scholarly journals Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Machine Learning Techniques

Polymers ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 30
Author(s):  
Afnan Nafees ◽  
Muhammad Nasir Amin ◽  
Kaffayatullah Khan ◽  
Kashif Nazir ◽  
Mujahid Ali ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Compressive Strength ◽  
Silica Fume ◽  
Sensitivity Analyses ◽  
Partial Substitution ◽  
Support Vector ◽  
Fine Aggregate ◽  
Statistical Parameters ◽  
Green Concrete

Silica fume (SF) is a frequently used mineral admixture in producing sustainable concrete in the construction sector. Incorporating SF as a partial substitution of cement in concrete has obvious advantages, including reduced CO2 emission, cost-effective concrete, enhanced durability, and mechanical properties. Due to ever-increasing environmental concerns, the development of predictive machine learning (ML) models requires time. Therefore, the present study focuses on developing modeling techniques in predicting the compressive strength of silica fume concrete. The employed techniques include decision tree (DT) and support vector machine (SVM). An extensive and reliable database of 283 compressive strengths was established from the available literature information. The six most influential factors, i.e., cement, fine aggregate, coarse aggregate, water, superplasticizer, and silica fume, were considered as significant input parameters. The evaluation of models was performed by different statistical parameters, such as mean absolute error (MAE), root mean squared error (RMSE), root mean squared log error (RMSLE), and coefficient of determination (R2). Individual and ensemble models of DT and SVM showed satisfactory results with high prediction accuracy. Statistical analyses indicated that DT models bested SVM for predicting compressive strength. Ensemble modeling showed an enhancement of 11 percent and 1.5 percent for DT and SVM compressive strength models, respectively, as depicted by statistical parameters. Moreover, sensitivity analyses showed that cement and water are the governing parameters in developing compressive strength. A cross-validation technique was used to avoid overfitting issues and confirm the generalized modeling output. ML algorithms are used to predict SFC compressive strength to promote the use of green concrete.

scholarly journals Predictive Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Artificial Intelligence Approaches: MLPNN, ANFIS, and GEP

Materials ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 7531
Author(s):  
Afnan Nafees ◽  
Muhammad Faisal Javed ◽  
Sherbaz Khan ◽  
Kashif Nazir ◽  
Furqan Farooq ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tensile Strength ◽  
Compressive Strength ◽  
Silica Fume ◽  
Splitting Tensile Strength ◽  
Partial Replacement ◽  
Fine Aggregate ◽  
Learning Models ◽  
Green Concrete ◽  
Machine Learning Models

Silica fume (SF) is a mineral additive that is widely used in the construction industry when producing sustainable concrete. The integration of SF in concrete as a partial replacement for cement has several evident benefits, including reduced CO2 emissions, cost-effective concrete, increased durability, and mechanical qualities. As environmental issues continue to grow, the development of predictive machine learning models is critical. Thus, this study aims to create modelling tools for estimating the compressive and cracking tensile strengths of silica fume concrete. Multilayer perceptron neural networks (MLPNN), adaptive neural fuzzy detection systems (ANFIS), and genetic programming are all used (GEP). From accessible literature data, a broad and accurate database of 283 compressive strengths and 149 split tensile strengths was created. The six most significant input parameters were cement, fine aggregate, coarse aggregate, water, superplasticizer, and silica fume. Different statistical measures were used to evaluate models, including mean absolute error, root mean square error, root mean squared log error and the coefficient of determination. Both machine learning models, MLPNN and ANFIS, produced acceptable results with high prediction accuracy. Statistical analysis revealed that the ANFIS model outperformed the MLPNN model in terms of compressive and tensile strength prediction. The GEP models outperformed all other models. The predicted values for compressive strength and splitting tensile strength for GEP models were consistent with experimental values, with an R2 value of 0.97 for compressive strength and 0.93 for splitting tensile strength. Furthermore, sensitivity tests revealed that cement and water are the determining parameters in the growth of compressive strength but have the least effect on splitting tensile strength. Cross-validation was used to avoid overfitting and to confirm the output of the generalized modelling technique. GEP develops an empirical expression for each outcome to forecast future databases’ features to promote the usage of green concrete.

