scholarly journals Application of Advanced Machine Learning Approaches to Predict the Compressive Strength of Concrete Containing Supplementary Cementitious Materials

Materials ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 5762
Author(s):  
Waqas Ahmad ◽  
Ayaz Ahmad ◽  
Krzysztof Adam Ostrowski ◽  
Fahid Aslam ◽  
Panuwat Joyklad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Compressive Strength ◽  
Mean Square Error ◽  
Gene Expression Programming ◽  
Cementitious Materials ◽  
Supplementary Cementitious Materials ◽  
Supervised Machine Learning ◽  
Mean Square ◽  
Compressive Strength Of Concrete

The casting and testing specimens for determining the mechanical properties of concrete is a time-consuming activity. This study employed supervised machine learning techniques, bagging, AdaBoost, gene expression programming, and decision tree to estimate the compressive strength of concrete containing supplementary cementitious materials (fly ash and blast furnace slag). The performance of the models was compared and assessed using the coefficient of determination (R2), mean absolute error, mean square error, and root mean square error. The performance of the model was further validated using the k-fold cross-validation approach. Compared to the other employed approaches, the bagging model was more effective in predicting results, with an R2 value of 0.92. A sensitivity analysis was also prepared to determine the level of contribution of each parameter utilized to run the models. The use of machine learning (ML) techniques to predict the mechanical properties of concrete will be beneficial to the field of civil engineering because it will save time, effort, and resources. The proposed techniques are efficient to forecast the strength properties of concrete containing supplementary cementitious materials (SCM) and pave the way towards the intelligent design of concrete elements and structures.

scholarly journals Comparative Study of Supervised Machine Learning Algorithms for Predicting the Compressive Strength of Concrete at High Temperature

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4222
Author(s):  
Ayaz Ahmad ◽  
Krzysztof Adam Ostrowski ◽  
Mariusz Maślak ◽  
Furqan Farooq ◽  
Imran Mehmood ◽  
...  
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
High Temperature ◽  
Mean Square Error ◽  
Supervised Machine Learning ◽  
Gradient Boosting ◽  
Fine Aggregate ◽  
Mean Square ◽  
Coefficient Corrélation ◽  
Compressive Strength Of Concrete

High temperature severely affects the nature of the ingredients used to produce concrete, which in turn reduces the strength properties of the concrete. It is a difficult and time-consuming task to achieve the desired compressive strength of concrete. However, the application of supervised machine learning (ML) approaches makes it possible to initially predict the targeted result with high accuracy. This study presents the use of a decision tree (DT), an artificial neural network (ANN), bagging, and gradient boosting (GB) to forecast the compressive strength of concrete at high temperatures on the basis of 207 data points. Python coding in Anaconda navigator software was used to run the selected models. The software requires information regarding both the input variables and the output parameter. A total of nine input parameters (water, cement, coarse aggregate, fine aggregate, fly ash, superplasticizers, silica fume, nano silica, and temperature) were incorporated as the input, while one variable (compressive strength) was selected as the output. The performance of the employed ML algorithms was evaluated with regards to statistical indicators, including the coefficient correlation (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual models using DT and ANN gave R2 equal to 0.83 and 0.82, respectively, while the use of the ensemble algorithm and gradient boosting gave R2 of 0.90 and 0.88, respectively. This indicates a strong correlation between the actual and predicted outcomes. The k-fold cross-validation, coefficient correlation (R2), and lesser errors (MAE, MSE, and RMSE) showed better performance than the ensemble algorithms. Sensitivity analyses were also conducted in order to check the contribution of each input variable. It has been shown that the use of the ensemble machine learning algorithm would enhance the performance level of the model. 

