Conference Papers

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Author: search.filters.author.Palanisamy, T.

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    ANN Model for Joint Shear Strength of RC Interior Beam-Column Joint
    (Springer Science and Business Media Deutschland GmbH, 2022) Alagundi, S.; Palanisamy, T.
    In the present study ANN model is developed to anticipate the Joint shear strength of interior Beam-Column joints. As there are many factors and parameters that influence the joint strength, it is challenging to determine the joint shear strength of joint. The current research aims to predict the Joint shear strength of the Beam-Column joint with the help of Artificial Intelligence. ANN models have recently gained popularity in Civil and Structural Engineering and have solved many non liner engineering problems. In the present research, ANN Model is constructed and the model is trained, tested and validated. Performance of the ANN model is measured by statistical relations. Error analysis is carried out to find out the deviation from experimental values. As the mean square error is less and correlation is nearly 95–100%, it has been concluded that the Present ANN model can accurately predict the Joint shear strength. The proposed ANN model is compared with design equations proposed by design code and found out that the ANN model shows more stability and accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Mechanical Properties of Fiber Reinforced Concrete by using Sisal Fiber with M-Sand as Fine Aggregate
    (Association of American Publishers, 2022) Gnanasundar, V.M.; Palanisamy, T.; Thirugnanam, G.S.; Preetha, V.
    Conventional concrete has a low tensile strength, constrained ductility and little protection from crack propagation. The inward miniaturized scale of cracks, prompting weak disappointment of concrete. Investigations have been carried out in many countries on various mechanical properties, physical performance and durability of cement-based matrices reinforced with naturally occurring fibers including sisal, coconut, jute, bamboo, and wood fibers. Raised natural mindfulness and an expanding worry with an unnatural weather change have invigorated the search for materials that can supplant traditional engineered fiber. Characteristic fiber, for example, sisal strands show up as one of the great options since they are accessible in sinewy structure and can be separated from plant leaves, stalk, and products of the soil at exceptionally low expenses. In this work, the impact of sisal strands on the quality of cement for M25 evaluation has been examined by shifting the level of filaments in concrete. Fiber substance were shifted by 0.05%, 0.10%, 0.15%, 0.20%, 0.25%, 0.30%, 0.35% and 0.40% by volume of cement. Cubes, Cylinder and Prism were cast to assess the Compressive, Split Tensile and Flexural Strength test. Every one of the samples was tested for a time of 28 days curing. The results of fiber reinforced concrete for 28 days curing with a varied percentage of fiber were studied and it has been found that there is significant strength improvement with addition of sisal fiber in concrete. © 2022, Association of American Publishers. All rights reserved.
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    Evaluation of Mechanical Properties on Light Weight Concrete by using Silica Fume with M-Sand
    (Association of American Publishers, 2022) Gnanasundar, V.M.; Palanisamy, T.; Refak Afrith, M.; Pradeepkumar, B.
    Light weight Concrete (LWC) is that the building material employed in the development of building utilization the most recent technology to cut back the self-weight of building. Silica fume is added for achieve strength. Silica fume is added in the percentage of 0%, 10%, 15% and 20%. Light weight Concrete prepared by exploitation light weight combinations (pumice stone) or volcanic stone or silicon oxide. Admixture metal powder as associate in nursing air-entraining agent to the conventional combine concrete. Light weight concrete is restricted to sure functions compared to traditional concrete. However, the introduction of light weight concrete offers additional different to the development business, that presently focuses on natural resources. Light weight concrete plays a distinguished role in reducing the density and to extend the thermal insulation. The density of light weight concrete varies from 1440 to 1840kg/m3 . By exploitation the sunshine weight concrete it minimizes the earthquake or any environment impact. Generally, light weight concrete has wonderful thermal and sound insulating properties, an honest hearth rating, non-combustible and offers price savings through construction speed and simple handling. Then light weight concrete is great for rooftop deck fixes, support profiles, raised floor chunks, and floor deck overlays. Light weight concrete has a lower temperature move rate than typical weight concrete, bringing about better protection. The principal advantage of lightweight cement is that it is incredibly fast and comparatively easy in construction. Light weight combination particle strength varies with kind and strength. In some cases, compressive strength may be exaggerated by commutation a part of the fine light weight combination with smart quality natural sand. © 2022, Association of American Publishers. All rights reserved.
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    Experimental Analysis of Glass Fibre in Concrete
    (Association of American Publishers, 2022) Gnanasundar, V.M.; Palanisamy, T.; Thirugnanam, G.S.; Vishalachi, C.
