Analysis of Mechanical Properties of Self Compacted Concrete by Partial Replacement of Cement with Industrial Wastes under Elevated Temperature

Abstract

Self-Compacting Concrete (SCC) differs from the normal concrete as it has the basic capacity to consolidate under its own weight. The increased awareness regarding environmental disturbances and its hazardous effects caused by blasting and crushing procedures of stone, it becomes a delicate and obvious issue for construction industry to develop an alternative remedy as material which can reduce the environmental hazards and enable high-performance strength to the concrete, which would make it durable and efficient for work. A growing trend is being established all over the world to use industrial byproducts and domestic wastes as a useful raw material in construction, as it provides an eco-friendly edge to the construction process and especially for concrete. This study aims to enlighten the use and comparative analysis for the performance of concrete with added industrial byproducts such as Ground Granulated Blast Furnace Slag (GGBFS), Silica fumes (SF) and Marble Powder (MP) in the preparation of SCC. This paper deals with the prediction of mechanical properties (i.e., compressive, tensile and flexural Strength) of self-compacting concrete by considering four major factors such as type of additive, percentage additive replaced, curing days and temperature using Artificial Neural Networks (ANNs). way to counter the hazardous effects of such materials. By using different percentages of materials as replacement for cement in concrete mix of grade M20 (Mix ratio 1:1.5:3) and a suitable water-cement (w/c) ratio 0.40, there is a broad analysis of performance of such high strength concrete with respect to the different adding ratios by testing the mechanical properties of concrete such as its compressive strength, split tensile strength and flexural strength [2]. Fresh properties for the SCC have also been monitored and examined, including the V-Funnel test, J-Ring test and Slump test. All these were carried out for the detail examination of fresh concrete behavior. Tests for hardened concrete were carried out using samples of different ratios of materials as a replacement in hardened cylinder and cube form. Many samples were casted for the comprehensive testing process. The samples with replacement ratios of 5%, 10%, 15%, 20% and 25% are tested thoroughly to analyze the hardened properties of mixes. By the utilization of these industrial byproducts and waste materials, an elevated level of pollution treatment can be achieved by the construction industry all over the world. This induction of materials in concrete tends to provide efficient and high strength ranging concrete mixes and is eco-friendly, too [3]. Previously much discussion was had on replacement of cement with Fly Ash and Marble Powder [4-9], but Ground Granulated Blast Furnace Slag (GGBFS) [4] was limited in use. Therefore, this research has contributed the comparison of Fly Ash and Marble powder with GGBFS not only at room temperature but also at elevated temperature. Furthermore, most previous studies only major focused on compressive strength [4][5][6...

End-of-Life Materials Used as Supplementary Cementitious Materials in the Concrete Industry

“…The American Society for Testing and Materials (ASTM) produced the initial report on standards for applying FA more than 40 years ago, ASTM C 618 [26]. SF additions to concrete improve several other physical and chemical properties besides the mechanical strength: lower adverse environmental impact, decreased permeability, increased corrosion protection for steel bars and improved resistance against the sulphate and chemicals attacks [68,69].…”

Cement Replacement for Sustainable Concrete: An Overview

“…(Rughooputh and Rana, 2014). For Mansoor et al, (2018), 25% replacement GBBFS is optimal. (Torgal et al, 2011) examined the feasibility of using ceramic wastes in concrete.…”

Rainfall Runoff Analysis and Sustainable Soil Bed Optimization Engineering Process: Application of an Advanced Decision-Making Technique

“…The JMP Pro tool was used to analyze the developed model. The two parameters under focus were the root-mean-squared error (RMSE), which is the difference between the actual and the predicted value, and the difference in the coefficient of determination (R 2 ) [26,27], as given below in Equations (3) and (4), respectively.…”

“…where SSE is the sum of squared errors of prediction, and SS y is the total variation. Usually, the performance of a model is compared using the coefficient of determination (R 2 ), where a better fit is represented by a value closer to 1, and a poor fit has a value which approaches 0 [26,27]. ANNs use a back-propagation neural system which involves a complete learning rate and momentum, including a set of nodes, hidden layers, and an order of hidden layers [26], as well as a practical estimation of parameters such as the learning rate [26].…”