scholarly journals Prediction Models for the Mechanical Properties of Self-Compacting Concrete with Recycled Rubber and Silica Fume

Materials ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1821 ◽  
Author(s):  
Robert Bušić ◽  
Mirta Benšić ◽  
Ivana Miličević ◽  
Kristina Strukar
Keyword(s):  
Mechanical Properties ◽  
Compressive Strength ◽  
Silica Fume ◽  
Prediction Models ◽  
Crumb Rubber ◽  
Fine Aggregate ◽  
Rubberized Concrete ◽  
Self Compacting Concrete ◽  
Recycled Rubber ◽  
European Standards

The paper aims to investigate the influence of waste tire rubber and silica fume on the fresh and hardened properties of self-compacting concrete (SCC) and to design multivariate regression models for the prediction of the mechanical properties of self-compacting rubberized concrete (SCRC). For this purpose, 21 concrete mixtures were designed. Crumb rubber derived from end-of-life tires (grain size 0.5–3.5 mm) was replaced fine aggregate by 0%, 5%, 10%, 15%, 20%, 25%, and 30% of total aggregate volume. Silica fume was replaced cement by 0%, 5%, and 10% of the total cement mass. The optimal replacement level of both materials was investigated in relation to the values of the fresh properties and mechanical properties of self-compacting concrete. Tests on fresh and hardened self-compacting concrete were performed according to the relevant European standards. Furthermore, models for predicting the values of the compressive strength, modulus of elasticity, and flexural strength of SCRC were designed and verified with the experimental results of 12 other studies. According to the obtained results, mixtures with up to 15% of recycled rubber and 5% of silica fume, with 28 days compressive strength above 30 MPa, were found to be optimal mixtures for the potential future investigation of reinforced self-compacting rubberized concrete structural elements.

scholarly journals Mechanical Properties of Concrete Modified with Silica Fume and Integral Waterproof and Comparison with Waste Glass Aggregate Concrete

2022 ◽  
Vol 961 (1) ◽  
pp. 012082
Author(s):  
Taghreed Abd-Almahdee Musa ◽  
Hiba Ali Abbas ◽  
Ayam Jabbar Jihad
Keyword(s):  
Mechanical Properties ◽  
Compressive Strength ◽  
Flexural Strength ◽  
Silica Fume ◽  
Strength Test ◽  
Waste Glass ◽  
Fine Aggregate ◽  
Glass Aggregate ◽  
Water Proof ◽  
Strength And Durability

Abstract This study includes the effect of using different dosages of integral waterproof Admixture and silica fume on some mechanical properties of concrete. Concrete improved by using different ratios of integral water proof admixture(IWP admixture) to increase strength and durability, this admixture used as percentages from cement weight in each mix ranged from 0.0% to 2% ( 0.0, 1.0%, 1.2%,1.4%,1.6%,1.8%, and 2%), compressive strength test done for cubes with (10*10*10) cm for each mix. The flexural strength test was done by (10*10*40) cm beams and tested after 28 days of curing. comparison study was made between silica fume mixes properties and mixes without silica fume. Adding IWP admixture leads to increase mechanical properties of ordinary concrete, the reference mix shows compressive strength equal to 26.38 MPa, while mixes with 2% IWP gives 38.8 MPa in this study. The study also includes the effect of using 2 main dosages of silica fume to the mixes that contain IWP, the new concrete with two admixtures show better values of compressive, tensile and flexural strength comparing with mixes with only IWP, the compressive strength increased from 38.8 MPa for ordinary IWP mixes to 52.3 MPa for 10% silica fume concrete mixes, and also the flexural strength increased from 4.8 MPa for mixes with only IWP to 7.3 MPa for mixes modified with 10 % silica fume. Study include also using waste glass as fine aggregate in mixes contain IWP and 10% silica fume and that show more increment in mechanical properties also.

