Permeability properties of lightweight self-consolidating concrete made with coconut shell aggregate

Liquid substance intrusion into concrete is one of the issues that gradually damage its phys- ical and structural integrity. The permeability properties of lightweight self-consolidating concrete containing coconut shell aggregate was investigated in this study. A partial replace- ment of crushed rock (granite) with coconut shell from 0 to 100% in step of 25% was considered for the mixtures. Rice husk ash (RHA) and Silica fume (SF) were considered for developing binary and ternary blended self-consolidating concrete with total powder content of 450 kg/m3 and 550 kg/m3. The testing of concrete involved the saturated water absorption, sorptivity and chloride ingress, which were used to examine the permeability properties of the concrete developed. The laboratory investigations showed encouraging results with better performance up to 75% replacement of crushed granite with coconut shell aggregate

Impact Resistance and Strength Development of Fly Ash Based Self-compacting Concrete

The development of self-compacting concrete using alternative materials is expanding in recent years due to the technical and economic benefits of the mixture. This study focuses on the structural and compositional behavior of sodium hydroxide (NaOH)-activated fly ash based self-compacting concrete (SCC). Fly ash was partially replaced with Ordinary Portland Cement from 0–30%. The tests performed on concrete samples include workability, strength, microstructural, and impact resistance. The results showed that activated fly ash reduces the heat of the hydration process of the concrete mixture but enhances pozzolanic reactions, which led to increased strength properties. The addition of activated fly ash modifies the mineralogy of the concrete, as evident in strength characteristics. The best performance of the activated fly ash based SCC, in terms of strength, was found at 10–15% substitutions, which can somewhat reduce the cost of production of SCC and strength improvement advantage

Development and assessment of cement and concrete made of the burning of quinary by-product

The aim of this study is to evaluate the usability of new cement (NC) made by the burning of quinary by-product to make commercial binders. Chemical analysis of the by-products and NC as well as X-ray diffraction (XRD) analysis of NC, fineness, density, consistency, and setting time of NC paste, and slump in addition to compressive strength (CS) and splitting tensile strength (STS) of NC concrete (NCC) were conducted. The results suggested that chemical composition of by-products is suitable to make NC binder. The NC contains Ca3SiO5, Ca2SiO5, Ca3Al2O6, and Ca3Al2FeO10. The particles passing through the 200 um Sieve were 56% compared with 52% for Portland cement (PC). The density of the of NC was similar to that of PC. The NC needed 48% more water than PC for normal consistency. The initial and final setting-time of NC was 105 min and 225 min respectively which is much higher than that of PC (15 and 45 min). The slump, compressive strength and splitting tensile strength were slightly lower for concrete containing NC compared with that pf PC concrete. Although the CS and STS of NCC are the lowest, the rate of the CS and STS gain of NCC is greater than that of PCC. It was concluded that NC is a viable alternative to PC for the production of greener concrete

COVLIAS 3.0: cloud-based quantized hybrid UNet3+ deep learning for COVID-19 lesion detection in lung computed tomography

Background and noveltyWhen RT-PCR is ineffective in early diagnosis and understanding of COVID-19 severity, Computed Tomography (CT) scans are needed for COVID diagnosis, especially in patients having high ground-glass opacities, consolidations, and crazy paving. Radiologists find the manual method for lesion detection in CT very challenging and tedious. Previously solo deep learning (SDL) was tried but they had low to moderate-level performance. This study presents two new cloud-based quantized deep learning UNet3+ hybrid (HDL) models, which incorporated full-scale skip connections to enhance and improve the detections.MethodologyAnnotations from expert radiologists were used to train one SDL (UNet3+), and two HDL models, namely, VGG-UNet3+ and ResNet-UNet3+. For accuracy, 5-fold cross-validation protocols, training on 3,500 CT scans, and testing on unseen 500 CT scans were adopted in the cloud framework. Two kinds of loss functions were used: Dice Similarity (DS) and binary cross-entropy (BCE). Performance was evaluated using (i) Area error, (ii) DS, (iii) Jaccard Index, (iii) Bland–Altman, and (iv) Correlation plots.ResultsAmong the two HDL models, ResNet-UNet3+ was superior to UNet3+ by 17 and 10% for Dice and BCE loss. The models were further compressed using quantization showing a percentage size reduction of 66.76, 36.64, and 46.23%, respectively, for UNet3+, VGG-UNet3+, and ResNet-UNet3+. Its stability and reliability were proved by statistical tests such as the Mann–Whitney, Paired t-Test, Wilcoxon test, and Friedman test all of which had a p < 0.001.ConclusionFull-scale skip connections of UNet3+ with VGG and ResNet in HDL framework proved the hypothesis showing powerful results improving the detection accuracy of COVID-19

STUDY ON NEED FOR SUSTAINABLE DEVELOPMENT IN EDUCATIONAL INSTITUTIONS – A CASE STUDY OF COLLEGE OF ENGINEERING- GUINDY, CHENNAI

