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

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.

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

2020 ◽  
Vol 10 (20) ◽  
pp. 7330 ◽  
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.

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 Effect of Mineral Aggregates and Chemical Admixtures as Internal Curing Agents on the Mechanical Properties and Durability of High-Performance Concrete

Materials ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2090 ◽  
Author(s):  
Francisco Javier Vázquez-Rodríguez ◽  
Nora Elizondo-Villareal ◽  
Luz Hypatia Verástegui ◽  
Ana Maria Arato Tovar ◽  
Jesus Fernando López-Perales ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Fly Ash ◽  
High Performance ◽  
High Performance Concrete ◽  
Chloride Ions ◽  
Internal Curing ◽  
Fine Aggregate ◽  
Pumice Stone ◽  
Chemical Admixtures ◽  
Mineral Aggregates

In the present work, the effect of mineral aggregates (pumice stone and expanded clay aggregates) and chemical admixtures (superplasticizers and shrinkage reducing additives) as an alternative internal curing technique was investigated, to improve the properties of high-performance concrete. In the fresh and hardened state, concretes with partial replacements of Portland cement (CPC30R and OPC40C) by pulverized fly ash in combination with the addition of mineral aggregates and chemical admixtures were studied. The physical, mechanical, and durability properties in terms of slump, density, porosity, compressive strength, and permeability to chloride ions were respectively determined. The microstructural analysis was carried out by scanning electronic microscopy. The results highlight the effect of the addition of expanded clay aggregate on the internal curing of the concrete, which allowed developing the maximum compressive strength at 28 days (61 MPa). Meanwhile, the replacement of fine aggregate by 20% of pumice stone allowed developing the maximum compressive strength (52 MPa) in an OPC-based concrete at 180 days. The effectiveness of internal curing to develop higher strength is attributed to control in the porosity and a high water release at a later age. Finally, the lowest permeability value at 90 days (945 C) was found by the substitutions of fine aggregate by 20% of pumice stone saturated with shrinkage reducing admixture into pores and OPC40C by 15% of pulverized fly ash. It might be due to impeded diffusion of chloride ions into cement paste in the vicinity of pulverized fly ash, where the pozzolanic reaction has occurred. The proposed internal curing technology can be considered a real alternative to achieve the expected performance of a high-performance concrete since a concrete with a compressive strength range from 45 to 67 MPa, density range from 2130 to 2310 kg/m3, and exceptional durability (< 2000 C) was effectively developed.

scholarly journals Classification of Agriculture Farm Machinery Using Machine Learning and Internet of Things

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 403
Author(s):  
Muhammad Waleed ◽  
Tai-Won Um ◽  
Tariq Kamal ◽  
Syed Muhammad Usman
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Support Vector ◽  
Farm Machinery ◽  
Learning Techniques

In this paper, we apply the multi-class supervised machine learning techniques for classifying the agriculture farm machinery. The classification of farm machinery is important when performing the automatic authentication of field activity in a remote setup. In the absence of a sound machine recognition system, there is every possibility of a fraudulent activity taking place. To address this need, we classify the machinery using five machine learning techniques—K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Gradient Boosting (GB). For training of the model, we use the vibration and tilt of machinery. The vibration and tilt of machinery are recorded using the accelerometer and gyroscope sensors, respectively. The machinery included the leveler, rotavator and cultivator. The preliminary analysis on the collected data revealed that the farm machinery (when in operation) showed big variations in vibration and tilt, but observed similar means. Additionally, the accuracies of vibration-based and tilt-based classifications of farm machinery show good accuracy when used alone (with vibration showing slightly better numbers than the tilt). However, the accuracies improve further when both (the tilt and vibration) are used together. Furthermore, all five machine learning algorithms used for classification have an accuracy of more than 82%, but random forest was the best performing. The gradient boosting and random forest show slight over-fitting (about 9%), but both algorithms produce high testing accuracy. In terms of execution time, the decision tree takes the least time to train, while the gradient boosting takes the most time.

scholarly journals Prediction of Geopolymer Concrete Compressive Strength Using Novel Machine Learning Algorithms

Polymers ◽  
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.

scholarly journals Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest

2021 ◽  
Vol 2021 ◽  
pp. 1-17 ◽  
Author(s):  
Mohsin Ali Khan ◽  
Shazim Ali Memon ◽  
Furqan Farooq ◽  
Muhammad Faisal Javed ◽  
Fahid Aslam ◽  
...  
Keyword(s):  
Gene Expression ◽  
Compressive Strength ◽  
Fly Ash ◽  
Random Forest ◽  
Gene Expression Programming ◽  
Curing Temperature ◽  
Fine Aggregate ◽  
Geopolymer Concrete ◽  
Soft Computing Techniques ◽  
Water Ratio

