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

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.

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

Materials ◽

10.3390/ma14195762 ◽

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.

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

Materials ◽

10.3390/ma15020647 ◽

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.

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Prediction of Compressive Strength of Fly-Ash-Based Concrete Using Ensemble and Non-Ensemble Supervised Machine-Learning Approaches

Applied Sciences ◽

10.3390/app12010361 ◽

2021 ◽

Vol 12 (1) ◽

pp. 361

Author(s):

Yang Song ◽

Jun Zhao ◽

Krzysztof Adam Ostrowski ◽

Muhammad Faisal Javed ◽

Ayaz Ahmad ◽

...

Keyword(s):

Machine Learning ◽

Compressive Strength ◽

Fly Ash ◽

Random Forest ◽

Cross Validation ◽

High Performance Concrete ◽

Supervised Machine Learning ◽

Gradient Boosting ◽

Fine Aggregate ◽

Neuron Network

The utilization of waste material, such as fly ash, in the concrete industry will provide a valuable alternative solution for creating an eco-friendly environment. However, experimental work is time-consuming; employing soft machine learning techniques can accelerate the process of forecasting the strength properties of concrete. Ensemble machine learning modeling using Python Jupyter Notebook was employed in the forecasting of compressive strength (CS) of high-performance concrete. Multilayer perceptron neuron network (MLPNN) and decision tree (DT) were used as individual learning which then ensembled with bagging and boosting to provide strong correlations. Random forest (RF) and gradient boosting regression (GBR) were also used for prediction. A total of 471 data points with input parameters (e.g., cement, fine aggregate, coarse aggregate, superplasticizer, water, days, and fly ash), and an output parameter of compressive strength (CS), were retrieved to train and test the individual learners. Cross-validation with K-fold and statistical error (i.e., MAE, MSE, RMSE, and RMSLE) analysis was applied to check the accuracy of all models. All models showed the best correlation with an ensemble model rather than an individual one. DT with AdaBoost and random forest gave a strong correlation of R2 = 0.89 with fewer errors. Cross-validation results revealed a good response with an error of less than 10 MPa. Thus, ensemble modeling not only trains the data by employing several weak learners but also produces a robust correlation that can then be used to model and predict the mechanical performance of concrete.

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Toward Improving the Prediction Accuracy of Product Recommendation System Using Extreme Gradient Boosting and Encoding Approaches

Symmetry ◽

10.3390/sym12091566 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1566 ◽

Cited By ~ 2

Author(s):

Zeinab Shahbazi ◽

Debapriya Hazra ◽

Sejoon Park ◽

Yung Cheol Byun

Keyword(s):

Machine Learning ◽

South Korea ◽

Collaborative Filtering ◽

Mean Square Error ◽

Prediction Accuracy ◽

Recommendation System ◽

Recommendation Systems ◽

Gradient Boosting ◽

Mean Square ◽

Extreme Gradient Boosting

With the spread of COVID-19, the “untact” culture in South Korea is expanding and customers are increasingly seeking for online services. A recommendation system serves as a decision-making indicator that helps users by suggesting items to be purchased in the future by exploring the symmetry between multiple user activity characteristics. A plethora of approaches are employed by the scientific community to design recommendation systems, including collaborative filtering, stereotyping, and content-based filtering, etc. The current paradigm of recommendation systems favors collaborative filtering due to its significant potential to closely capture the interest of a user as compared to other approaches. The collaborative filtering harnesses features like user-profile details, visited pages, and click information to determine the interest of a user, thereby recommending the items that are related to the user’s interest. The existing collaborative filtering approaches exploit implicit and explicit features and report either good classification or prediction outcome. These systems fail to exhibit good results for both measures at the same time. We believe that avoiding the recommendation of those items that have already been purchased could contribute to overcoming the said issue. In this study, we present a collaborative filtering-based algorithm to tackle big data of user with symmetric purchasing order and repetitive purchased products. The proposed algorithm relies on combining extreme gradient boosting machine learning architecture with word2vec mechanism to explore the purchased products based on the click patterns of users. Our algorithm improves the accuracy of predicting the relevant products to be recommended to the customers that are likely to be bought. The results are evaluated on the dataset that contains click-based features of users from an online shopping mall in Jeju Island, South Korea. We have evaluated Mean Absolute Error, Mean Square Error, and Root Mean Square Error for our proposed methodology and also other machine learning algorithms. Our proposed model generated the least error rate and enhanced the prediction accuracy of the recommendation system compared to other traditional approaches.

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Prediction of Geopolymer Concrete Compressive Strength Using Novel Machine Learning Algorithms

Polymers ◽

10.3390/polym13193389 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3389

Author(s):

Ayaz Ahmad ◽

Waqas Ahmad ◽

Krisada Chaiyasarn ◽

Krzysztof Adam Ostrowski ◽

Fahid Aslam ◽

...

