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The growing awareness of the environmental damage caused by petroleum-based fibres has led to an increase in renewable and biodegradable resources. The continually growing demand for eco-friendly and sustainable materials pushes automakers and material researchers to consider the ecological importance of their materials during fabricating, recovering, and disposal. Natural fibre-reinforced composites (NFRC) have been introduced into the automobile market substantially over the last ten years due to their renewability, eco-friendliness, recyclability, biodegradability, light weight, better specific strength, good resistance to impact and corrosion, abundantly available, ease of processing, and cost-effectiveness. Due to cost-effectiveness and weight reduction, NFRC is becoming a better replacement for petroleum/synthetic fibres like aramid, kevlar, carbon, and glass fibre-reinforced composites, and the transportation sector has instigated the use of these materials in many applications like car interiors and exteriors, dash boards, bumpers, spoilers, seat covers, and mirror casings. NFRC with nanofillers is gaining more attention in the field of engineering, particularly for automotive, defense, building, and construction applications due to better aspect ratio, larger surface area, and attractive properties. The mechanical, tribological, and thermal properties of plant fibre-reinforced composites can be improved through the incorporation of organic or inorganic nanofillers. The present review profoundly explores the effects of various factors influencing NFRC with nanofillers. This paper also summarises the effects of various chemical reagents, fabrication techniques, and industrial applications of NFRC.

This article uses cutting-edge deep learning technology to identify structural damage from images for a civil engineering application. The public infrastructures of the country are generally inspected physically by a visual evaluation by qualified inspectors. However, manual inspections are pretty time-consuming and often require too much labor. The number of experts capable of evaluating such structural damage is inadequate. As a result, computer vision-based techniques for automatic damage detection have been developed. This paper’s civil infrastructure damages are classified into four damages of roads common in Indian highways and the concrete deterioration in the bridges. The convolutional neural network has become a standard tool for organizing and recognizing images. In this paper, an ensemble of three CNN models is proposed, and two are transfer learning-based models. The proposed ensemble transfer learning model provided a validation accuracy of 87.1%.