    Compared compared to concrete in a construction, the essential portion of the structure has higher weight, however steel utilised as reinforcement has no weight. To address this problem, the Glass Fibre Reinforced Concrete (GFRC) material was developed. Polymers and glass fibre are impregnated in the cementation framework of GFRC, which is a material. Glass fibre, Fly ash, silica sand, Portland cement and water are all components of concrete. The glass content, mix procedure, and casting process all have an impact on the qualities of GFRC concrete. We present the fibre glass as well as other characteristic synthetic chemicals in GFRC to develop a material that is extremely solid and adaptableto construction.By this research, using 0.5 percent and 0.1 percent glass fibres increases compressive and flexural strength of concrete for 7,14 and 28 days with no admixtures. © 2022, Association of American Publishers. All rights reserved.
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    Numerical Modeling on Buckling Behavior of Structural Stiffened Panel
    (Springer Science and Business Media Deutschland GmbH, 2023) Alagundi, S.; Palanisamy, T.
    Stiffened panels are essential building elements in weight-sensitive structures. They have various applications in marine, aircraft, and other structures. Plate structures can undergo buckling when subjected to axial compression loads and then exhibit out of plane displacements. The present work aims to study the buckling behavior of the stiffened panel. The finite element model of the stiffened panel is developed, and buckling analysis is performed using ANSYS software. This model is validated with the published experimental work. Once the model is validated, total of 320 numbers of models of stiffened panels with varying plate thickness, stiffener height, stiffener thickness, and distance between stiffeners are modeled in ANSYS-2020, and buckling analysis is performed. An artificial neural network model is proposed to predict the buckling load of the stiffened panel. Neural network model is created in MATLAB software, and it is trained, tested, and validated, and its performance is checked by statistical relations like coefficient of correlation and mean square error. Proposed ANN model shows high accuracy in the prediction of buckling load. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Intelligent Modeling for Shear Strength of RC Exterior Beam-Column Joint Subjected to Seismic Loading
    (Springer Science and Business Media Deutschland GmbH, 2023) Swapnil, B.; Palanisamy, T.
    RC beam-column joints are subjected to impounding shear demand and bond-slip during the event of an earthquake. Accurate prediction of joint shear strength is necessary to avoid brittle shear failure in design and retrofitting procedures. In this study the accurate shear strength of RC exterior beam-column joints are predicted by providing a contemporary intelligent modeling approach through eXtreme Gradient Boosting regressor (XGBoost), an ensemble learning technique that combines several weak learners to generate a strong predictive model. From the experimental results of diverse publications on exterior beam-column joints, parameters affecting joint shear strength are found through examination of current models, and a vast database is constructed. Eleven such parameters that describe the material property, geometric configuration and bond resistance, are chosen as the inputs, and joint shear strength as the output. The model is then trained, tested and validated on this database. The performance of this model is evaluated by various regression evaluation metrics such as MSE, RMSE, and R2. Comparison of this model with the existing empirical equation, code provisions, and even with an individual ML algorithm, demonstrated its superiority over all the models in terms of accuracy and computation time. Sensitivity analysis done using predictive power score (PPS) showed that the most important parameter for the estimation of the shear strength of RC exterior beam-column joint is the percentage of beam longitudinal reinforcement. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Review of Various Microbial Immobilization Methods Towards Self-healing Application
    (Springer Science and Business Media Deutschland GmbH, 2023) Baby, B.; Palanisamy, T.; Sundaramoorthi, S.
    Crack development and propagation in concrete structures, associated with internal and external stresses possess a severe threat to the performance and durability. Repair works of such concrete structures impart an immense financial toll. Self-healing mortar and concrete are developed with a view to provide a solution to address the aforementioned problem. The viability and performance of calcite precipitating microbes inside the concrete, in the long term, is always a concern when it comes to self-healing application. Among different methods to introduce such bacteria inside, immobilisation is considered to yield better results having the advantage of lesser impact from the adverse environment. This paper reviews the available immobilisation and encapsulation methods for microbial transport into the mortar or concrete, which makes use of porous media, hydrogels, polymeric coatings, etc., and its effectiveness in making a resilient building material. The current practices and the challenges associated with encapsulation methods to make a viable bio-mortar is critically reviewed and presented. The interaction of microbial colonies with the transporting medium and crack healing efficiency is compared based on different encapsulation methods. An experimental study was conducted to determine the impact of nutrients on the compressive strength of cement mortar was also conducted to identify the impact on strength parameters. The nutrients like calcium lactate, calcium nitrate, urea, calcium formate, and yeast extract in different dosages were analysed to achieve the optimum dosage value. It was observed nutrients other than urea and yeast extract, improved the compressive strength of bio-mortar at respective optimum dosages. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Predicting the Axial Load Carrying Capacity of Columns Reinforced with GFRP Rebars Using ANN Modelling
    (Springer Science and Business Media Deutschland GmbH, 2023) Sumesh Manohar, G.; Palanisamy, T.
