scholarly journals COMPRESSIVE STRENGTH OF HIGH PERFORMANCE CONCRETE USING GRANITE POWDER AS FINE AGGREGATE

2015 ◽  
Vol 04 (16) ◽  
pp. 47-49 ◽  
Author(s):  
Raghavendra R .
Keyword(s):  
Compressive Strength ◽  
High Performance ◽  
High Performance Concrete ◽  
Fine Aggregate ◽  
Granite Powder

scholarly journals Effect of Mineral Aggregates and Chemical Admixtures as Internal Curing Agents on the Mechanical Properties and Durability of High-Performance Concrete

Materials ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2090 ◽  
Author(s):  
Francisco Javier Vázquez-Rodríguez ◽  
Nora Elizondo-Villareal ◽  
Luz Hypatia Verástegui ◽  
Ana Maria Arato Tovar ◽  
Jesus Fernando López-Perales ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Fly Ash ◽  
High Performance ◽  
High Performance Concrete ◽  
Chloride Ions ◽  
Internal Curing ◽  
Fine Aggregate ◽  
Pumice Stone ◽  
Chemical Admixtures ◽  
Mineral Aggregates

In the present work, the effect of mineral aggregates (pumice stone and expanded clay aggregates) and chemical admixtures (superplasticizers and shrinkage reducing additives) as an alternative internal curing technique was investigated, to improve the properties of high-performance concrete. In the fresh and hardened state, concretes with partial replacements of Portland cement (CPC30R and OPC40C) by pulverized fly ash in combination with the addition of mineral aggregates and chemical admixtures were studied. The physical, mechanical, and durability properties in terms of slump, density, porosity, compressive strength, and permeability to chloride ions were respectively determined. The microstructural analysis was carried out by scanning electronic microscopy. The results highlight the effect of the addition of expanded clay aggregate on the internal curing of the concrete, which allowed developing the maximum compressive strength at 28 days (61 MPa). Meanwhile, the replacement of fine aggregate by 20% of pumice stone allowed developing the maximum compressive strength (52 MPa) in an OPC-based concrete at 180 days. The effectiveness of internal curing to develop higher strength is attributed to control in the porosity and a high water release at a later age. Finally, the lowest permeability value at 90 days (945 C) was found by the substitutions of fine aggregate by 20% of pumice stone saturated with shrinkage reducing admixture into pores and OPC40C by 15% of pulverized fly ash. It might be due to impeded diffusion of chloride ions into cement paste in the vicinity of pulverized fly ash, where the pozzolanic reaction has occurred. The proposed internal curing technology can be considered a real alternative to achieve the expected performance of a high-performance concrete since a concrete with a compressive strength range from 45 to 67 MPa, density range from 2130 to 2310 kg/m3, and exceptional durability (< 2000 C) was effectively developed.

scholarly journals The use of ceramics as an internal curing agent in high performance concrete with variable temperature curing to improve mechanical characteristics

2022 ◽  
Vol 961 (1) ◽  
pp. 012024
Author(s):  
Abdulrasool Thamer Abdulrasool ◽  
Noor R. Kadhim ◽  
Safaa S. Mohammed ◽  
Ahmed Abdulmueen Alher
Keyword(s):  
Compressive Strength ◽  
High Performance ◽  
Mechanical Characteristics ◽  
High Performance Concrete ◽  
Variable Temperature ◽  
Porous Ceramic ◽  
Autogenous Shrinkage ◽  
Internal Curing ◽  
Fine Aggregate ◽  
Fine Aggregates

Abstract Concrete curing is one of the most significant factors in the development of compressive strength, and a high temperature difference during curing may reduce strength. The microcracks created in the concrete as a result of the constant temperature change cause this exudation. Internal curing has become popular for decreasing the risk of early-age cracking in high-performance concrete by limiting autogenous shrinkage (HPC). This study looks at the effectiveness of internal wet curing offered by a new kind of aggregate called “recycled waste porous ceramic fine aggregates”. The evolution of measured mechanical characteristics is examined on three distinct HPCs, both with and without internal curing materials. Ceramic fine aggregates were used to replace two different quantities of regular weight fine aggregate. Ceramic fine aggregates were shown to be quite beneficial for internal cure. It has been discovered that incorporating 20% ceramic fine aggregates into HPC improves the properties of the material, resulting in low internal stress and a large improvement in compressive strength. It should be emphasized that, unlike some traditional lightweight aggregates, no loss in compressive strength has been seen for the various quantities of ceramic fine aggregates introduced at either early or later ages.

