damage states, the age-specific fragility curves are developed for the considered structure under both single MS events and MS-AS sequences. It was found that the severely corroded RC frames are most likely to collapse before the second event comes up. Moreover, results show that the PGA ratio of AS to MS plays a critical role in seismic vulnerability assessment of highly corrosion-damaged RC frames.