Keyword(s):

Machine Learning ◽

Compressive Strength ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Coefficient Of Determination ◽

Gradient Boosting ◽

Geopolymer Concrete ◽

Ann Model ◽

High Calcium ◽

Boosting Technique

The innovation of geopolymer concrete (GPC) plays a vital role not only in reducing the environmental threat but also as an exceptional material for sustainable development. The application of supervised machine learning (ML) algorithms to forecast the mechanical properties of concrete also has a significant role in developing the innovative environment in the field of civil engineering. This study was based on the use of the artificial neural network (ANN), boosting, and AdaBoost ML approaches, based on the python coding to predict the compressive strength (CS) of high calcium fly-ash-based GPC. The performance comparison of both the employed techniques in terms of prediction reveals that the ensemble ML approaches, AdaBoost, and boosting were more effective than the individual ML technique (ANN). The boosting indicates the highest value of R2 equals 0.96, and AdaBoost gives 0.93, while the ANN model was less accurate, indicating the coefficient of determination value equals 0.87. The lesser values of the errors, MAE, MSE, and RMSE of the boosting technique give 1.69 MPa, 4.16 MPa, and 2.04 MPa, respectively, indicating the high accuracy of the boosting algorithm. However, the statistical check of the errors (MAE, MSE, RMSE) and k-fold cross-validation method confirms the high precision of the boosting technique. In addition, the sensitivity analysis was also introduced to evaluate the contribution level of the input parameters towards the prediction of CS of GPC. The better accuracy can be achieved by incorporating other ensemble ML techniques such as AdaBoost, bagging, and gradient boosting.

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Application of Gradient Boosting Machine Learning Algorithms to Predict Uniaxial Compressive Strength of Soft Sedimentary Rocks at Thar Coalfield

Advances in Civil Engineering ◽

10.1155/2021/2565488 ◽

2021 ◽

Vol 2021 ◽

pp. 1-19

Author(s):

Niaz Muhammad Shahani ◽

Muhammad Kamran ◽

Xigui Zheng ◽

Cancan Liu ◽

Xiaowei Guo

Keyword(s):

Machine Learning ◽

Compressive Strength ◽

Learning Algorithms ◽

Sedimentary Rocks ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Training Phase ◽

Mean Square ◽

Gradient Boosting Machine ◽

Thar Coalfield

The uniaxial compressive strength (UCS) of rock is one of the essential data in engineering planning and design. Correctly testing UCS of rock to ensure its accuracy and authenticity is a prerequisite for assuring the design of any rock engineering project. UCS of rock has a broad range of applications in mining, geotechnical, petroleum, geomechanics, and other fields of engineering. The application of the gradient boosting machine learning algorithms has been rarely used, especially for UCS prediction, and has performed well, based on the relevant literature of the study. In this study, four gradient boosting machine learning algorithms, namely, gradient boosted regression (GBR), Catboost, light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost), were developed to predict the UCS in MPa of soft sedimentary rocks of the Block-IX at Thar Coalfield, Pakistan, using four input variables such as wet density (ρw) in g/cm3; moisture in %; dry density (ρd) in g/cm3; and Brazilian tensile strength (BTS) in MPa. Then, 106-point dataset was allocated identically for each algorithm into 70% for the training phase and 30% for the testing phase. According to the results, the XGBoost algorithm outperformed the GBR, Catboost, and LightGBM with coefficient of correlation (R2) = 0.99, mean absolute error (MAE) = 0.00062, mean square error (MSE) = 0.0000006, and root mean square error (RMSE) = 0.00079 in the training phase and R2 = 0.99, MAE = 0.00054, MSE = 0.0000005, and RMSE = 0.00069 in the testing phase. The sensitivity analysis showed that BTS and ρw are positively correlated, and the moisture and ρd are negatively correlated with the UCS. Therefore, in this study, the XGBoost algorithm was shown to be the most accurate algorithm among all the investigated four algorithms for UCS prediction of soft sedimentary rocks of the Block-IX at Thar Coalfield, Pakistan.

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A Comparative Study of Random Forest and Genetic Engineering Programming for the Prediction of Compressive Strength of High Strength Concrete (HSC)

Applied Sciences ◽

10.3390/app10207330 ◽

2020 ◽

Vol 10 (20) ◽

pp. 7330 ◽

Cited By ~ 1

Author(s):

Furqan Farooq ◽

Muhammad Nasir Amin ◽

Kaffayatullah Khan ◽

Muhammad Rehan Sadiq ◽

Muhammad Faisal Faisal Javed ◽

...