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 Application of Novel Machine Learning Techniques for Predicting the Surface Chloride Concentration in Concrete Containing Waste Material

Materials ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2297
Author(s):  
Ayaz Ahmad ◽  
Furqan Farooq ◽  
Krzysztof Adam Ostrowski ◽  
Klaudia Śliwa-Wieczorek ◽  
Slawomir Czarnecki
Keyword(s):  
Machine Learning ◽  
Mean Square Error ◽  
Gene Expression Programming ◽  
Chloride Concentration ◽  
Absolute Error ◽  
Chloride Ions ◽  
Machine Learning Techniques ◽  
Extensive Literature ◽  
Mean Square ◽  
Chloride Concentrations

Structures located on the coast are subjected to the long-term influence of chloride ions, which cause the corrosion of steel reinforcements in concrete elements. This corrosion severely affects the performance of the elements and may shorten the lifespan of an entire structure. Even though experimental activities in laboratories might be a solution, they may also be problematic due to time and costs. Thus, the application of individual machine learning (ML) techniques has been investigated to predict surface chloride concentrations (Cc) in marine structures. For this purpose, the values of Cc in tidal, splash, and submerged zones were collected from an extensive literature survey and incorporated into the article. Gene expression programming (GEP), the decision tree (DT), and an artificial neural network (ANN) were used to predict the surface chloride concentrations, and the most accurate algorithm was then selected. The GEP model was the most accurate when compared to ANN and DT, which was confirmed by the high accuracy level of the K-fold cross-validation and linear correlation coefficient (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) parameters. As is shown in the article, the proposed method is an effective and accurate way to predict the surface chloride concentration without the inconveniences of laboratory tests.

scholarly journals Utilization of tailings in cement and concrete: A review

2019 ◽  
Vol 26 (1) ◽  
pp. 449-464 ◽  
Author(s):  
Mifeng Gou ◽  
Longfei Zhou ◽  
Nathalene Wei Ying Then
Keyword(s):  
Compressive Strength ◽  
Cementitious Materials ◽  
Solid Wastes ◽  
Supplementary Cementitious Materials ◽  
Cement Clinker ◽  
Fine Aggregate ◽  
Granulated Blast Furnace Slag ◽  
Fine Aggregates ◽  
Compressive Strength Of Concrete ◽  
Cement And Concrete

AbstractOne of the advantages of cement and the cement concrete industry in sustainability is the ability to utilize large amounts of industrial solid wastes such as fly ash and ground granulated blast furnace slag. Tailings are solid wastes of the ore beneficiation process in the extractive industry and are available in huge amounts in some countries. This paper reviews the potential utilization of tailings as a replacement for fine aggregates, as supplementary cementitious materials (SCMs) in mortar or concrete, and in the production of cement clinker. It was shown in previous research that while tailings had been used as a replacement for both fine aggregate and cement, the workability of mortar or concrete reduced. Also, at a constant water to cement ratio, the compressive strength of concrete increased with the tailings as fine aggregate. However, the compressive strength of concrete decreased as the replacement content of the tailings as SCMs increased, even whentailings were ground into smaller particles. Not much research has been dedicated to the durability of concrete with tailings, but it is beneficial for heavy metals in tailings to stabilize/solidify in concrete. The clinker can be produced by using the tailings, even if the tailings have a low SiO2 content. As a result, the utilization of tailings in cement and concrete will be good for the environment both in the solid waste processing and virgin materials using in the construction industry.

scholarly journals Workability and mechanical properties of ultrafine cement based grout for structural rehabilitation: A parametric study on the partial replacement with SCMs

2018 ◽  
Vol 199 ◽  
pp. 07006
Author(s):  
Md Shamsuddoha ◽  
Götz Hüsken ◽  
Wolfram Schmidt ◽  
Hans-Carsten Kühne ◽  
Matthias Baeßler
Keyword(s):  
Mechanical Properties ◽  
Compressive Strength ◽  
Cementitious Materials ◽  
Strength Properties ◽  
Supplementary Cementitious Materials ◽  
Partial Replacement ◽  
Hardened Cement ◽  
Binder Ratio ◽  
Strengthening Technique ◽  
Ultrafine Cement