scholarly journals Behaviour of Light Weight Brick with the Influence of EPS Beads and Silica Fume

2020 ◽  
Vol 9 (3) ◽  
pp. 862-867
Keyword(s):  
Thermal Conductivity ◽  
Compressive Strength ◽  
Water Absorption ◽  
Silica Fume ◽  
Expanded Polystyrene ◽  
Partial Substitution ◽  
Fine Aggregate ◽  
Light Weight ◽  
Literature Study ◽  
Thermal Insulating

The principal reason behind this experimental investigation carried out here is to diminish the dead load of structures with the potential use of light weight bricks. EPS beads and silica fume are light in nature. The investigation work enhanced, with numerous literature study to find out the utilization of Expanded polystyrene(EPS) beads and silica fume in light weight brick can be used in military bases in cold regions due to its low thermal insulating quality. The main objective of this research is to prepare a light weight brick by partial substitution of Cement with silica fume and the replacement of fine aggregate with EPS beads. A total of 70 bricks containing two different sizes of EPS beads say Type A and Type B with different proportions (0%, 7%, 14%, 21%) of each Type were casted in order to check the mechanical properties such as compressive strength, water absorption, efflorescence, workability, and thermal conductivity of the brick. The compressive strength test was carried out at 7, 14 and 28 days of curing. As the percentage of EPS beads in the brick increased the strength of brick decreased while with the increase of EPS beads in the brick the water absorption as well as the thermal conductivity of brick decreased. There were slight presence of Efflorescence in some of the bricks while in most of the brick there were no efflorescence found.

scholarly journals Experimental Investigation on Rcc by Using Multiple Admixtures

2018 ◽  
Vol 7 (3.3) ◽  
pp. 14 ◽  
Author(s):  
Professor P.Venkatreddy ◽  
A Siva Krishna ◽  
G SwamyYadav
Keyword(s):  
Mechanical Properties ◽  
Experimental Investigation ◽  
Compressive Strength ◽  
Silica Fume ◽  
Research Work ◽  
Copper Slag ◽  
Partial Replacement ◽  
Fine Aggregate ◽  
Flexure Strength ◽  
Concrete Properties

In this article, the effect of replacing cement with silica fume and fine aggregate with copper slag has been investigated. For this research work, concrete of M40 grade is prepared and evaluated for fresh and harden concrete properties such as compressive strength, tensile strength and flexural strength. Further, the cement is replaced with silica fume at 0, 2, 4, 6, 8 and 10 % and fine aggregate replaced with copper slag at 0, 10, 20, 30, 40 and 50 %. Compressive strength, strength and Flexure strength have been tested. It is observed from the results that the use of silica fume and copper slag as partial replacement material improves mechanical properties of the concrete. Concrete with 40 % copper slag and 8 % silica fume shows better performance among all the mixes.  

scholarly journals The Influence of the Use of Cement Brands on the Physical and Mechanical Properties of Reactive Powder Concrete (RPC)

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
E. Sutandar ◽  
Setya Budi ◽  
F. Juniardi
Keyword(s):  
Mechanical Properties ◽  
Compressive Strength ◽  
Silica Fume ◽  
Binding Capacity ◽  
Physical And Mechanical Properties ◽  
Reactive Powder Concrete ◽  
Powder Materials ◽  
Fine Aggregate ◽  
Reactive Material ◽  
Reactive Powder

