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The aim of this study was to develop flame-retardant textile materials and study their aspects to durability against several washing cycles. Since the conventional textile substrates are flammable and combustible, the development of flame-retardant apparel from pure Trevira CS fabrics as well as its blends with cotton, modal, and acrylic in certain proportion was carried out in this study. Furthermore, in addition to demonstrating necessary flame-retardancy characteristics, flame-retardant garments must provide a minimum degree of comfort to the wearer, and it is thought of as blending. The productions of yarn were made with ring spinning systems, and knitted fabrics also were produced from this pure Trevira CS and its blends yarn. Flammability test was carried out for the spun yarn and for the fabrics manufactured separately. Limiting oxygen index (LOI) tests were carried out for the sample fabrics. The result indicates that most of the fabrics are flame resistant except the T/C (80 : 20) fabric which show low LOI values, but by treatment with finishing agents, it can be improved. In order to study the durability against washing cycles, LOI values of all sample fabrics were determined. LOI values of all the four types of knitted fabrics in gray, finished, and washed states were evaluated and compared. The pure Trevira-spun knitted fabric exhibits very good flame retardancy in gray state and hence may not require any flame-retardant finishing treatment. Upon imparting the wash resistant, flame-retardant finish Pekoflam DPN, it was observed that the LOI of all the treated samples showed a significant increase. It was revealed also that upon washing (after 10 washes), the LOI values of the fabrics have been considerably reduced; however, no appreciable reduction in flame retardancy is noticed upon further washing of fabrics, after 20 and 30 washes, respectively. Even after 30 washes, the flame retardancy of most of fabrics are very good and one sample showed good result. Finally, the knitted fabrics from pure Trevira-spun yarns and the FR-treated T/C (80 : 20), T/M (80 : 20), and T/A (80 : 20) ones qualify very well for the production of flame-retardant garments for various applications in different forms.

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%.