scholarly journals Non-Tuned Machine Learning Approach for Predicting the Compressive Strength of High-Performance Concrete

Materials ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1023
Author(s):  
Abobakr Khalil Al-Shamiri ◽  
Tian-Feng Yuan ◽  
Joong Hoon Kim
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Prediction Model ◽  
Extreme Learning Machine ◽  
High Performance ◽  
High Performance Concrete ◽  
Fine Aggregate ◽  
Input Variables ◽  
Prediction Reliability ◽  
Learning Machine

Compressive strength is considered as one of the most important parameters in concrete design. Time and cost can be reduced if the compressive strength of concrete is accurately estimated. In this paper, a new prediction model for compressive strength of high-performance concrete (HPC) was developed using a non-tuned machine learning technique, namely, a regularized extreme learning machine (RELM). The RELM prediction model was developed using a comprehensive dataset obtained from previously published studies. The input variables of the model include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age of specimens. k-fold cross-validation was used to assess the prediction reliability of the developed RELM model. The prediction results of the RELM model were evaluated using various error measures and compared with that of the standard extreme learning machine (ELM) and other methods presented in the literature. The findings of this research indicate that the compressive strength of HPC can be accurately estimated using the proposed RELM model.

scholarly journals Producing Heavyweight High-Performance Concrete by Using Black Sand as Newly Shielding Construction Material

Materials ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5353
Author(s):  
Khaled A. Eltawil ◽  
Mohamed G. Mahdy ◽  
Osama Youssf ◽  
Ahmed M. Tahwia
Keyword(s):  
Compressive Strength ◽  
High Performance ◽  
Elevated Temperatures ◽  
Gamma Ray ◽  
High Performance Concrete ◽  
Construction Material ◽  
Construction Materials ◽  
Linear Attenuation Coefficient ◽  
Fine Aggregate ◽  
Black Sand

Experimental work was carried out to study new fine aggregate shielding construction materials, namely black sand (BS). The BS effect on the mechanical, durability, and shielding characteristics of heavyweight high-performance concrete (HWHPC) was evaluated. This study aimed at improving various HWHPC properties, concertedly. Fifteen mixtures of HWHPC were made, with various variables, including replacing 10% and 15% of the cement with fly ash (FA) and replacing normal sand by BS at various contents (15%, 30%, 45%, 60%, 75%, and 100%). The test specimens were subjected to various exposure conditions, including elevated temperatures, which ranged from 250 °C to 750 °C, for a duration of 3 h; magnesium sulfate (MS) exposure; and gamma-ray exposure. The effects of elevated temperature and sulfate resistance on concrete mass loss were examined. The results revealed that BS is a promising shielding construction material. The BS content is the most important factor influencing concrete compressive strength. Mixes containing 15% BS demonstrated significantly better strength compared to the control mixes. Exposure to 250 °C led to a notable increase in compressive strength. BS showed a significant effect on HWHPC fire resistance properties, especially at 750 °C and a significant linear attenuation coefficient. Using 10% FA with 15% BS was the most effective mixing proportion for improving all HWHPC properties concertedly, especially at greater ages.

scholarly journals Setting Time of Granite Powder Blended Banana Fibre Reinforced High Performance Concrete

2020 ◽  
Vol 9 (4) ◽  
pp. 891-896
Keyword(s):  
Compressive Strength ◽  
Reinforced Concrete ◽  
High Performance ◽  
High Performance Concrete ◽  
Setting Time ◽  
Hardened Concrete ◽  
Optimum Level ◽  
Fibre Reinforced Concrete ◽  
Banana Fibre ◽  
Granite Powder

The investigation is intended to analyse the variations in setting time behaviour of high performance fibre reinforced concrete (HPFRC) after adding granite powder as substitute for cement. The replacement of cement by granite powder was consider at the rate of 0, 5, 10, 15 and 20%. In order to improve the performance of concrete, banana fibres are added in concrete at 0.25, 0.5, 0.75, 1.0, and 1.25% by weight of cement. High strength concrete with M50 grade was designed and investigated in this study. The investigation was carried out in two stages, the stage one was predicting the optimum level of adding banana fibre based on the strength performance and the stage two was evaluating the variations in setting time performance of hardened concrete due to addition of granite powder. The setting time of concrete was conducted using penetration resistance test as per IS: 8142-1976. The slump value of the HPFRC was maintained by 80-100 mm and the dosage of superplasticizer was modified accordingly without varying the w/c ratio. Based on the results obtained in this investigation, 1% banana fibre was taken for developing the fibre reinforced concrete without affecting the 28 days compressive strength. The substitution of granite powder beyond 15% increases the setting time significantly and also affect the strength of HPFRC. The relationship between the setting time and 28 days compressive strength was predicted.

