Applied Mechanics

Recent advances in nanotechnology have highlighted the transformative potential of carbon-based nanomaterials, such as carbon nanofibers, carbon nanotubes, and graphene, in cementitious systems. These materials have shown a remarkable ability to enhance the mechanical strength, fracture toughness, and overall functional performance of cementitious composites. Their nanoscale dimensions and exceptional intrinsic properties allow for effective stress bridging, crack arrest, and matrix densification. Despite these promising features, the current understanding remains limited, particularly regarding their application to concrete. Furthermore, literature lacks systematic, parallel evaluations of their respective effectiveness in improving both mechanical performance and long-term durability, as well as their potential to impart true multifunctionality to concrete structures. It is worth noting that significant and statistically significant improvements in fracture behavior were observed at specific nanofiller concentrations, suggesting strong potential for the material system in next-generation innovative infrastructure applications. Experimental results demonstrated that both CNTs and GNPs significantly enhanced the mechanical performance of concrete, with flexural strength increases of approximately 49% and 38%, and compressive strength improvements of 22% and 47%, respectively, at optimum contents of 0.6 wt.% CNTs and 0.8 wt.% GNPs. SEM analyses confirmed improved matrix densification and interfacial bonding at these concentrations, while higher dosages led to agglomeration and reduced performance. This gap highlights the need for targeted experimental studies to elucidate the structure-property relationships governing these advanced materials.

In this study, the influence of nanochitosan and kenaf fibers on the tensile strength, elastic modulus, and impact strength of polylactic acid (PLA)/natural rubber (Standard Malaysian Rubber, grade 20—SMR20) biocomposites was investigated experimentally using Response Surface Methodology (RSM). The independent variables included the weight percentage of nanochitosan (2, 4, and 6 wt%), kenaf fibers (5, 10, and 15 wt%), and SMR20 natural rubber (10, 20, and 30 wt%). Composite samples were prepared by melt mixing in an internal mixer and subsequently fabricated into test samples using hot compression molding in accordance with relevant standards. Tensile tests were conducted to evaluate tensile strength and elastic modulus, while Charpy impact tests were performed to assess impact strength. The results revealed that increasing nanochitosan content up to 4 wt% enhanced tensile strength, elastic modulus, and impact strength by 39%, 22%, and 27%, respectively; however, further addition (6 wt%) led to a decline in these properties due to nanoparticle agglomeration. Increasing kenaf fiber content to 15 wt% improved tensile strength, elastic modulus, and impact strength by 44%, 26%, and 37%, respectively, demonstrating their effective reinforcing role. The incorporation of SMR20 natural rubber significantly increased impact strength by 59% (at 30 wt%), while causing a reduction of 17% in tensile strength and 20% in elastic modulus, consistent with its elastomeric nature. Furthermore, field emission scanning electron microscopy (FESEM) was employed to examine the dispersion of nanochitosan and kenaf fibers within the PLA/SMR20 matrix, providing insights into the interfacial adhesion and failure mechanisms. The findings highlight the potential of optimizing natural filler and rubber content to tailor the mechanical performance of sustainable PLA-based biocomposites.

This paper presents a systematic review conducted under the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, analyzing 286 scientific articles focused on vibration-based predictive maintenance strategies for wind turbines within the context of advanced Prognostics and Health Management (PHM). The review combines international standards (ISO 10816, ISO 13373, and IEC 61400) with recent developments in sensing technologies, including piezoelectric accelerometers, microelectromechanical systems (MEMS), and fiber Bragg grating (FBG) sensors. Classical signal processing techniques, such as the Fast Fourier Transform (FFT) and wavelet-based methods, are identified as key preprocessing tools for feature extraction prior to the application of machine-learning-based diagnostic algorithms. Special emphasis is placed on machine learning and deep learning techniques, including Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and autoencoders, as well as on hybrid digital twin architectures that enable accurate Remaining Useful Life (RUL) estimation and support autonomous decision-making processes. The bibliometric and case study analysis covering the period 2020–2025 reveals a strong shift toward multisource data fusion—integrating vibration, acoustic, temperature, and Supervisory Control and Data Acquisition (SCADA) data—and the adoption of cloud-based platforms for real-time monitoring, particularly in offshore wind farms where physical accessibility is constrained. The results indicate that vibration-based predictive maintenance strategies can reduce operation and maintenance costs by more than 20%, extend component service life by up to threefold, and achieve turbine availability levels between 95% and 98%. These outcomes confirm that vibration-driven PHM frameworks represent a fundamental pillar for the development of smart, sustainable, and resilient next-generation wind energy systems.