Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

Ly, Haï‐Bang; Le, Tien-Thinh; Vu, Huong-Lan Thi; Tran, Van Quan; Le, Lu Minh; Pham, Binh Thai

doi:10.3390/su12072709

Cited by 61 publications

(26 citation statements)

References 132 publications

(170 reference statements)

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“…In the case of MAE, a low MAE indicates good accuracy of prediction output using the models. MAE can be calculated using the following equation [98][99][100][101]:…”

Section: Quality Assessment Criteriamentioning

confidence: 99%

Prediction of Ultimate Load of Rectangular CFST Columns Using Interpretable Machine Learning Method

Phan

2020

Advances in Civil Engineering

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The ultimate compressive load of concrete-filled steel tubular (CFST) structural members is recognized as one of the most important engineering parameters for the design of such composite structures. Therefore, this paper deals with the prediction of ultimate load of rectangular CFST structural members using the adaptive neurofuzzy inference system (ANFIS) surrogate model. To this end, compression test data on CFST members were extracted from the available literature, including: (i) the mechanical properties of the constituent materials (i.e., steel’s yield strength and concrete’s compressive strength) and (ii) the geometric parameters (i.e., column length, width and height of cross section, and steel tube thickness). The ultimate load is the output response of the problem. The ANFIS model was trained using a hybrid of the least-squares and backpropagation gradient descent method. Quality assessment criteria such as coefficient of determination (R2), root mean square error (RMSE), and slope of linear regression were used for error measurements. A 11-fold cross-validation technique was employed to evaluate the performance of the model. Results showed that for the training process, the average performance was as follows: R2, RMSE, and slope were 0.9861, 89.83 kN, and 0.9861, respectively. For the validating process, the average performance was as follows: R2, RMSE, and slope were 0.9637, 140.242 kN, and 0.9806, respectively. Therefore, the ANFIS model may be considered valid because it performs well in predicting ultimate load using the validated data. Moreover, partial dependence (PD) analysis was employed to interpret the “black-box” ANFIS model. It is observed that PD enabled us to locally track the influence of each input variable on the output response. Besides reliable prediction of ultimate load, ANFIS can also provide maps of ultimate load. Finally, the ANFIS model developed in this study was compared with other works in the literature, showing that the ANFIS model could improve the accuracy of ultimate load prediction, in comparison to previously published results.

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“…In the case of MAE, a low MAE indicates good accuracy of prediction output using the models. MAE can be calculated using the following equation [98][99][100][101]:…”

Section: Quality Assessment Criteriamentioning

confidence: 99%

Prediction of Ultimate Load of Rectangular CFST Columns Using Interpretable Machine Learning Method

Phan

2020

Advances in Civil Engineering

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“…AI models have profound application in structural engineering owing to the ability to provide remarkable solutions [19][20][21]. AI models can provide solutions to problems associated with high stochasticity, nonlinearity, and nonstationarity.…”

Section: Introductionmentioning

confidence: 99%

Random Forest versus Support Vector Machine Models’ Applicability for Predicting Beam Shear Strength

Mohammed

Ismail

2021

Complexity

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The shear and bending are the actions that are experienced in the beam owing to the fact that the beam is a flexural member due to the load in the transverse direction to their longitudinal axis. The shear strength (Vs) computation of reinforced concrete (RC) beams has been a major topic in the field of structural engineering. There have been several methodologies introduced for the Vs prediction; however, the modeling accuracy is relatively low owing to the complex characteristic of the resistance mechanism involving dowel effect of longitudinal reinforcement, concrete in the compression zone, contribution of the stirrups if existed, and the aggregate interlock. Hence, the current research proposed a new soft computing model called random forest (RF) to predict Vs. Experimental datasets were collected from the open-source literature including the related geometric properties and concrete characteristics of beam specimens. Nine input combinations were constructed based on the statistical correlation to be supplied for the proposed predictive model. The prediction accuracy of the RF model was validated against the Support Vector Machine (SVM), and several other empirical formulations have been adopted in the literature. The proposed RF model revealed better prediction accuracy in addition the model structure emphasis in the incorporation of seven predictors by excluding (beam flange thickness and coefficient). In the quantitative term, the minimal root mean square error value was attained (RMSE = 89.68 kN).

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“…In recent years, Artificial Intelligence (AI) has been widely used for modeling many problems in the areas of science and engineering [13][14][15][16][17]. AI approaches have been developed to predict different properties of concrete, such as the shear strength of reinforced concrete beams [18,19], corrosion of concrete sewers [20], crack width of concrete [21], the ultimate strength of reinforced concrete beams [22], strength of recycled aggregate concrete [23], the compressive strength of silica fume concrete [24], compressive strength of geopolymer concrete [25], compressive strength prediction of concrete using BFS [26][27][28][29][30][31], or concrete using FA [32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Investigation of ANN Model Containing One Hidden Layer for Predicting Compressive Strength of Concrete with Blast-Furnace Slag and Fly Ash

Thi

Nguyen

et al. 2021

Advances in Materials Science and Engineering

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The prediction accuracy of concrete compressive strength is important and considered a challenging task, aiming at reducing costly and time-consuming experiments. Moreover, compressive strength prediction of concrete using blast-furnace slag (BFS) and fly ash (FA) is more difficult due to the complex mix design of a composition. In this investigation, an approach using the artificial neuron network (ANN), one of the most powerful machine learning algorithms, is applied to predict the compressive strength of concrete containing BFS and FA. The ANN models with one hidden layer containing 13 neuron number cases are proposed to determine the best ANN structure. Under the effect of random sampling strategies and the network structures selected, Monte Carlo simulations (MCS) are introduced to statistically investigate the convergence of results. Next, the evaluation of the model is concluded over 100 simulations for the convergence analysis. The results show that ANN is a highly efficient predictor of the compressive strength using BFS and FA, with maximum values of the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) of 0.9437, 3.9474, and 2.9074, respectively, on the training part and 0.9285, 4.4266, and 3.2971, respectively, for the testing part. The best-defined structure of ANN is [8-24-1], with 24 neurons in the hidden layer. Partial Dependence Plots (PDP) are also performed to investigate the dependence of the prediction results of input variables used in the ANN model. The age of sample and cement content are found to be the two most crucial factors that affect the compressive strength of concrete using BFS and FA. The ANN algorithm is practical for engineers to reduce costly experiments.

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Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

Cited by 61 publications

References 132 publications

Prediction of Ultimate Load of Rectangular CFST Columns Using Interpretable Machine Learning Method

Prediction of Ultimate Load of Rectangular CFST Columns Using Interpretable Machine Learning Method

Random Forest versus Support Vector Machine Models’ Applicability for Predicting Beam Shear Strength

Investigation of ANN Model Containing One Hidden Layer for Predicting Compressive Strength of Concrete with Blast-Furnace Slag and Fly Ash

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