An Artificial Neural Networks model for the prediction of the compressive strength of FRP-confined concrete circular columns

Cascardi, Alessio; Micelli, Francesco; Aiello, Maria Antonietta

doi:10.1016/j.engstruct.2017.02.047

Cited by 164 publications

(71 citation statements)

References 34 publications

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“…The testing data were selected randomly to eliminate the effects of artificial selection on the results. In accordance with existing calculation models [31][32][33][34][35][36][37][38], the water to binder ratio (W/B), the diameter (D), the length (L), the aspect ratio (AR), and the volume fraction (VF) of steel fibers, and the compressive strength (PCS) or the flexural strength (PFS) of UHPFRC without steel fibers were selected as the main input parameters, and the compressive strength (CS) or flexural strength (FS) of UHPFRC was used as the output variable. Table 3 shows the ranges of the input and the output variables in the compressive strength database and the flexural strength database in this study.…”

Section: Data Collectionsupporting

confidence: 63%

“…In this study, six indicators were applied in order to evaluate the performance of the compressive strength ANN model and five indicators for the flexural strength ANN model, respectively. These three indicators are root mean square error (RMS), absolute fraction of variance (R 2 ), and integral absolute error (IAE), which are calculated by Equations (11)-(13), respectively [38,45]. Theoretically, When the RMS and the IAE tend to zero and the R 2 tends to one, the proposed models predict the experimental data accurately.…”

Section: Results Assessment Criteriamentioning

confidence: 99%

“…Thus, when new test data is available, the predictive accuracy and reliability of those empirical models have to be re-evaluated.In recent years, the artificial intelligence techniques of artificial neural networks (ANNs) have been widely adopted to solve many engineering complex problems due to its high accuracy and adaptability. The ANN model has been successfully used to evaluate the early-age autogenous shrinkage of concrete [37], estimate the compressive strength of FRP-confined (FRP denotes fiber reinforced polymer) concrete circular columns [38], optimize the mix compositions of steel fiber-reinforced concrete [39], and so on. However, very few studies have been conducted on the application of ANNs to predict the compressive strength and the flexural strength of UHPFRC.…”

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confidence: 99%

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Evaluating the Effects of Steel Fibers on Mechanical Properties of Ultra-High Performance Concrete Using Artificial Neural Networks

Cai

Chang

2018

Applied Sciences

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Featured Application: This work can be utilized to predict the flexural strength and the compressive strength of ultra-high performance concrete (UHPC), determine the volume fraction of steel fibers in ultra-high performance steel fiber reinforced concrete (UHPFRC), and optimize the UHPFRC mixtures.Abstract: Steel fibers enhance the flexural strength, the compressive strength and the ductility of untra-high performance concrete, predicting the flexural strength and the compressive strength of ultra-high performance steel fiber reinforced concrete (UHPFRC) accurately has significant influence on controlling steel fiber volume fraction and optimizing UHPFRC mix proportion. In this study, to evaluate the effects of steel fibers on the mechanical properties of UHPFRC, two artificial neural networks were developed in order to predict the flexural strength and the compressive strength of UHPFRC, respectively. 102 test data sets and 162 test data sets from literature were trained and tested to establish the flexural strength model and the compressive strength model, respectively. In these two models, the influential parameters, including the water to binder ratio, the diameter, the length, the aspect ratio, and the volume fraction of steel fibers, as well as the compressive strength and the flexural strength of concrete without fibers were investigated as the inputs, while the compressive strength and the flexural strength of UHPFRC were the outputs. The results show that the artificial neural network models predicted the compressive strength and flexural strength of UHPFRC accurately. Then, by comparing with existing analytical models, it was determined that the proposed models had high applicability and reliability with respect to predicting the compressive strength and the flexural strength of UHPFRC.the strength and the ductility of UHPFRC [3]. Unfortunately, too many steel fibers lead to fibers inter-wrap and interlock with each other, affecting the workability of UHPFRC, to reduce the strength of UHPFRC [4]. Furthermore, steel fibers are expensive and numerous steel fibers added to the UHPFRC cost too much. Thus, predicting the compressive strength and the flexural strength of UHPFRC accurately can optimize mix proportion, control the volume fraction of steel fibers, and decrease the costs of UHPFRC. However, evaluating the flexural strength and the compressive strength of the UHPFRC is a huge challenge due to the complex composite behavior caused by the properties steel fibers (diameter (D), length (L), aspect ratio (AR), and volume fraction (VF)) and concrete matrix (water to binder ratio (W/B) and concrete strength without fibers).Nowadays, the contribution of cement-based materials to sustainability is a topic of study [5-8] and the performance of several additions in cement-based materials, such as silica fume, fly ash, the water to cement ratio, and so on, has been analyzed, which could be also suitable for being used in high performance concrete. This fact also makes it necessary to consider their influence...