Grouting is a universal repair and strengthening technique, which is constantly used for structural remediation of concrete components, trenches, mine subsidence, dam joints, restoration of masonry structures, and geological stabilizations. Having an extremely small particle size of only few microns, ultrafine cements are ideal for grouting applications due to their superior permeability and compressive strength properties of the hardened cement paste compared to that of the less-expensive, but coarser ordinary Portland cements. Supplementary cementitious materials (SCMs) are often used to replace ultrafine cement in order to modify certain properties and to reduce costs. The aim of this experimental study is to investigate the effect of three supplementary materials: microsilica (MS), fly ash (FA), and metakaolin (MK) on the workability, and mechanical properties of an ultrafine cement based grout with a constant water-binder ratio and constant superplasticizer content. Maximum percentages of replacement with ultrafine cement were 6% by volume of cement for MS and 16% for FA, and MK. In general, results suggest that the workability is improved by addition of FA, whereas is reduced, when modified with MS and MK. The compressive strength of grout after cement replacement remains comparable to that of pure cement grout. However, there is a tendency of the MS to positively affect the compressive strength opposite to FA, whereas flexural strength is positively affected by FA. Based on the results, it is evident that grouts with Hägerman cone flow more than 500 mm and compressive strength of more than 90 MPa after 28 days can be produced.

scholarly journals Compressive Strength Prediction via Gene Expression Programming (GEP) and Artificial Neural Network (ANN) for Concrete Containing RCA

Buildings ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 324
Author(s):  
Ayaz Ahmad ◽  
Krisada Chaiyasarn ◽  
Furqan Farooq ◽  
Waqas Ahmad ◽  
Suniti Suparp ◽  
...  
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Mean Square Error ◽  
Gene Expression Programming ◽  
Mix Design ◽  
Mean Square ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

To minimize the environmental risks and for sustainable development, the utilization of recycled aggregate (RA) is gaining popularity all over the world. The use of recycled coarse aggregate (RCA) in concrete is an effective way to minimize environmental pollution. RCA does not gain more attraction because of the availability of adhered mortar on its surface, which poses a harmful effect on the properties of concrete. However, a suitable mix design for RCA enables it to reach the targeted strength and be applicable for a wide range of construction projects. The targeted strength achievement from the proposed mix design at a laboratory is also a time-consuming task, which may cause a delay in the construction work. To overcome this flaw, the application of supervised machine learning (ML) algorithms, gene expression programming (GEP), and artificial neural network (ANN) was employed in this study to predict the compressive strength of RCA-based concrete. The linear coefficient correlation (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were evaluated to investigate the performance of the models. The k-fold cross-validation method was also adopted for the confirmation of the model’s performance. In comparison, the GEP model was more effective in terms of prediction by giving a higher correlation (R2) value of 0.95 as compared to ANN, which gave a value of R2 equal to 0.92. In addition, a sensitivity analysis was conducted to know about the contribution level of each parameter used to run the models. Moreover, the increment in data points and the use of other supervised ML approaches like boosting, gradient boosting, and bagging to forecast the compressive strength, would give a better response.

Influence of Washing of Crushed Aggregate on Mechanical Properties of High-Performance Concrete Containing Supplementary Cementitious Materials

2020 ◽  
Vol 309 ◽  
pp. 26-30 ◽  
Author(s):  
Josef Fládr ◽  
Petr Bílý ◽  
Tomáš Trtík ◽  
Roman Chylík ◽  
Vladimír Hrbek
Keyword(s):  
Mechanical Properties ◽  
Compressive Strength ◽  
High Performance ◽  
High Performance Concrete ◽  
Cementitious Materials ◽  
Positive Influence ◽  
Supplementary Cementitious Materials ◽  
Basic Material ◽  
Depth Of Penetration ◽  
Crushed Aggregate

The paper compares macromechanical and micromechanical properties of high-performance concrete containing supplementary cementitious materials and basalt aggregate. The aggregate was either a common unprocessed crushed basalt aggregate or crushed basalt aggregate the coarse fractions (4/8 and 8/16 mm) of which were washed by water and dried before use. The observed macro-mechanical properties were compressive strength, tensile strength, elastic modulus and depth of penetration of water under pressure; the paper is focused on the first observed property, which is the basic material characteristic. On the microscale, the thickness of the interfacial transition zone (ITZ) was determined by nanoindentation. The positive influence of supplementary cementitious materials and aggregate washing on compressive strength was confirmed and the correlation between macromechanical and micromechanical characteristics was proved.