Keyword(s):

Machine Learning ◽

Compressive Strength ◽

Random Forest ◽

Decision Tree ◽

High Strength Concrete ◽

High Strength ◽

Supervised Machine Learning ◽

Fine Aggregate ◽

Strength Concrete ◽

Statistical Measures

Supervised machine learning and its algorithm is an emerging trend for the prediction of mechanical properties of concrete. This study uses an ensemble random forest (RF) and gene expression programming (GEP) algorithm for the compressive strength prediction of high strength concrete. The parameters include cement content, coarse aggregate to fine aggregate ratio, water, and superplasticizer. Moreover, statistical analyses like MAE, RSE, and RRMSE are used to evaluate the performance of models. The RF ensemble model outbursts in performance as it uses a weak base learner decision tree and gives an adamant determination of coefficient R2 = 0.96 with fewer errors. The GEP algorithm depicts a good response in between actual values and prediction values with an empirical relation. An external statistical check is also applied on RF and GEP models to validate the variables with data points. Artificial neural networks (ANNs) and decision tree (DT) are also used on a given data sample and comparison is made with the aforementioned models. Permutation features using python are done on the variables to give an influential parameter. The machine learning algorithm reveals a strong correlation between targets and predicts with less statistical measures showing the accuracy of the entire model.

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Reinforced XGBoost machine learning model for sustainable intelligent agrarian applications

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-200862 ◽

2020 ◽

Vol 39 (5) ◽

pp. 7605-7620 ◽

Cited By ~ 1

Author(s):

Dhivya Elavarasan ◽

Durai Raj Vincent

Keyword(s):

Machine Learning ◽

Decision Tree ◽

Mean Square Error ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

The Other ◽

Gradient Boosting ◽

Model Assessment ◽

Mean Square ◽

Extreme Gradient Boosting

The development in science and technical intelligence has incited to represent an extensive amount ofdata from various fields of agriculture. Therefore an objective rises up for the examination of the available data and integrating with processes like crop enhancement, yield prediction, examination of plant infections etc. Machine learning has up surged with tremendous processing techniques to perceive new contingencies in the multi-disciplinary agrarian advancements. In this pa- per a novel hybrid regression algorithm, reinforced extreme gradient boosting is proposed which displays essentially improved execution over traditional machine learning algorithms like artificial neural networks, deep Q-Network, gradient boosting, ran- dom forest and decision tree. Extreme gradient boosting constructs new models, which are essentially, decision trees learning from the mistakes of their predecessors by optimizing the gradient descent loss function. The proposed hybrid model performs reinforcement learning at every node during the node splitting process of the decision tree construction. This leads to effective utilizationofthesamplesbyselectingtheappropriatesplitattributeforenhancedperformance. Model’sperformanceisevaluated by means of Mean Square Error, Root Mean Square Error, Mean Absolute Error, and Coefficient of Determination. To assure a fair assessment of the results, the model assessment is performed on both training and test dataset. The regression diagnostic plots from residuals and the results obtained evidently delineates the fact that proposed hybrid approach performs better with reduced error measure and improved accuracy of 94.15% over the other machine learning algorithms. Also the performance of probability density function for the proposed model delineates that, it can preserve the actual distributional characteristics of the original crop yield data more approximately when compared to the other experimented machine learning models.

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

Materials ◽

10.3390/ma14092297 ◽

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.

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Experimental Study on Sand and Cement Replacement by Termite Mound Soil

International Research Journal of Multidisciplinary Technovation ◽

10.34256/irjmtcon38 ◽

2019 ◽

pp. 279-287

Author(s):

Harish R ◽

Ramesh S ◽

Tharani A ◽

Mageshkumar P

Keyword(s):

Experimental Study ◽

Experimental Investigation ◽

Compressive Strength ◽

Natural Environment ◽

Fine Aggregate ◽

Test Results ◽

Termite Mound ◽

Cement Concrete ◽

Cement Ratio ◽

Compressive Strength Of Concrete

This paper presents the results of an experimental investigation of the compressive strength of concrete cubes containing termite mound soil. The specimens were cast using M20 grade of concrete. Two mix ratios for replacement of sand and cement are of 1:1.7:2.7 and 1:1.5:2.5 (cement: sand: aggregate) with water- cement ratio of 0.45 and varying combination of termite mound soil in equal amount ranging from 30% and 40% replacing fine aggregate (sand) and cement from 10%,15%,20% were used. A total of 27 cubes, 18 cylinders and 6 beams were cast by replacing fine aggregate, specimens were cured in water for 7,14 and 28 days. The test results showed that the compressive strength of the concrete cubes increases with age and decreases with increasing percentage replacement of cement and increases with increasing the replacement of sand with termite mound soil cured in water. The study concluded that termite mound cement concrete is adequate to use for construction purposes in natural environment.

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