Carbon fiber reinforced polymers (CFRPs) are continuously gaining attention in aerospace and space applications, and especially their multi-scale reinforcement with nanoadditives. Carbon nanotubes (CNTs), graphene, carbon nanofibers (CNFs), and their functionalized forms are often incorporated into interactive systems to engage specific changes in the environment of application to a smart response. Structural integrity of these nanoscale reinforced composites is assessed with advanced characterization techniques, with the most prominent being nanoindentation testing. Nanoindentation is a well-established technique, which enables quantitative mapping of nanomechanical properties with the µm surficial and nm indentation resolution scale and high precision characterization. This feature enables the characterization of the interface in a statistical and quantitative manner and the correlation of (nano-) reinforcement to interface properties of CFRPs. Identification of reinforcement is performed with k-Nearest Neighbors and Support Vector Machine classification algorithms. Expertise is necessary to describe the physical problem and create representative training/testing datasets. Development of open source Machine Learning algorithms can have an influential impact on uniformity of nanometry data creation and management. The statistical character of nanoindentation is a key factor to supply information on heterogeneity of multiscale reinforced composites. Both the identification of (nano-) reinforcement and quality assessment of composites are provided by involving artificial intelligence.

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Nanoindentation was utilized as a non-destructive technique to identify Portland Cement hydration phases. Artificial Intelligence (AI) and semi-supervised Machine Learning (ML) were used for knowledge gain on the effect of carbon nanotubes to nanomechanics in novel cement formulations. Data labelling is performed with unsupervised ML with k-means clustering. Supervised ML classification is used in order to predict the hydration products composition and 97.6% accuracy was achieved. Analysis included multiple nanoindentation raw data variables, and required less time to execute than conventional single component probability density analysis (PDA). Also, PDA was less informative than ML regarding information exchange and re-usability of input in design predictions. In principle, ML is the appropriate science for predictive modeling, such as cement phase identification and facilitates the acquisition of precise results. This study introduces unbiased structure-property relations with ML to monitor cement durability based on cement phases nanomechanics compared to PDA, which offers a solution based on local optima of a multidimensional space solution. Evaluation of nanomaterials inclusion in composite reinforcement using semi-supervised ML was proved feasible. This methodology is expected to contribute to design informatics due to the high prediction metrics, which holds promise for the transfer learning potential of these models for studying other novel cement formulations.

Purpose Structures in seismic areas, during their service lifetime, are subjected to numerous seismic loads that certainly affect their structural integrity. The degradation of these structures, to a great extent, depends on the scale of seismic events, the steel mechanical performance on reversal loads and its resistance to corrosion phenomena. The paper aims to discuss these issues. Design/methodology/approach Based on the experimental results of seismic steel behavior S400 (BSt III), which was widely used in the past years, a prediction study of seismic steel behavior was conducted in the current study. This prediction on behavior of both reference and corroded steel was succeeded through a simulation of experimental low cycle fatigue conditions (LCF – strain controlled). Findings At the same time, the present study analyses fatigue factors (ef, a, fSR, ed, ep, R, b) that define their inelastic relation between tension – strain and a prediction model on behavior of both reference and corroded steel rebar, in seismic loads conditions (LCF), is proposed. Originality/value Moreover, this study dealt with the synergy of corrosion factor and the existence of superficial ribs (ribbed and smoothed) in seismic behavior of steel bar S400 (BSt420). The S-N curves that are exported can be resulted in a first attempt of prediction of anti-seismic behavior on reinforced concrete structures with this the same steel class.

Abstract:The purpose of this study was the synthesis of novel low-cost carbon fibers along with the investigation of the optimal parameters of temperature and time for the stabilization of hybrid high-density polyethylene (HDPE) and lignin melt-spun fibers. These fibers were manufactured by physical compounding of HDPE and chemically-modified softwood kraft lignin (SKL) in order to produce green fiber precursors for carbon fiber synthesis. Stabilization tests were performed with respect to thermal treatment (physical method) and sulfonation treatment (chemical method). The results revealed that only chemical methods induce the desired thermal process-ability to the composite fibers in order to manufacture carbon fibers by using a simple method. This investigation shed light on the stabilization techniques of polymeric fibers in the absence of any cyclic groups in terms of environmentally-friendly mass production of carbon fibers using low-cost and green raw materials. This study facilitates incorporation of softwood lignin in homegrown polymeric fibers by a low-cost production process via melt-spinning of composite fibers, which were successfully stabilized using a facile chemical method and carbonized. Additionally, a comprehensive investigation of the thermal behavior of the samples was accomplished, by examining several ways and aspects of fiber thermal treating. The properties of all studied fibers are presented, compared, and discussed.