ABSTRACT Cracks in concrete structures are caused due to contraction and expansion irregularities, from potential damages caused in the buildings. These irregularities and damages are assessed by the engineers manually or through identification and prediction models with machine learning techniques to evaluate the impact and significance of the structural health in buildings. This research aims at applying machine learning based on VGG16-Net model for the detection of cracks in concrete structures. The proposed model is of CNN (convolutional neural network) + VGG neural network based architecture. The study uses the gradient boosting algorithm for image segmentation. The datasets are obtained from “Kaggle” resource and the library used is ‘Hugging Face Transformers’. To evaluate the developed models’ performance metrics such as “accuracy, precision, recall and f1-score” are used. The ‘accuracy’ score obtained is compared against the ‘ViT’ (Google transformer) accuracy rate, for comparison. The proposed model achieved 98% validation accuracy rate with 0.3% loss. Thus the developed research contributes an innovative and a novel ML model that predicts and identifies the cracks in concrete structures with less loss and higher accuracy with CNN architecture than ViT (vision transformer) models. Current study also provides more input upon CNN being more accurate than ViT models for future researchers for comparative analyses.

Abstract: In the presented paper, an attempt has been made to first find the permeability of the Euphorbia tortilis cactus (ETC) concrete by the water permeability method and infiltration method. After that, the flexural strength of the ETC RC beam wrapped with AFRP kelvar 149 is carried out by a 2-point load test. This research aimed to develop a more durable, flexural, and sustainable beam under static load. Based on the state-of-the-art information available in the literature, 3-layer Kelvar 149 AFRP is considered as a laminate to solve the deflections of the ETC beam. In this project, RCC beams were strengthened by ETC and aramid FRP sheets. Novel results are obtained by different layers and patterns of Aramid FRP sheets. Based on the investigation 3-layers Kelvar 149 perform well than a normal concrete beam. As no result based on hydraulic conductivity and drying shrinkage of a beam with AFRP laminates are available in the literature, the obtained results are validated with the finite element method (ABAQUS) under static load conditions.

Abstract: In the presented research paper, investigated the torsional performance of reinforced concrete beams with light emissions grade of cement utilizing Carbon fiber and its relative investigation with aramid fiber, for its torsional conduct, utilizing both exploratory and insightful strategies. A distinctive example of its strips folded over RC beams and the torsional conduct of these restrengthened beams is considered. Carbon fiber (CF) is utilized as outside support. Reinforced concrete beam retrofitted with CF tried for torsional disappointment utilizing lever arms exposed to torque. The beams have dimensions of 150mm in width and 200mm in depth and 1 m long, as planned according to IS456-2000. Three bars are intended for a twist. Steel has a stronger ultimate strength but a lower density than aramid FRP, as well as being easier to install and requiring no interim support until it reaches its full strength. The impact of various sorts and designs of CF on the initial stage of breaking load, extreme load conveying limit, and disappointment method of the beam are thought about and its relative examination utilizing finite element programming with aramid fiber gives a better outcome for additional investigation.

ABSTRACT In the present work, Cu2+ substituted cobalt ferrite (Co1-xCuxFe2O4, x = 0, 0.3, 0.5, 0.7 and 1) magnetic nanopowders were synthesized via chemical co-precipitation method. The prepared powders were investigated by various characterization methods such as X-ray diffraction analysis (XRD), scanning electron microscope analysis (SEM), vibrating sample magnetometer analysis (VSM) and fourier transform infrared spectroscopy analysis (FTIR). The XRD analysis reveals that the synthesized nanopowders possess single phase centred cubic spinel structure. The average crystallite size of the particles ranging from 27-49 nm was calculated by using Debye-scherrer formula. Magnetic properties of the synthesized magnetic nanoparticles are studied by using VSM. The VSM results shows the magnetic properties such as coercivity, magnetic retentivity decreases with increase in copper substitution whereas the saturation magnetization shows increment and decrement in accordance with Cu2+ substitution in cobalt ferrite nanoparticles. SEM analysis reveals the morphology of synthesized magnetic nanoparticles. FTIR spectra of Cu2+ substituted cobalt ferrite magnetic nanoparticles were recorded in the frequency range 4000-400cm-1. The spectrum shows the presence of water adsorption and metal oxygen bonds. The adhesion nature of Cu2+ substituted cobalt ferrite magnetic nanoparticles with bacteria in reviewed results indicates that the synthesized nanoparticles could be used in biotechnology and biomedical applications.