The most popular building material today is concrete. Concrete is used for buildings, industrial structures, bridges, railroad sleepers, roads, dams and more. Along with the development of concrete technology is increasingly developing and innovative so as to achieve better characteristics, cheaper prices and environmentally friendly. In the early days of the development of concrete it is explained to us about the origin from ancient times to civilization to the present century of its development. In current developments, concrete already has a very high compressive strength up to 200-800 MPa, this is also called Ultra Hight Concrete (UHC). It is developed by the Reactive Powder concrete (RPC) method. Where in the research conducted, the manufacture of reactive material, namely Reactive Powder concrete (RPC) was given with a compressive strength of up to 52 MPa. The components of the RPC mixture consist of cement, fine aggregate, silica fume, superplasticizer and water. To produce a compressive strength of 52 MPa, the composition of the mixture used previous research. Detailed RPC concrete mix proportions are given in this study. Preparation and testing of materials are made in the laboratory of the Faculty of Civil Engineering in Untan. The results that will be obtained are the physical and mechanical properties of RPC Concrete. As previously explained, the manufacture of RPC concrete requires basic ingredients that include cement, fine aggregate, silica fume, superplasticizer and water mixed together. Of course, with basic ingredients with good and correct composition, the physical and mechanical properties of RPC concrete will reach the planned quality. Cement sold in the market today has PPC and PCC types. Therefore, the cement which is a binder of powder materials into a solid one plays a very important role in the formation of the best physical and mechanical properties of RPC concrete. With cement as a binding material, it is certain that the strength of RPC concrete is determined by the binding capacity of the type of cement to be used, thus RPC concrete will be obtained according to the planned quality of the plan. From the cement brands on the Indonesian market consisting of PPC and PCC cement types, it can be concluded that one of the cements using the PCC type cement brand has better physical and mechanical properties than the PPC type cement brand. And all brands of cement with PCC type produce compressive strength > 52 MPa. So, in the manufacture of RPC concrete it is recommended to use a cement brand with PCC type.

scholarly journals Development of Machine Learning Models to Evaluate the Toughness of OPH Alloys

Materials ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6713
Author(s):  
Omid Khalaj ◽  
Moslem Ghobadi ◽  
Ehsan Saebnoori ◽  
Alireza Zarezadeh ◽  
Mohammadreza Shishesaz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Mechanical Alloying ◽  
Fuzzy Inference ◽  
Oxide Dispersion Strengthened ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Anfis Model ◽  
Inference Systems ◽  
The Impact

Oxide Precipitation-Hardened (OPH) alloys are a new generation of Oxide Dispersion-Strengthened (ODS) alloys recently developed by the authors. The mechanical properties of this group of alloys are significantly influenced by the chemical composition and appropriate heat treatment (HT). The main steps in producing OPH alloys consist of mechanical alloying (MA) and consolidation, followed by hot rolling. Toughness was obtained from standard tensile test results for different variants of OPH alloy to understand their mechanical properties. Three machine learning techniques were developed using experimental data to simulate different outcomes. The effectivity of the impact of each parameter on the toughness of OPH alloys is discussed. By using the experimental results performed by the authors, the composition of OPH alloys (Al, Mo, Fe, Cr, Ta, Y, and O), HT conditions, and mechanical alloying (MA) were used to train the models as inputs and toughness was set as the output. The results demonstrated that all three models are suitable for predicting the toughness of OPH alloys, and the models fulfilled all the desired requirements. However, several criteria validated the fact that the adaptive neuro-fuzzy inference systems (ANFIS) model results in better conditions and has a better ability to simulate. The mean square error (MSE) for artificial neural networks (ANN), ANFIS, and support vector regression (SVR) models was 459.22, 0.0418, and 651.68 respectively. After performing the sensitivity analysis (SA) an optimized ANFIS model was achieved with a MSE value of 0.003 and demonstrated that HT temperature is the most significant of these parameters, and this acts as a critical rule in training the data sets.

scholarly journals Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms

Materials ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 647
Author(s):  
Meijun Shang ◽  
Hejun Li ◽  
Ayaz Ahmad ◽  
Waqas Ahmad ◽  
Krzysztof Adam Ostrowski ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Mean Square Error ◽  
Coarse Aggregate ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Environmental Damage ◽  
Fine Aggregate ◽  
Mean Square ◽  
The Impact

