Department of Civil Engineering

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Author: search.filters.author.Barai, Sudhir KumarSubject: search.filters.subject.Concrete

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Now showing 1 - 7 of 7
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    Temperature, porosity and strength relationship for fire affected concrete
    (Springer, 2022-02) Barai, Sudhir Kumar
    In a fire incident, structural members are mostly unevenly exposed to temperatures and consequently suffer uneven damage. To rehabilitate and restore these for future usage, it is essential to correctly map the temperature field that the structural elements were subjected to during fire events. The majority of the existing relationships for temperature prediction apply to reinforced concrete beams only. In the present study, a material-porosity-based approach is proposed. Normal and high strength concrete structural elements were exposed to a range of elevated temperatures, and reserved compressive strength was evaluated. Another set of the same specimens were used to determine porosity using four techniques. Based on the observations, correlations among temperature, strength, and porosity for normal and high strength concrete are proposed. The suggested methodology and expressions may be used to predict the reserved strength and temperature field that the structural elements may have been exposed to, based on the evaluated porosity of concrete. Back-scattered electron Imaging was found to be the most fitting method for porosity evaluation.
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    Recycled Aggregate Concrete: Particle Packing Method (PPM) of Mix Design Approach
    (Springer, 2019) Pradhan, Subhasis; Barai, Sudhir Kumar
    The reuse of old concrete as a source of aggregate is a reliable alternative to Natural Aggregate (NA) in concrete construction. Because of poor quality of Recycled Aggregate (RA), the performance of Recycled Aggregate Concrete (RAC) is not up to the mark in fresh stage and hardened stage as compared to Natural Aggregate Concrete (NAC). In this work, Recycled Coarse Aggregate (RCA) is replaced 100% to produce RAC. The Particle Packing Method (PPM) is proposed for the mix proportioning of concrete. In PPM, the smaller particles are selected to fill up the voids between large particles and so on. PPM is found out to be cost-effective than IS code method of mix design because of the requirement of lesser quantity of cement. The 7 and 28 days compressive strength of conventional concrete and RAC using PPM design mix are very similar. But, the increment in compressive strength from 28 to 90 days curing is higher for NAC than RAC
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    Use of machine learning based technique to X-ray microtomographic images of concrete for phase segmentation at meso-scale
    (Elsevier, 2020-07) Barai, Sudhir Kumar; Pradhan, Subhasis
    The paper discusses the technical limitation of the gray value thresholding technique to detect the voids, aggregate and mortar phases. A two-stage image processing methodology is proposed for the segmentation of the three phases of concrete using the X-ray microtomographic images. In the first stage, the gray value thresholding technique is used to detect the voids. A machine learning based technique is proposed in the second stage for the segmentation of aggregate and mortar. The training data is used to model a planar decision boundary using the logistic regression method. For this, the radial distance from the centre of the image, gray value, and gray value of the filtered embossed image features are considered. The accuracy of the model to quantify the voids is validated with the commercial software. The machine learning model based on logistic regression method exhibits very good accuracy () in detecting the aggregate.
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    Application of Novel Radial Thresholding Method for the Segmentation of Different Phases from X-Ray Microtomographic Images of Concrete
    (Springer, 2020-11-14) Barai, Sudhir Kumar
    Concrete is a composite material, which can be segmented into three major phases, namely voids, aggregates and mortar. This paper presents the digital image processing techniques based on grey value thresholding and a novel radial thresholding approach to segregate the three phases of concrete. In this context, the 8-bit images of concrete specimen obtained from X-ray microtomography (XRT) scanning of cylindrical specimen are operated. The non-local means denoising filter is used to remove the unwanted noise from the original images and enhance their clarity without losing any details. There is a clear distinction in the grey values of air voids from that of aggregates and mortar. The threshold grey value of air voids is determined by observing the variation in grey value profile near the edges of the air voids, and using this threshold grey value, air voids are segmented assuredly. However, the segmentation of phases using this thresholding technique doesn’t suffice to isolate the aggregates from mortar because of the overlap of their grey values. Hence, a radial thresholding method is proposed for the detection and determination of the phases, which works similar to our eyes. The grey value vs radius graph exhibits sudden jumps, which represent the change in contrast, that is, phase. The change in phases is evaluated by using a simple function, ∥GV(n)−GV(n−1)|−L|>0 which is considered the radial variation for every degree rotation. The estimated air voids and aggregates content are 0.91 and 49.19%, respectively. The error in the detection of aggregates content is only 0.6%.
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    Chloride diffusion study in different types of concrete using finite element method (FEM)
    (Techno Press, 2014-03) Barai, Sudhir Kumar
    Corrosion in RCC structures is one of the most important factors that affects the structure‟s durability and subsequently causes reduction of serviceability. The most severe cause of this corrosion is chloride attack. Hence, to prevent this to happen proper understanding of the chloride penetration into concrete structures is necessary. In this study, first the mechanism of this chloride attack is understood and various parameters affecting the process are identified. Then an FEM modelling is carried out for the chloride diffusion process. The effects of fly ash and slag on the diffusion coefficient and chloride penetration depth in various mixes of concretes are also analyzed through integrating Virtual RCPT Lab and FEM.
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    Thermal and mechanical properties of concrete and its constituents at elevated temperatures: A review
    (Elsiever, 2021-02-08) Barai, Sudhir Kumar
    To retrofit a fire damaged structural element, it is important to understand the extent of thermal exposure of such structural element. It is presumed that the extent of damage of fire affected structural elements may be assessed with the knowledge of the properties of their constituent materials at elevated temperatures. The fire resistance of concrete and temperature propagation inside the structural element depends on the thermal and mechanical properties of the ingredients and also on concrete heterogeneity and its compactness. Different crystallographic arrangements of materials result in differential thermal expansion of cement-sand matrix and coarse aggregates. Since concrete consists of a major proportion of coarse aggregates, special focus has been given on the thermal and mechanical properties of different types of coarse aggregate at elevated temperatures. The objective of this paper is to present a brief critical review of the thermal and mechanical properties of different types of concrete and its constituents at elevated temperatures and highlight certain areas that may be explored further by researchers.
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    Use of machine learning based technique to X-ray microtomographic images of concrete for phase segmentation at meso-scale
    (Elsiever, 2020-07) Barai, Sudhir Kumar
    The paper discusses the technical limitation of the gray value thresholding technique to detect the voids, aggregate and mortar phases. A two-stage image processing methodology is proposed for the segmentation of the three phases of concrete using the X-ray microtomographic images. In the first stage, the gray value thresholding technique is used to detect the voids. A machine learning based technique is proposed in the second stage for the segmentation of aggregate and mortar. The training data is used to model a planar decision boundary using the logistic regression method. For this, the radial distance from the centre of the image, gray value, and gray value of the filtered embossed image features are considered. The accuracy of the model to quantify the voids is validated with the commercial software. The machine learning model based on logistic regression method exhibits very good accuracy () in detecting the aggregate.