Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks

Siddique, Rafat; Aggarwal, Paratibha; Aggarwal, Yogesh

doi:10.1016/j.advengsoft.2011.05.016

Cited by 236 publications

(92 citation statements)

References 14 publications

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“…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]8,[10][11][12][13]. However, in this research tensile and flexural strength has also been studied.…”

mentioning

confidence: 99%

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

Mansoor¹,

Shah

Khan

et al. 2018

Applied Sciences

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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...

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mentioning

confidence: 99%

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

Mansoor¹,

Shah

Khan

et al. 2018

Applied Sciences

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“…The GRNN was proposed by Specht [36], to perform linear and non-linear regressions. The GRNN structure contains four layers: the input units are in the initial layer, the second layer has the pattern units, the outcomes of these layers are passed on to the summation units in the third layer, and the last layer covers the target units.…”

Section: Generalized Regression Neural Network (Grnn)mentioning

confidence: 99%

“…The ANN is an artificial intelligence based approach generally used for the exact forecast of civil engineering problems [36,37]. ANN is a parallel knowledge processing system containing a set of layered neurons.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Estimation of Punching Shear Capacity of Concrete Slabs Using Data Mining Techniques

Aggarwal

Sihag

et al. 2019

IJE

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A B S T R A C TPunching shear capacity is a key factor for governing the collapsed form of slabs. This fragile failure that occurs at the slab-column connection is called punching shear failure and has been of concern for the engineers. The most common practice in evaluating the punching strength of the concrete slabs is to use the empirical expressions available in different building design codes. T he estimation of punching loads involves experimental setup which is time-consuming, uneconomical and also, more manpower and materials are required. T he present study demonstrates the use of data mining techniques as a substitute of former to predict the punching loads on the variation of various parameters. In this study, various type of data mining techniques including Adaptive Neuro-fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Generalized Neural Network (GRNN) were applied to model and estimate the punching load of reinforced concrete slab-column connections. For the study, a data set consisting of 89 observations from available literature was analysed and randomly selected 62 observations were used for model development whereas the rest 27 were used to test the developed models. While the outcomes of ANN and GRNN model provides suitable estimation performance, the Gaussian membership based ANFIS model performed best in the determination of coefficient of correlation (Cc). Sensitivity study indicates that the parameter effective depth of slab (d) is the most influencing one for the estimation of punching load of reinforced concrete slab-column connections for this data set.

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“…The model developed from literature data was successfully extended to the experimental data [6]. Paresh Chandra Deka and Somanath N Diwate predicted 28-day compressive strength of Ready Mix Concrete by using soft computing techniques Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) modeling.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Compressive Strength of Self compacting Concrete with Flyash and Rice Husk Ash using Adaptive Neuro-fuzzy Inference System

S¹,

Sharma²,

Sood³

et al. 2012

IJACSA

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Abstract-Self-compacting concrete is an innovative concrete that does not require vibration for placing and compaction. It is able to flow under its own weight, completely filling formwork and achieving full compaction even in congested reinforcement without segregation and bleeding. In the present study self compacting concrete mixes were developed using blend of fly ash and rice husk ash. Fresh properties of theses mixes were tested by using standards recommended by EFNARC (European Federation for Specialist Construction Chemicals and Concrete system). Compressive strength at 28 days was obtained for these mixes. This paper presents development of Adaptive Neuro-fuzzy Inference System (ANFIS) model for predicting compressive strength of self compacting concrete using fly ash and rice husk ash. The input parameters used for model are cement, fly ash, rice husk ash and water content. Output parameter is compressive strength at 28 days. The results show that the implemented model is good at predicting compressive strength.

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Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks

Cited by 236 publications

References 14 publications

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

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

Estimation of Punching Shear Capacity of Concrete Slabs Using Data Mining Techniques

Prediction of Compressive Strength of Self compacting Concrete with Flyash and Rice Husk Ash using Adaptive Neuro-fuzzy Inference System

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