Environment-friendly concrete is gaining popularity these days because it consumes less energy and causes less damage to the environment. Rapid increases in the population and demand for construction throughout the world lead to a significant deterioration or reduction in natural resources. Meanwhile, construction waste continues to grow at a high rate as older buildings are destroyed and demolished. As a result, the use of recycled materials may contribute to improving the quality of life and preventing environmental damage. Additionally, the application of recycled coarse aggregate (RCA) in concrete is essential for minimizing environmental issues. The compressive strength (CS) and splitting tensile strength (STS) of concrete containing RCA are predicted in this article using decision tree (DT) and AdaBoost machine learning (ML) techniques. A total of 344 data points with nine input variables (water, cement, fine aggregate, natural coarse aggregate, RCA, superplasticizers, water absorption of RCA and maximum size of RCA, density of RCA) were used to run the models. The data was validated using k-fold cross-validation and the coefficient correlation coefficient (R2), mean square error (MSE), mean absolute error (MAE), and root mean square error values (RMSE). However, the model’s performance was assessed using statistical checks. Additionally, sensitivity analysis was used to determine the impact of each variable on the forecasting of mechanical properties.

scholarly journals Strength Characteristics of High Strength Concrete (HSC) with and without Coarse Aggregate

2019 ◽  
Vol 9 (2) ◽  
pp. 4701-4704
Keyword(s):  
Mechanical Properties ◽  
Compressive Strength ◽  
Fly Ash ◽  
Flexural Strength ◽  
Silica Fume ◽  
High Strength Concrete ◽  
Mechanical Characteristics ◽  
Coarse Aggregate ◽  
High Strength ◽  
Set Up

This paper aimed to investigate the mechanical characteristics of HSC of M60 concrete adding 25% of fly ash to cement and sand and percentage variations of silica fumes 0%,5% and 10% to cement with varying sizes of 10mm,6mm,2mm and powder of granite aggregate with w/c of 0.32. Specimens are tested for compressive strength using 10cm X 10cmX10cm cubes for 7,14,28 days flexural strength was determined by using 10cmX10cmX50cm beam specimens at 28 days and 15cm diameter and 30cm height cylinder specimens at 28 days using super plasticizers of conplast 430 as a water reducing agent. In this paper the experimental set up is made to study the mechanical properties of HSC with and without coarse aggregate with varying sizes as 10mm, 6mm, 2mm and powder. Similarly, the effect of silica fume on HSC by varying its percentages as 0%, 5% and 10% in the mix studied. For all mixes 25% extra fly ash has been added for cement and sand.

Investigation of the mechanical properties of concrete containing recycled aggregate and scrap crumb rubber and polypropylene fibers

2020 ◽  
pp. 147776062097750
Author(s):  
Moein Khoshroo ◽  
Ali Akbar Shirzadi Javid ◽  
Nima Rajabi Bakhshandeh ◽  
Mohamad Shalchiyan
Keyword(s):  
Mechanical Properties ◽  
Tensile Strength ◽  
Compressive Strength ◽  
Polypropylene Fiber ◽  
Crumb Rubber ◽  
Recycled Aggregate ◽  
Recycled Aggregates ◽  
Fine Aggregate ◽  
Mixture Ratio ◽  
Coarse Aggregates

In this study, the effect of using crumb rubber and recycled aggregates on the mechanical properties of concrete has been evaluated as areplacement of fine and coarse aggregates In order to add the admixtures and evaluate their combined effect, 20 different types of concrete mixture ratio were prepared. The results indicated that in those samples containing crumb rubber and recycled aggregates the compressive strength is reduced and adding fiber up to 0.1%. to these concrete samples can improve the compressive strength Also, the tensile strength of the samples mixed with crumb rubber and recycled aggregates were decreased, and with the addition of propylene fiber up to 0.4%. the tensile strength slightly increased Moreover by adding the crumb rubber to the samples the elasticity modulus was reduced but by adding fiber to samples about 0.1% and 0.2.% the modulus of elasticity of concrete in all samples were increased. According to the results, it can be said that using the combination of 5% of crumb rubber as a replacement of fine aggregate, and the combination of 35% of recycled aggregates as a replacement of coarse aggregate, and also by adding 0.1% polypropylene fiber in volumetric percentage of concrete along with adding 7% of micro silica as a replacement of cement led to the best effect on the mechanical properties of concrete.

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