Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network and support vector machine

Ghanizadeh, Ali Reza; Abbaslou, Hakime; Amlashi, Amir Tavana; Alidoust, Pourya

doi:10.1007/s11709-018-0489-z

Cited by 54 publications

(21 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One can determine and capture the interactions between various variables of a complex system by using ANNs, despite the unknown and usually unpredictable nature of these interactions. ANN models can predict the compressive strength of confined plastic concrete accurately [53][54][55][56]. Khademi et al [57] and Reddy [58] used ANN techniques for the prediction of the axial strength of confined concrete.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Properties of FRP-Confined Concrete Cylinders Based on Artificial Neural Networks

2020

View full text Add to dashboard Cite

Recently, the use of fiber-reinforced polymers (FRP)-confinement has increased due to its various favorable effects on concrete structures, such as an increase in strength and ductility. Therefore, researchers have been attracted to exploring the behavior and efficiency of FRP-confinement for concrete structural elements further. The current study investigates improved strength and strain models for FRP confined concrete cylindrical elements. Two new physical methods are proposed for use on a large preliminary evaluated database of 708 specimens for strength and 572 specimens for strain from previous experiments. The first approach is employing artificial neural networks (ANNs), and the second is using the general regression analysis technique for both axial strength and strain of FRP-confined concrete. The accuracy of the newly proposed strain models is quite satisfactory in comparison with previous experimental results. Moreover, the predictions of the proposed ANN models are better than the predictions of previously proposed models based on various statistical indices, such as the correlation coefficient (R) and mean square error (MSE), and can be used to assess the members at the ultimate limit state.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction of Properties of FRP-Confined Concrete Cylinders Based on Artificial Neural Networks

2020

View full text Add to dashboard Cite

show abstract

“…6) and time-consuming training difficulties (Al-Shamiri et al 2019;Yaseen et al 2018). The process SVM has a slightly lower prediction accuracy than ANN, but it can avoid the case where the model finds the local optimum and has the benefits of reduced model training time and lower computing cost (Ghanizadeh et al 2019). Although SVM is effective in predicting concrete compressive strength, it has several limitations when applied to big specimen data.…”

Section: Compressive Strengthmentioning

confidence: 99%

Progress in Artificial Intelligence-based Prediction of Concrete Performance

et al. 2021

ACT

View full text Add to dashboard Cite

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.

show abstract

“…Intelligent dynamic systems, such as ANNs, have been under researchers' focus recently [44][45][46][47][48][49][50][51]. ANNs are able to identify the relationship among data by analyzing them and to then exploit this relationship in further analyses [52].…”

Section: Predicting Earned Value Using Artificial Neural Networkmentioning

confidence: 99%

Improving the Results of the Earned Value Management Technique Using Artificial Neural Networks in Construction Projects

et al. 2020

View full text Add to dashboard Cite

The cost, time and scope of a construction project are key parameters for its success. Thus, predicting these indices is indispensable. Correct and accurate prediction of cost throughout the progress of a project gives project managers the chance to identify projects that need revision in their schedules in order to result in the maximum benefit. The aim of this study is to minimize the shortcomings of the Earned Value Management (EVM) method using an Artificial Neural Network (ANN) and multiple regression analysis in order to predict project cost indices more precisely. A total of 50 road construction projects in Fars Province, Iran, were selected for analysis in this research. An ANN model was used to predict the projects’ cost performance indices, thereby creating a more accurate symmetry between the predicted and actual cost by considering factors that influence project success. The input data of the ANN model were analysed in MATLAB software. A multiple regression model was also used as another analytical tool to validate the outcome of the ANN. The results showed that the ANN model resulted in a lower Mean Squared Error (MSE) and a greater correlation coefficient than both the traditional EVM model and the multiple regression model.

show abstract

Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network and support vector machine

Cited by 54 publications

References 39 publications

Prediction of Properties of FRP-Confined Concrete Cylinders Based on Artificial Neural Networks

Prediction of Properties of FRP-Confined Concrete Cylinders Based on Artificial Neural Networks

Progress in Artificial Intelligence-based Prediction of Concrete Performance

Improving the Results of the Earned Value Management Technique Using Artificial Neural Networks in Construction Projects

Contact Info

Product

Resources

About