Faculty of Engineering
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Publication Embargo Durability and mechanical performance of glass and natural fiber-reinforced concrete in acidic environments(Elsevier, 2025-02-28) Justin, S.; Thushanthan, K; Tharmarajah, GThis study investigates the mechanical and durability characteristics of fiber-reinforced concrete when exposed to acidic environments. The research focuses on the effects of adding 1 % of treated coir fibers (TCF), treated rice husk fibers (TRH), and glass fibers (GF), along with 5 % silica fume (SF), to concrete. Experimental results show that the inclusion of these fibers and SF enhances both compressive and tensile strengths, with the most significant improvements observed in GF-reinforced concrete. The durability of the concrete was tested by immersing samples in acidic solutions with pH values of 3 and 5 for 28 days. Ultrasonic Pulse Velocity (UPV) tests indicated that the concrete's quality remained stable, while compressive strength tests revealed an increase in strength, particularly in samples exposed to pH 5. Sorptivity tests, which measure water absorption, indicated higher initial absorption rates due to the porous nature of fiber-reinforced concrete. However, as hydration progressed, the rate decreased. SEM images show that incorporating silica fume improves the microstructure of the specimens benefitting the strength of the structure. The study concludes that concrete reinforced with GF and SF exhibits superior mechanical properties and durability in acidic environments, making it a promising material for use in harsh conditions.Publication Open Access Facial identity recognition using StyleGAN3 inversion and improved tiny YOLOv7 model(www.nature.com, 2025-03-17) Kumar, A; Bhattacharjee, S; Kumar, A; Jayakody, D. N. KFacial identity recognition is one of the challenging problems in the domain of computer vision. Facial identity comprises the facial attributes of a person’s face ranging from age progression, gender, hairstyle, etc. Manipulating facial attributes such as changing the gender, hairstyle, expressions, and makeup changes the entire facial identity of a person which is often used by law offenders to commit crimes. Leveraging the deep learning-based approaches, this work proposes a one-step solution for facial attribute manipulation and detection leading to facial identity recognition in few-shot and traditional scenarios. As a first step towards performing facial identity recognition, we created the Facial Attribute Manipulation Detection (FAM) Dataset which consists of twenty unique identities with thirty-eight facial attributes generated by the StyleGAN3 inversion. The Facial Attribute Detection (FAM) Dataset has 11,560 images richly annotated in YOLO format. To perform facial attribute and identity detection, we developed the Spatial Transformer Block (STB) and Squeeze-Excite Spatial Pyramid Pooling (SE-SPP)-based Tiny YOLOv7 model and proposed as FIR-Tiny YOLOv7 (Facial Identity Recognition-Tiny YOLOv7) model. The proposed model is an improvised variant of the Tiny YOLOv7 model. For facial identity recognition, the proposed model achieved 10.0% higher mAP in the one-shot scenario, 30.4% higher mAP in the three-shot scenario, 15.3% higher mAP in the five-shot scenario, and 0.1% higher mAP in the traditional 70% − 30% split scenario as compared to the Tiny YOLOv7 model. The results obtained with the proposed model are promising for general facial identity recognition under varying facial attribute manipulation.