scholarly journals The Influence and Application of Slag, Fly Ash, and Limestone Flour on Compressive Strength of Concrete Based on the Concrete Compressive Strength Development over Time (CCSDOT) Model

2020 ◽  
Vol 10 (10) ◽  
pp. 3572
Author(s):  
Jian Zheng ◽  
Guohua Liu
Keyword(s):  
Compressive Strength ◽  
Fly Ash ◽  
Cementitious Materials ◽  
Supplementary Cementitious Materials ◽  
Strength Development ◽  
Mix Proportion ◽  
Practical Engineering ◽  
Compressive Strength Development ◽  
Compressive Strength Of Concrete ◽  
Selection Of

Concrete and cement have been widely used in past decades as a result of urbanization. More and more supplementary cementitious materials are adopted in concrete because its production complements environmental conservation. The influence of slag, fly ash, limestone, etc., on compressive strength of concrete is of interest to engineers worldwide. Many previous studies were specific to certain engineering or certain experiments that could not reveal the nature of the influence of the three supplementary cementitious materials on concrete’s compressive strength. The research concerning the influence of two or more kinds of supplementary cementitious materials on concrete’s compressive strength is still unclear. Moreover, there is a lack of clarity on the optimum proportion of one or more certain cementitious materials in practical engineering or experiments. To overcome these problems, this study adopts the concrete compressive strength development over time (CCSDOT) model, which generates an explicit formula to conduct quantitative research based on extensive data. The CCSDOT model performs well in fitting the compressive strength development of concrete containing cement, slag, fly ash, and limestone flour. The results reveal the nature of the influence of the three supplementary cementitious materials on concrete’s compressive strength through the parameter analysis in the model. Two application cases are analyzed concerning the selection of the three supplementary cementitious materials and design of concrete mix proportion for practical engineering. It is concluded that the CCSDOT model and the method in this study can possibly provide guidance on both the selection of supplementary cementitious materials and the design of optimal concrete mix proportion for practical engineering. Therefore, the study is highly essential and useful.

scholarly journals Compressive Strength of Concrete using Fly Ash and Rice Husk Ash: A Review

2020 ◽  
Vol 6 (7) ◽  
pp. 1400-1410
Author(s):  
Joel Sam
Keyword(s):  
Compressive Strength ◽  
Fly Ash ◽  
Rice Husk ◽  
Rice Husk Ash ◽  
Concrete Strength ◽  
Cementitious Materials ◽  
Supplementary Cementitious Materials ◽  
Partial Replacement ◽  
Compressive Strength Of Concrete ◽  
Minor Compounds

Decreasing our over-reliance on cement as an ingredient in the making of concrete due to its contribution to the CO2 emissions has led to numerous researches been conducted to find suitable replacement for cement in concrete mixes.  Materials like fly ash, ground granulated blast furnace slag, silica fume, rice husk ash and metakaolin among others have been identified as materials that can at the very least be used as a replacement for cement in concrete mix. These materials are referred to as supplementary cementitious materials (SCMs). This paper reviewed the work that has been done on the use of fly ash and rice husk ash as partial replacements for concrete, its chemical composition and its effect on the compressive strength of concrete. Charts, tables and figures were employed as tools to study the various chemical compounds of fly ash and rice husk ash. It was seen that depending on how the coal or rice husk was initially processed the percentage of some of the minor compounds like Sodium oxide (Na2O), Titanium oxide (TiO2) and Phosphorus pentoxide (P2O5) were sometimes very low or not recorded as part of the final product.  The data on the compressive strength of concrete after fly ash and rice husk ash had been added in percentage increments of 0%, 10%, 20%, 30%, 40%, 50% and 0%, 5%, 7.5%, 10%, 12.5%, 15% respectively analysed over a minimum period of 7 days and a maximum period of 28 days found out that the optimal percentage partial replacement of fly ash and rice husk ash for a strong compressive concrete strength is 30% of fly ash and 7.5% of rice husk ash.

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