    In recent years most of the concrete structures are getting exposed to environments that are resulting in the corrosion of steel. To eliminate this, studies have been carried out to replace steel in RCC by Glass Fiber Reinforced Polymer (GFRP) rebars. In this paper, several experimental results were considered and the impacts of substituting steel by GFRP rebars were studied. Parameters affecting the load-carrying capacity of columns reinforced with GFRP rebars were identified from various literature and a database has been created. Twelve such parameters describing the material property and geometric configuration are chosen as inputs and the axial load carrying capacity as an output. An ANN model is developed with optimized architecture for predicting the compressive strength of columns reinforced with GFRP rebars. The model is then trained, tested, and validated on this database. The accuracy of the ANN model is evaluated by various regression evaluation metrics such as MSE, RMSE and R2. Comparison with the existing empirical equations and code provisions showed that the ANN model outperformed all these models. For the purpose of determining the efficiency of ANN model, a subset of the experimental data collected from work done on GFRP reinforced columns is used. Sensitivity analysis is carried out and the results showed that the most important parameters for the estimation of the strength of GFRP reinforced columns are the geometrical dimensions of the column. The results obtained showed that the ANN model is in good agreement with the experimental results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Crack Detection in Concrete Using Artificial Neural Networks
    (Springer Science and Business Media Deutschland GmbH, 2023) Palanisamy, T.; Shakya, R.; Nalla, S.; Prakhya, S.S.
    This paper aims to explore the possibility of using machine learning (ML) algorithms and image processing to determine cracks in concrete and classify them as Cracked and Uncracked. This is a very current field of study with a lot of research currently taking place. In particular, neural network algorithms such as VGG16, ResNet50, Xception and MobileNet, were used to name a few. Two datasets were used to detect the presence of cracks in concrete. The first two datasets were taken from the Kaggle website. The first dataset is generated from 458 high-resolution images (4032 × 3024 pixels). This dataset consists of 40,000 images, 20,000 with and 20,000 without cracks. The second dataset had pictures of cracked and uncracked decks on a bridge from a dataset called SDNET2018 (2018). VGG16 Architecture based artificial neural network performed the best while MobileNet performed the worst. As the scope for the project expanded, an effort was made to determine crack properties, specifically crack width as an automated system for the same would be much more useful than a manual one. It was done using morphological transformations and concepts of Euclidean distance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Experimental Study on Durability and Mechanical Properties of Lightweight Mortar with Encapsulated Spore Forming Bacteria
    (Springer Science and Business Media Deutschland GmbH, 2023) Akshay, J.P.; Baby, B.; Palanisamy, T.
    Concrete is a material that is used worldwide for centuries as a construction material. Increased consumption of concrete leads to vulnerability of structure physically, chemically and biologically. Exposure to extreme conditions and detrimental effects of corrosion of reinforcements leads to cracking of concrete. Compressive strength, Flexural strength, and permeability can be affected by these cracks consequently leading to shortening the useful life of the concrete. Repairing and maintenance of these infrastructures need higher cement consumption and expenses. The benefits of microbial concrete can reduce the consumption of cement for structural replacements and maintenance works. Self-healing concrete by microbially induced calcite precipitating bacteria is an economical and sustainable solution, as it autonomously repairs small cracks. In this paper, we discuss the mechanism and performance of bio-concrete along with the idea of microbially induced calcite precipitation (MICP). The Bacillus species is proven to be an effective microbial agent for self-healing concrete. Hence the sample preparation is done using encapsulated spore-forming Bacillus Subtilis bacteria species as a biological healing agent. Three concentrations of 105, 107, and 109 cells per millilitre are adopted for sample preparation. The mix proportion of cement mortar is 1:3, using expanded perlite as a replacement for the fine aggregate in the percentages 10% was done. The mechanical and durable properties of bio-mortar are evaluated to ascertain the possibility of the same as a resilient building material. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.