Machine learning techniques to predict the compressive strength of concrete

Silva, Priscila F. S.; Moita, Gray Farias; Arruda, Vanderci Fernandes

doi:10.23967/j.rimni.2020.09.008

Cited by 21 publications

(11 citation statements)

References 30 publications

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“…Although all three types of algorithms have a lot of potential for concrete performance prediction, the prediction accuracy can reach 0.85 for a limited number of samples (Chou et al 2011(Chou et al , 2014Young et al 2019), but the prediction accuracy and calculation time are varied, as shown in Fig. 2 (Silva et al 2020). SVM prediction accuracy is lower than ANN when the number of sample sets is around 1000, but the time cost is cheap; ANN prediction accuracy is generally not less than 0.9 (Ben Chaabene et al 2020), but the time cost is 10-20 times more than the other two types of algorithms.…”

Section: Ai Algorithm Overviewmentioning

confidence: 99%

Progress in Artificial Intelligence-based Prediction of Concrete Performance

et al. 2021

ACT

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Artificial intelligence technology has super high-dimensional nonlinear computing capabilities, intelligent comprehensive analysis and judgment functions, and self-learning knowledge reserve expression functions. It can unlock the potential of high-dimensional nonlinearity relation between tangible components and performance indicators when compared to the empirical formula generated from classic statistical approaches. This article summarizes the types of artificial intelligence algorithms used to predict concrete performance, comprehensively sorts out the research progress of artificial intelligence technology in predicting the mechanical properties, work performance, and durability of concrete, and compares and analyzes the effects of algorithm selection, sample data, and model construction on the concrete compressive prediction system. The analysis shows that artificial intelligence technology has obvious advantages in measurement accuracy in predicting concrete performance compared to conventional statistical methods. Multiple algorithms should be used to cross-validate the model prediction findings. For tiny data sets, support vector machines are utilized. Decision tree evolution techniques should be used in algorithm models that require feature optimization or dispersed index prediction. Artificial neural networks can be used to solve different challenges. To improve the prediction model and boost its prediction accuracy, measures such as optimized features, integrated algorithms, hyperparameter optimization, enlarged sample data set, richer data sources, and data pretreatment are proposed.

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Section: Ai Algorithm Overviewmentioning

confidence: 99%

Progress in Artificial Intelligence-based Prediction of Concrete Performance

et al. 2021

ACT

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“…Currently, concrete, as a construction material, is in great demand due to the rapid and advanced growth of infrastructure development in many countries, typically utilized in engineered buildings throughout the globe [1][2][3]; this requires the technology surrounding it to permanently change, looking for improvements and innovations. This is why particular types of concrete have recently emerged, such as self-compacting concrete (SCC), representing an acceptable construction potential while also attracting interest in the use of recycled aggregates (RA) [4][5][6][7][8] from construction and demolition waste (CDW) as a substitute to conventional aggregates [9][10][11], minimizing or potentially eliminating the environmental impacts produced by these CDW [12] and allowing the combination of economic development with sustainability and environmental protection [13].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete

de‐Prado‐Gil

Palencia

Jagadesh³

et al. 2022

Materials

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Several types of research currently use machine learning (ML) methods to estimate the mechanical characteristics of concrete. This study aimed to compare the capacities of four ML methods: eXtreme gradient boosting (XG Boost), gradient boosting (GB), Cat boosting (CB), and extra trees regressor (ETR), to predict the splitting tensile strength of 28-day-old self-compacting concrete (SCC) made from recycled aggregates (RA), using data obtained from the literature. A database of 381 samples from literature published in scientific journals was used to develop the models. The samples were randomly divided into three sets: training, validation, and test, with each having 267 (70%), 57 (15%), and 57 (15%) samples, respectively. The coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) metrics were used to evaluate the models. For the training data set, the results showed that all four models could predict the splitting tensile strength of SCC made with RA because the R2 values for each model had significance higher than 0.75. XG Boost was the model with the best performance, showing the highest R2 value of R2 = 0.8423, as well as the lowest values of RMSE (=0.0581) and MAE (=0.0443), when compared with the GB, CB, and ETR models. Therefore, XG Boost was considered the best model for predicting the splitting tensile strength of 28-day-old SCC made with RA. Sensitivity analysis revealed that the variable contributing the most to the split tensile strength of this material after 28 days was cement.

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“…Typically, engineers use the laboratory test method to design high-performance concrete to meet the mechanical characteristics of high UCS, large flow performance, good durability, and so on [ 27 , 28 ]. However, the laboratory test method is of high cost, time consuming, and effort consuming [ 29 ]. In particular, when multiple properties of concrete need to be optimized, the number of samples that need to be designed will increase exponentially, and the shortcomings of laboratory experimental methods will be more prominent [ 30 , 31 , 32 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

“…Silva et al proposed three models—random forest (RF), support vector machine (SVM), and artificial neural network (ANN)—to predict the UCS of concrete, and compared the results with other models. The results showed that the three models are better [ 29 ]. The above research results showed that the machine learning method has a good effect on predicting the UCS of concrete.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Design of Building Materials: Development of an AI-Based Method for Cement-Slag Concrete Design

Zhu

Zhou

et al. 2022

Materials

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Cement-slag concrete has become one of the most widely used building materials considering its economical advantage and satisfying uniaxial compressive strength (UCS). In this study, an AI-based method for cement-slag concrete design was developed based on the balance of economic and mechanical properties. Firstly, the hyperparameters of random forest (RF), decision tree (DT), and support vector machine (SVM) were tuned by the beetle antennae search algorithm (BAS). The results of the model evaluation showed the RF with the best prediction effect on the UCS of concrete was selected as the objective function of UCS optimization. Afterward, the objective function of concrete cost optimization was established according to the linear relationship between concrete cost and each mixture. The obtained results showed that the weighted method can be used to construct the multi-objective optimization function of UCS and cost for cement-slag concrete, which is solved by the multi-objective beetle antennae search (MOBAS) algorithm. An optimal concrete mixture ratio can be obtained by Technique for Order Preference by Similarity to Ideal Solution. Considering the current global environment trend of “Net Carbon Zero”, the multi-objective optimization design should be proposed based on the objectives of economy-carbon emission-mechanical properties for future studies.

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Machine learning techniques to predict the compressive strength of concrete

Cited by 21 publications

References 30 publications

Progress in Artificial Intelligence-based Prediction of Concrete Performance

Progress in Artificial Intelligence-based Prediction of Concrete Performance

A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete

Intelligent Design of Building Materials: Development of an AI-Based Method for Cement-Slag Concrete Design

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