Evaluation of Factors Affecting Compressive Strength of Concrete using Machine Learning

Recently, there has been a tendency to use machine learning (ML)–based methods, such as artificial neural networks (ANNs), for more accurate estimates. This paper investigates the effectiveness of three different machine learning methods including radial basis function neural network (RBNN), multi-layer perceptron (MLP), and support vector regression (SVR), for predicting the ultimate strength of square and rectangular columns confined by various FRP sheets. So far, in the previous study, several experiments have been conducted on concrete columns confined by fiber reinforced polymer (FRP) sheets with the results suggesting that the use of FRP sheets enhances the compressive strength of concrete columns effectively. Also, a wide range of experimental data (including 463 specimens) has been collected in this study for square and rectangular columns, confined by various FRP sheets. The comparison of ML-derived results with the experimental findings, which were in a very good agreement, demonstrated the ability of ML to estimate the compressive strength of concrete confined by FRP; the correlation coefficient (R2) for MLP, RBFNN, and SVR methods was equal to 0.97, 0.97, and 0.90, respectively. Similar accuracy was obtained by MLP and RBFNN, and they provided better estimates for determining the compressive strength of concrete confined by FRP. Also, the results showed that the difference between statistical indicators for training and testing specimens in the RBFNN method was greater than the MLP method, and this difference indicated the poor performance of RBFNN.

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111) Study on Various Factors Affecting Compressive Strength of Concrete in the Field(Materials・Execution)

Transactions of the Architectural Institute of Japan ◽

10.3130/aijsaxx.57.1.0_41 ◽

1957 ◽

Vol 57.1 (0) ◽

pp. 41-44

Author(s):

Hisao Takahashi ◽

Kiichiro Kobayashi

Keyword(s):

Compressive Strength ◽

Factors Affecting ◽

Compressive Strength Of Concrete

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Machine learning prediction of concrete compressive strength with data enhancement

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-211088 ◽

2021 ◽

pp. 1-10

Author(s):

Xiaoning Cui ◽

Qicai Wang ◽

Rongling Zhang ◽

Jinpeng Dai ◽

Sheng Li

Keyword(s):

Machine Learning ◽

Compressive Strength ◽

Prediction Accuracy ◽

Data Augmentation ◽

Machine Learning Algorithms ◽

Data Sets ◽

Concrete Compressive Strength ◽

Contribution Rate ◽

Generalization Ability ◽

Compressive Strength Of Concrete

The compressive strength of concrete can be predicted by machine learning. One thousand thirty samples of concrete compressive strength data were used as the dataset. Machine learning was applied to prediction of concrete compressive strength with seven machine learning algorithms. To improve data utilization and generalization ability of machine learning model, ten data sets were constructed by feature reorganization for data augmentation. Compared with other machine learning models, the XGBoost model based on Boosting tree algorithm had the highest prediction accuracy and the most robust generalization ability. With different multi-feature combination input conditions, the R2 score of the XGBoost algorithm was 0.9283, the MAE score was 3.4292, the MAPE score was 12.5656, and the RMSE score was 5.2813. The error accumulation curve of the XGBoost algorithm was analyzed. When the compressive strength of concrete is at 5–20MPa, the error contribution rate is higher. When the concrete compressive strength is at 20–40MPa, the prediction result error of the model drops sharply. When the strength reaches 40MPa, the error contribution rate of the model tends to converge and the error contribution rate is stable between 1 and 1.2, which indicates that the model has high prediction accuracy when the compressive strength is higher than 40 MPa.

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Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete

Advances in Civil Engineering ◽

10.1155/2018/5481705 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 9

Author(s):

Palika Chopra ◽

Rajendra Kumar Sharma ◽

Maneek Kumar ◽

Tanuj Chopra

Keyword(s):

Machine Learning ◽

Compressive Strength ◽

High Accuracy ◽

Machine Learning Techniques ◽

Coefficient Of Determination ◽

Software Environment ◽

R Software ◽

Learning Techniques ◽

Compressive Strength Of Concrete ◽

Indispensable Tool

A comparative analysis for the prediction of compressive strength of concrete at the ages of 28, 56, and 91 days has been carried out using machine learning techniques via “R” software environment. R is digging out a strong foothold in the statistical realm and is becoming an indispensable tool for researchers. The dataset has been generated under controlled laboratory conditions. Using R miner, the most widely used data mining techniques decision tree (DT) model, random forest (RF) model, and neural network (NN) model have been used and compared with the help of coefficient of determination (R2) and root-mean-square error (RMSE), and it is inferred that the NN model predicts with high accuracy for compressive strength of concrete.

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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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