Models for Predictions of Mechanical Properties of Low-Density Self-compacting Concrete Prepared from Mineral Admixtures and Pumice Stone

Kumar, Bipul; Sangeetha, G.; Srinivas, A.; Awoyera, Paul O.; Gobinath, R.; Ramana, V. Venkata

doi:10.1007/978-981-15-0184-5_58

Cited by 20 publications

(1 citation statement)

References 22 publications

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“…A low-density SCC containing pumice and mineral admixtures was also studied by Arun Kumar et al [15]. They applied the principle of artificial neural network for the modelling of the mechanical properties of a lightweight SCC.…”

Section: Literature Reviewmentioning

confidence: 99%

Experimental Investigation of the Pozzolanic Potentials of Millet and Rice Husk Ash as Mineral Additive in Self-Compacting Concrete

John¹,

Oluwatosin²

2020

Acta Polytech

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Self-compacting concrete (SCC) is a new concept of concrete mix which flows in a formwork and consolidates itself without the need for compaction. Effectively compacting concrete can be very difficult especially in areas with a high number of reinforcement. Millet Husk Ash (MHA) and Rice Husk Ash (RHA) are agricultural waste materials obtained from farm and burnt to ashes to discard them since they are environmental waste. This research is focused on finding the pozzolanic potentials of MHA and RHA as a mineral additive in SCC to see if it will improve its properties rather than discarding them as environmental waste. Laboratory investigations were carried out on normally vibrated concrete (NVC) and SCC using MHA and RHA as an additive at a 10 % replacement with cement. Workability tests were carried out following the BS specifications. ASTM 293 C was used for the Flexural Capacity test on the beam specimen. The Results of the workability tests using MHA and RHA as mineral additive are within the specified standard values. The compressive strength test also revealed that the SCC using MHA is about 12.8 % higher than the RHA and NVC at 28 days with densities of 2487.5, 2516.5 and 2437.5kg/m3 respectively. The Modulus of Rupture (MoR) and Split Tensile strength for MHA is 0-19.2 % and 17.2-22.2 % higher than the RHA and NVC respectively. It was concluded that the improvement in the pozzolanic properties of MHA and RHA may be due to the content of Lime (CaO), Silica ((SiO2)), Alumina (Al2O3), Iron oxide (Fe2O3) being greater than 70 % and in an accordance with BS 618 code. It is concluded that the MHA and RHA can find suitable applications in the SCC as a mineral additive rather than discarding them as environmental waste.

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Section: Literature Reviewmentioning

confidence: 99%

Experimental Investigation of the Pozzolanic Potentials of Millet and Rice Husk Ash as Mineral Additive in Self-Compacting Concrete

John¹,

Oluwatosin²

2020

Acta Polytech

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Short‐term electric power load forecasting using factor analysis and long short‐term memory for smart cities

Veeramsetty¹,

Chandra²,

Salkuti

2020

Circuit Theory & Apps

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Electric load estimation is an important activity for electrical power system operators to operate the system stably and optimally. This paper develops a machine learning model with a long short-term memory and a factor analysis to predict the load at a specific hour of the day on an electrical power substation. Historical load data from the 33-/11-kV substation near Kakatiya University in Warangal are taken at each hour of the day for the period from September 2018 to November 2018. A new long short-term memory architecture with factor analysis is being designed based on the approach used to predict substation loads by simulation in Microsoft Azure Notebooks. Based on the study, it was found that the proposed design predicts loads with good accuracy.

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Short Term Active Power Load Forecasting Using Machine Learning with Feature Selection

Veeramsetty

Chandra²,

Salkuti

2022

Lecture Notes in Electrical Engineering

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Models for Predictions of Mechanical Properties of Low-Density Self-compacting Concrete Prepared from Mineral Admixtures and Pumice Stone

Cited by 20 publications

References 22 publications

Experimental Investigation of the Pozzolanic Potentials of Millet and Rice Husk Ash as Mineral Additive in Self-Compacting Concrete

Experimental Investigation of the Pozzolanic Potentials of Millet and Rice Husk Ash as Mineral Additive in Self-Compacting Concrete

Short‐term electric power load forecasting using factor analysis and long short‐term memory for smart cities

Short Term Active Power Load Forecasting Using Machine Learning with Feature Selection

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