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Section: Data Collectionsupporting

confidence: 63%

Section: Results Assessment Criteriamentioning

confidence: 99%

mentioning

confidence: 99%

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Evaluating the Effects of Steel Fibers on Mechanical Properties of Ultra-High Performance Concrete Using Artificial Neural Networks

Cai

Chang

2018

Applied Sciences

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“…Because the optimization of a multiple-factor system can easily fall into a local optimum, the uniform design method and a neural network model [20,21] are employed. Considering that the driving force of concrete cracking is the principal tensile stress rather than the maximum temperature or rate of temperature decrease, we focus on the ratio of the relationship between the principal tensile stress and the tensile strength at the corresponding age of the sluice pier concrete; this ratio can be expressed as follows:…”

Section: Optimization Of the Temperature-control Measures For Pier Comentioning

confidence: 99%

Optimization of Temperature‐Control Measures for Concrete Structures: A Case Study of the Sluice Project

Huang

2018

Advances in Civil Engineering

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Temperature control and crack prevention in sluice pier concrete is a key issue in the early design and construction period. Strong surface insulation may lead to cracks after formwork removal, while weak surface insulation may result in a high crack risk in the early age. e water-cooling measure may also cause severe cracks at a rapid cooling rate. erefore, the optimum temperature control scheme should be comparatively studied against the alternatives. In this paper, we investigate crack prevention in sluice pier concrete as a multiple-factor system optimization problem and investigate an optimization method for temperature-control measures using the uniform design method and a neural network model. e minimum ratios for the internal and surface points of the sluice pier concrete are taken as inputs, and the corresponding combinations of temperature-control parameters based on the uniform design method are taken as outputs. Combined with a sluice project, the optimization method for the temperaturecontrol measures is implemented. e analysis results show that internal pipe cooling combined with reasonable surface heat preservation measures should be employed, and a low concrete pouring temperature is more beneficial than a low cooling temperature and long duration for crack prevention in sluice pier concrete.

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“…Pham and Hadi [16] proposed the utilization of neural networks to compute the strain and compressive strength of FRP-confined columns, and the results show agreement between proposed neural network models and experimental data. Also, there are several studies related to design-oriented and analysis-oriented models [9,[17][18][19][20][21][22][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Ultimate Strain and Strength of FRP-Confined Concrete Cylinders Using Soft Computing Methods

et al. 2017

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This paper investigates the effectiveness of four different soft computing methods, namely radial basis neural network (RBNN), adaptive neuro fuzzy inference system (ANFIS) with subtractive clustering (ANFIS-SC), ANFIS with fuzzy c-means clustering (ANFIS-FCM) and M5 model tree (M5Tree), for predicting the ultimate strength and strain of concrete cylinders confined with fiber-reinforced polymer (FRP) sheets. The models were compared according to the root mean square error (RMSE), mean absolute relative error (MARE) and determination coefficient (R 2 ) criteria. Similar accuracy was obtained by RBNN and ANFIS-FCM, and they provided better estimates in modeling ultimate strength of confined concrete. The ANFIS-SC, however, performed slightly better than the RBNN and ANFIS-FCM in estimating ultimate strain of confined concrete, and M5Tree provided the worst strength and strain estimates. Finally, the effects of strain ratio and the confinement stiffness ratio on strength and strain were investigated, and the confinement stiffness ratio was shown to be more effective.

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An Artificial Neural Networks model for the prediction of the compressive strength of FRP-confined concrete circular columns

Cited by 164 publications

References 34 publications

Evaluating the Effects of Steel Fibers on Mechanical Properties of Ultra-High Performance Concrete Using Artificial Neural Networks

Evaluating the Effects of Steel Fibers on Mechanical Properties of Ultra-High Performance Concrete Using Artificial Neural Networks

Optimization of Temperature‐Control Measures for Concrete Structures: A Case Study of the Sluice Project

Prediction of Ultimate Strain and Strength of FRP-Confined Concrete Cylinders Using Soft Computing Methods

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