Fly ash (FA) is a residual from thermal industries that has been effectively utilized in the production of FA-based geopolymer concrete (FGPC). To avoid time-consuming and costly experimental procedures, soft computing techniques, namely, random forest regression (RFR) and gene expression programming (GEP), are used in this study to develop an empirical model for the prediction of compressive strength of FGPC. A widespread, reliable, and consistent database of compressive strength of FGPC is set up via a comprehensive literature review. The database consists of 298 compressive strength data points. The influential parameters that are considered as input variables for modelling are curing temperature T , curing time t , age of the specimen A , the molarity of NaOH solution M , percent SiO2 solids to water ratio %   S / W in sodium silicate (Na2SiO3) solution, percent volume of total aggregate (   %   A G ), fine aggregate to the total aggregate ratio F / A G , sodium oxide (Na2O) to water ratio N / W in Na2SiO3 solution, alkali or activator to the FA ratio A L / F A , Na2SiO3 to NaOH ratio N s / N o , percent plasticizer ( %   P ), and extra water added as percent FA E W % . RFR is an ensemble algorithm and gives outburst performance as compared to GEP. However, GEP proposed an empirical expression that can be used to estimate the compressive strength of FGPC. The accuracy and performance of both models are evaluated via statistical error checks, and external validation is considered. The proposed GEP equation is used for sensitivity analysis and parametric study and then compared with nonlinear and linear regression expressions.

scholarly journals Non-Tuned Machine Learning Approach for Predicting the Compressive Strength of High-Performance Concrete

Materials ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1023
Author(s):  
Abobakr Khalil Al-Shamiri ◽  
Tian-Feng Yuan ◽  
Joong Hoon Kim
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Prediction Model ◽  
Extreme Learning Machine ◽  
High Performance ◽  
High Performance Concrete ◽  
Fine Aggregate ◽  
Input Variables ◽  
Prediction Reliability ◽  
Learning Machine

Compressive strength is considered as one of the most important parameters in concrete design. Time and cost can be reduced if the compressive strength of concrete is accurately estimated. In this paper, a new prediction model for compressive strength of high-performance concrete (HPC) was developed using a non-tuned machine learning technique, namely, a regularized extreme learning machine (RELM). The RELM prediction model was developed using a comprehensive dataset obtained from previously published studies. The input variables of the model include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age of specimens. k-fold cross-validation was used to assess the prediction reliability of the developed RELM model. The prediction results of the RELM model were evaluated using various error measures and compared with that of the standard extreme learning machine (ELM) and other methods presented in the literature. The findings of this research indicate that the compressive strength of HPC can be accurately estimated using the proposed RELM model.

scholarly journals Computation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques

Materials ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7034
Author(s):  
Yue Xu ◽  
Waqas Ahmad ◽  
Ayaz Ahmad ◽  
Krzysztof Adam Ostrowski ◽  
Marta Dudek ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Support Vector Regression ◽  
High Performance ◽  
Cross Validation ◽  
High Performance Concrete ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Fold Cross Validation

The current trend in modern research revolves around novel techniques that can predict the characteristics of materials without consuming time, effort, and experimental costs. The adaptation of machine learning techniques to compute the various properties of materials is gaining more attention. This study aims to use both standalone and ensemble machine learning techniques to forecast the 28-day compressive strength of high-performance concrete. One standalone technique (support vector regression (SVR)) and two ensemble techniques (AdaBoost and random forest) were applied for this purpose. To validate the performance of each technique, coefficient of determination (R2), statistical, and k-fold cross-validation checks were used. Additionally, the contribution of input parameters towards the prediction of results was determined by applying sensitivity analysis. It was proven that all the techniques employed showed improved performance in predicting the outcomes. The random forest model was the most accurate, with an R2 value of 0.93, compared to the support vector regression and AdaBoost models, with R2 values of 0.83 and 0.90, respectively. In addition, statistical and k-fold cross-validation checks validated the random forest model as the best performer based on lower error values. However, the prediction performance of the support vector regression and AdaBoost models was also within an acceptable range. This shows that novel machine learning techniques can be used to predict the mechanical properties of high-performance concrete.

scholarly journals Evaluation of Three Different Machine Learning Methods for Object-Based Artificial Terrace Mapping—A Case Study of the Loess Plateau, China

2021 ◽  
Vol 13 (5) ◽  
pp. 1021
Author(s):  
Hu Ding ◽  
Jiaming Na ◽  
Shangjing Jiang ◽  
Jie Zhu ◽  
Kai Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Loess Plateau ◽  
Water Conservation ◽  
Nearest Neighbor ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
The Loess Plateau ◽  
Object Based ◽  
Extreme Gradient Boosting

Artificial terraces are of great importance for agricultural production and soil and water conservation. Automatic high-accuracy mapping of artificial terraces is the basis of monitoring and related studies. Previous research achieved artificial terrace mapping based on high-resolution digital elevation models (DEMs) or imagery. As a result of the importance of the contextual information for terrace mapping, object-based image analysis (OBIA) combined with machine learning (ML) technologies are widely used. However, the selection of an appropriate classifier is of great importance for the terrace mapping task. In this study, the performance of an integrated framework using OBIA and ML for terrace mapping was tested. A catchment, Zhifanggou, in the Loess Plateau, China, was used as the study area. First, optimized image segmentation was conducted. Then, features from the DEMs and imagery were extracted, and the correlations between the features were analyzed and ranked for classification. Finally, three different commonly-used ML classifiers, namely, extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor (KNN), were used for terrace mapping. The comparison with the ground truth, as delineated by field survey, indicated that random forest performed best, with a 95.60% overall accuracy (followed by 94.16% and 92.33% for XGBoost and KNN, respectively). The influence of class imbalance and feature selection is discussed. This work provides a credible framework for mapping artificial terraces.

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