Study on early Mechanical Performance of High Performance Concrete with Low Clinker Influenced by Rubber Powder

2014 ◽  
Vol 584-586 ◽  
pp. 1738-1741
Author(s):  
Qing Hai Meng ◽  
Li Hua Lv ◽  
Xu Yan
Keyword(s):  
Compressive Strength ◽  
Flexural Strength ◽  
High Performance ◽  
Bending Strength ◽  
Mechanical Performance ◽  
High Performance Concrete ◽  
Fine Aggregate ◽  
Rubber Powder ◽  
Concrete Mechanical Properties ◽  
Strength And Ductility

Selecting rubber powders, which is divided into 80 mesh and 150 mesh, as the research object, to understand the influence of high performance of lower clinker concrete mechanical properties of rubber powder with different varieties and volume. Taking the compressive strength, flexural strength and ratio of flexural strength to compressive strength as an indicator, the thesis explores the influence of the high performance concrete with low clinker, which rubber powder are mixed into as fine aggregate, on the compressive strength bending strength and ductility.

Estimating Distribution of Concrete Strength Using Quantile Regression Neural Networks

2014 ◽  
Vol 584-586 ◽  
pp. 1017-1025 ◽  
Author(s):  
I Cheng Yeh
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Quantile Regression ◽  
Blast Furnace Slag ◽  
High Performance ◽  
Coarse Aggregate ◽  
High Performance Concrete ◽  
Concrete Strength ◽  
Fine Aggregate ◽  
Furnace Slag

This paper is aimed at demonstrating the possibilities of adaptingQuantile Regression Neural Network (QRNN) to estimate the distribution ofcompressive strength of high performance concrete (HPC). The databasecontaining 1030 compressive strength data were used to evaluate QRNN. Each dataincludes the amounts of cement, blast furnace slag, fly ash, water,superplasticizer, coarse aggregate, fine aggregate (in kilograms per cubicmeter), the age, and the compressive strength. This study led to the followingconclusions: (1) The Quantile Regression Neural Networks can buildaccurate quantile models and estimate the distribution of compressive strengthof HPC. (2) The various distributions of prediction of compressive strength of HPCshow that the variance of the error is inconstant across observations, whichimply that the prediction is heteroscedastic. (3) The logarithmic normaldistribution may be more appropriate than normal distribution to fit thedistribution of compressive strength of HPC. Since engineers should not assumethat the variance of the error of prediction of compressive strength isconstant, the ability of estimating the distribution of compressive strength ofHPC is an important advantage of QRNN.

scholarly journals PREDICTION OF STRENGTH AND SLUMP OF RICE HUSK ASH INCORPORATED HIGH-PERFORMANCE CONCRETE

2012 ◽  
Vol 18 (3) ◽  
pp. 310-317 ◽  
Author(s):  
Md. Nazrul Islam ◽  
Muhammad Fauzi Mohd Zain ◽  
Maslina Jamil
Keyword(s):  
Experimental Data ◽  
Compressive Strength ◽  
Rice Husk ◽  
High Performance ◽  
Rice Husk Ash ◽  
High Performance Concrete ◽  
Fine Aggregate ◽  
Aggregate Content ◽  
Binder Ratio ◽  
Water To Binder Ratio

This paper describes the development of statistical models to predict strength and slump of rice husk ash (RHA) incorporated high-performance concrete (HPC). Sixty samples of RHA incorporated HPC mixes having compressive strength range of 42–92 MPa and slump range of 170–245 mm were prepared and tested in the laboratory. These experimental data of sixty RHA incorporated HPC mixes were used to develop two models. Six variables namely water-to-binder ratio, cement content, RHA content, fine aggregate content, coarse aggregate content and superplasticizer content were selected to develop the models and ultimately to predict strength and slump of RHA incorporated HPC. The models were developed by regression analysis. Additional five HPC mixes were prepared with the same ingredients and tested under the same testing conditions to verify the ability of the proposed models to predict the responses. The results of the prediction of the models showed good agreement with the experimental data. Thus the developed models can be used to predict slump and 28-day compressive strength of RHA incorporated HPC. The research demonstrated that strength and slump of HPC could be successfully modeled using statistical analysis.

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