This paper aims to show the possible applicability of artificial neural networks (ANNs) to predict the compressive strength of recycled aggregate concrete. ANN model is constructed, trained and tested using 146 available sets of data obtained from 16 different published literature sources.
A new artificial reef concrete (NARC) with sulphoaluminate cement, marine sand and sea water et al. was proposed. The effect of cement type, sand type and water type on the workability (namely slump, slump loss cohesiveness, water retention), mechanical properties namely compressive strength, splitting tensile strength and …
Request PDF | On Jan 30, 2020, Emadaldin Mohammadi Golafshani and others published Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with ...
The paper evaluated the possibility of using artificial neural network models for predicting the compressive strength (Fc) of concretes with the addition of recycled concrete aggregate (RCA).
This study aimed to use an artificial neural network to predict the compressive strength of waste-based concretes. The methodology, architecture, and learning methods were explained, based on feedforward and backpropagation techniques.
This research was to study the chloride penetration resistance of normal (W/B of 0.80, 0.62, 0.48) and high (W/B of 0.41, 0.35, 0.30) strength concretes containing ground pozzolans such as fly ash, bottom ash and rice husk ash using the rapid chloride penetration test and the immersion test methods. Furthermore, on the basis of this …
Normal Concrete (NC) usually contains Ordinary Portland Cement (OPC), Coarse Aggregate (CA), Sand (S), and water. Recently, a lot of attentions have been given to the use of alternative materials in the concrete mixtures to …
The use of supplementary cementitious materials ( SCMs) in the binder system of concrete mixtures can provide several environmental and technical benefits. Several previous studies have focused on evaluating the compressive strength ( CS) of concretes containing SCMs using machine learning ( ML) techniques.
Some studies recommended the ratio of artificial sand/natural sand should be less than 0.5 to achieve an expected strength of concretes (Shanmugapriya and Uma 2012, Mundra et al. 2016).
Semantic Scholar extracted view of "Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves" by A. Behnood et al.
Finally, it is concluded that the use of WFS may be feasible in conventional concrete, as the results of the tests carried out showed that concretes with WFS have similar properties to concretes obtained with natural sand.
In the present study, we developed M80 grade of high strength concrete using fully replacement of artificial sand as fine aggregate and fly ash, silica fume as supplementary cementaneous material with incorporation of super plasticizer by Indian standard method.
Lightweight aggregate (LWA), a kind of aggregates used in the production of concretes or cementitious composites, is mainly coming either from natural, synthetic and recycled.
The use of machine learning methods for predicting the HPC strength parameter has grown rapidly in recent years. I-Cheng et al. (Yeh 1998) investigate the compressive strength prediction using an artificial neural network (ANN) and found that the ANN result ( R2 = 0.928) is more accurate than other machine learning (ML) models …
Semantic Scholar extracted view of "Effect of limestone fines content in manufactured sand on durability of low- and high-strength concretes" by B. Li et al.
In this study, it was assumed that the mechanical properties of concretes containing WFS as partial or full replacement for natural sand is a function of mixture proportions and the age of concrete.
Compressive strength of alkali-activated slag (AAS) concrete is influenced by multi-factors in a nonlinear way. Both artificial neural network (ANN) and alternating conditional expectation (ACE) models of 3-day (3 d) and 28-day (28 d) compressive strength of AAS were established in this study by using the data reported in related literature, where …
Modelling the effects of petroleum product contaminated sand on the compressive strength of concretes using fuzzy logic and artificial neural networks: A case study of diesel:
The carbonation depth test results of self-compacting concretes for marine artificial reef after C14d and HC28d curing regimes are shown in Fig. 10. With the same curing regime, the carbonation depth of the self-compacting concretes increased as the water-cement ratio increased.