Sustainability has become the key word of developing world and it’s evident in many issues, the growing economy is facing nowadays. Sustainability is the need of the hour for Indian economy to support our future generation with a cleaner, safer environment. Legal framework implemented by governing bodies such as Pollution control board is also supporting the implementation of sustainable development by new enforcements introduced then and there, but it is questionable about the effectiveness of this frameworks. Most of the enforcements are focusing to imply the sustainability in industries or equivalent organizations but not putting thrust on all polluting bodies, educational institutions are one among them. Recent growth in educational scenario in India had increased the number of educational institutions to a large extent, also increased the effect on environment by their activities. Growth of educational sector and the number of institutions catering various fields of education is needed for India but the growth should be optimized in a way such that it’s sustainable and eco friendly. Various methods are developed recently to find out the exact problems associated with the environment, Geograpchial Information System (GIS) is one among them taking a big leap in the recent years in the area of environmental problem identification. This paper provides the details of the environmental impacts of educational institutions with case studies and also suggests a sustainable framework to make them environmental friendly by the use of (GIS)

STUDY ON NEED FOR SUSTAINABLE DEVELOPMENT IN EDUCATIONAL INSTITUTIONS, AN ECOLOGICAL PERSPECTIVE - A CASE STUDY OF COLLEGE OF ENGINEERING - GUINDY, CHENNAI

Sustainability has become the key word of developing world and it¿s evident in many issues, the growing economy is facing nowadays. Sustainability is the need of the hour for Indian economy to support our future generation with a cleaner, safer environment. Legal framework implemented by governing bodies such as Pollution control board is also supporting the implementation of sustainable development by new enforcements introduced then and there, but it is questionable about the effectiveness of this frameworks. Most of the enforcements are focusing to imply the sustainability in industries or equivalent organizations but not putting thrust on all polluting bodies, educational institutions are one among them. Recent growth in educational scenario in India had increased the number of educational institutions to a large extent, also increased the effect on environment by their activities. Growth of educational sector and the number of institutions catering various fields of education is needed for India but the growth should be optimized in a way such that it¿s sustainable and eco friendly. Various methods are developed recently to find out the exact problems associated with the environment, Geograpchial Information System (GIS) is one among them taking a big leap in the recent years in the area of environmental problem identification. This paper provides the details of the environmental impacts of educational institutions with case studies and also suggests a sustainable framework to make them environmental friendly by the use of (GIS)

Evaluation of Artificial Neural Network Predicted Mechanical Properties of Jute and Bamboo Fiber Reinforced Concrete Along with Silica Fume

The aim of the effort is to estimate the effect of jute and bamboo fibers with silica fume (SF) of different proportions on mechanical properties of concrete. Cube, cylinder and prism specimens are tested to determine the compressive, split tensile and flexural strength at 14 and 28 days of curing. To verify the experimental findings, further Artificial Neural Network (ANN) analysis is conducted. The study employs neural network (NN), such as the Neural Network-Leven Berg–Marquardt and the Neural Network Gradient Descent. In this investigation, feed-ahead lower back promulgation neural networks were employed. The NN predicted values are validated with actual values and the variation is found to be within 10% only. The predicted ANN results are compared with existing Response Surface Methodology model. Under compressive, split tensile and flexural load, the broken surface is examined at a smaller-scale level with a scanning electron microscope (SEM). The experimental results show that concrete with 0.5% bamboo fibers and 0.5% jute fibers with 10% SF had higher influence on the mechanical properties of concrete. When comparing ANN results, the suggested ANN model showed high level of accuracy in estimating the mechanical properties of natural-fiber-reinforced concrete. SEM examination displayed the failure pattern of concrete and fibers

Thermal and Acoustic Features of Lightweight Concrete Based on Marble Wastes and Expanded Perlite Aggregate

A large amount of industrial solid waste is generated from industrial activities worldwide. One such waste is marble waste, a waste generated from quarries which is generated in larger amount which needs attention. It is proved that this waste has a significant impact both on the people health and on the environment. Hence, research works are directed towards addressing usage of waste marble power, the aim of this experimental investigation is to study the usability of sand obtained by crushing marble waste (MWS) on the mixing of lightweight concrete based on expanded perlite aggregate (EPA). First, the mechanical, chemical, and physical properties of marble waste sand and expanded perlite aggregate were determined after which different mixtures of concrete are prepared by varying the percentage of EPA (0, 20, 40, 60, 80, and 100%), in order to find the optimum mixture focussing on obtaining best hydraulic properties. Also, in this work, the thermal and acoustic properties (thermal conductivity, thermal diffusivity, specific heat capacity and sound reduction index at different frequencies) of the tested concrete samples were investigated. Results shows that it is possible to obtain thermal and acoustic insulation lightweight concrete by using sand obtained by crushing marble wastes. Also, addition of more than 20% of EPA aggregate in concrete, develops a thermal insulating lightweight concrete which possess capacity to store heat and produce better thermal performance. Concrete blend with a percentage of more than of 20% of EPA aggregate can be placed in the category of acoustic insulation lightweight concrete. In summary, cement based on MWs and EPA provides better workability and energy saving qualities, which are economical and environmentally beneficial and may result in decreased construction budget and improve a long-term raw materials sustainability