Modelling the effects of petroleum product contaminated sand on the compressive strength of concretes using fuzzy logic and artificial neural networks: A case study of diesel
By using this robot sand with sand in various ratios, you can get the concrete mixture you need. The replacement of natural fine aggregate is done with 20%, 40% and 60% artificial fine aggregate, and the compressive strength of the concrete cube is also known.
An artificial neural network and cuckoo search method has been developed in this study to assess the strength characteristics of recycled aggregate concrete based on vital predetermined input parameters.
The prediction accuracy of artificial neural network was enhanced by replacing the conventional trainer with metaheuristic science. Biogeography-based optimization and multi-tracker optimization algorithms could successfully tune the neural interactions established for predicting the UCS of manufactured-sand concrete.
Hence, effort has been made in this study to propose a model to estimate the compressive strength of green concretes containing fine copper slag aggregates to reduce the sample making/testing cost/time using the artificial neural network (ANN) and response surface method (RSM).
Compressive strength of alkali-activated slag (AAS) concrete is influenced by multi-factors in a nonlinear way. Both artificial neural network (ANN) and alternating conditional expectation (ACE) models of 3-day (3 d) and 28-day (28 d) compressive strength of AAS were established in this study by using the data reported in related …
Towards sustainable use of foundry by-products: Evaluating the compressive strength of green concrete containing waste foundry sand using hybrid biogeography-based optimization with artificial neural networks
As a result of artificial intelligence, computers are able to make intelligent and human-like decisions regarding a wide range of issues. The purpose of artificial intelligence is to interpret data so that a machine can learn them and use the acquired knowledge to perform tasks that require human intelligence [18].
According to these input, in the artificial neural networks and fuzzy logic models are predicted the compressive and splitting tensile strengths values from mechanical properties of recycled aggregate concretes containing silica fume.
This paper presents the effects of use of M-sand with different percentage of fines in concrete for a particular mix design provided. Study reveals that workability of concrete increases with increase of fines content in M-sand and lesser dosage of super plasticizer is sufficient at higher percentage of fines.
Prediction of Compressive Strength in Plain and Blended Cement Concretes Using a Hybrid Artificial Intelligence Model
Manufactured sand (MS) has been increasingly used as fine aggregate for concrete. This paper proposes a prediction of the compressive strength of concrete with manufactured sand (MS-concrete) based on an ensemble classification and regression tree (En_CART) method.
A network of the feedforward-type artificial neural networks (ANNs) was used to predict the compressive strength of concrete made from crude oil contaminated soil samples at different degrees of contamination and has shown that the use of neural networks is effective.
Both artificial neural network (ANN) and alternating conditional expectation (ACE) models of 3-day (3 d) and 28-day (28 d) compressive strength of AAS were established in this study by using the ...
In the present paper, the models in artificial neural networks (ANN) for predicting compressive strength of concretes containing metakaolin and silica fume have been developed at the age of 1, 3, 7, 28, 56, 90 and 180days.
— This paper give the experimental study of optimum replacement of natural sand with artificial sand in concrete Concrete may be a combine proportion of cement, sand and mixture. The strength of mixture can have an effect on on the strength of
In this study, using these beneficial properties of artificial neural networks in order to predict the 1, 3, 7, 14, 28, 56, 90 and 180 days compressive strength values of concretes containing metakaolin and silica fume without attempting any experiments were developed two different multilayer artificial neural network architectures namely …
The artificial sand have to satisfy the technical requisites such as workability, strength and durability of concrete and hence it has become necessary to study these properties in order to check the suitability and appropriate replacement level of artificial sand in comparison with the natural sand for producing concretes in an …
One of the solutions is to use crushed sand to replace natural sand as aggregate in concrete, especially the study of using crushed sand to make high-strength self-compacting concrete.
This investigation focuses the dependence of abrasive and compressive properties of concretes, which is applied for road pavement, upon different contents of fine artificial sand.
Compressive Strength Prediction of Alkali-Activated Slag Concretes by Using Artificial Neural Network (ANN) and Alternating Conditional Expectation (ACE)
