Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models

Ly, Haï‐Bang; Pham, Binh Thai; Le, Lu Minh; Le, Tien-Thinh; Le, Vuong Minh

doi:10.1007/s00521-020-05214-w

Cited by 89 publications

(31 citation statements)

References 143 publications

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“…Artificial intelligence-(AI-) based models have received significant attention from researchers all around the world, especially in civil engineering-related problems [35][36][37][38][39][40][41][42][43][44][45][46]. For single-material structures, various studies have set out to predict (i) the buckling capacity of steel members [47][48][49][50] and (ii) the compressive strength of concrete [51][52][53][54][55].…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…MAE represents the difference between the original and predicted values, extracted by averaging the absolute difference over the chosen dataset (equation 4) [54][55][56]. Besides, RMSE is the error rate by the square root of MSE, as shown in equation (5) [5,57,58]. R is an important indicator of regression analysis [59,60].…”

Section: Performance Indicatorsmentioning

confidence: 99%

Backpropagation Neural Network-Based Machine Learning Model for Prediction of Soil Friction Angle

Nguyen

Pham

2020

Mathematical Problems in Engineering

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In the design process of foundations, pavements, retaining walls, and other geotechnical matters, estimation of soil strength-related parameters is crucial. In particular, the friction angle is a critical shear strength factor in assessing the stability and deformation of geotechnical structures. Practically, laboratory or field tests have been conducted to determine the friction angle of soil. However, these jobs are often time-consuming and quite expensive. Therefore, the prediction of geo-mechanical properties of soils using machine learning techniques has been widely applied in recent times. In this study, the Bayesian regularization backpropagation algorithm is built to predict the internal friction angle of the soil based on 145 data collected from experiments. The performance of the model is evaluated by three specific statistical criteria, such as the Pearson correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE). The results show that the proposed algorithm performed well for the prediction of the friction angle of soil (R = 0.8885, RMSE = 0.0442, and MAE = 0.0328). Therefore, it can be concluded that the backpropagation neural network-based machine learning model is a reasonably accurate and useful prediction tool for engineers in the predesign phase.

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“…These techniques are a branch of computational intelligence that employ a variety of statistical and optimization tools to learn from past examples and to then utilize that prior training to estimate novel trends. ML and SC methods have been widely employed in several research areas [26], [27], [36]- [45], [28], [46]- [55], [29], [56]- [65], [30], [66], [67], [31]- [35]. In terms of the applications of ML and SC in RB classification and prediction, the initial attempts were made by Feng and Wang [68], who established artificial neural networks (ANNs) for controlling and predicting the likelihood of RB.…”

Section: Introductionmentioning

confidence: 99%

Rock-Burst Occurrence Prediction Based on Optimized Naïve Bayes Models

Khandelwal²,

Asteris³

et al. 2021

IEEE Access

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Rock-burst is a common failure in hard rock related projects in civil and mining construction and therefore, proper classification and prediction of this phenomenon is of interest. This research presents the development of optimized naïve Bayes models, in predicting rock-burst failures in underground projects. The naïve Bayes models were optimized using four weight optimization techniques including forward, backward, particle swarm optimization, and evolutionary. An evolutionary random forest model was developed to identify the most significant input parameters. The maximum tangential stress, elastic energy index, and uniaxial tensile stress were then selected by the feature selection technique (i.e., evolutionary random forest) to develop the optimized naïve Bayes models. The performance of the models was assessed using various criteria as well as a simple ranking system. The results of this research showed that particle swarm optimization was the most effective technique in improving the accuracy of the naïve Bayes model for rock-burst prediction (cumulative ranking = 21), while the backward technique was the worst weight optimization technique (cumulative ranking = 11). All the optimized naïve Bayes models identified the maximum tangential stress as the most significant parameter in predicting rock-burst failures. The results of this research demonstrate that particle swarm optimization technique may improve the accuracy of naïve Bayes algorithms in predicting rock-burst occurrence.

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Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models

Cited by 89 publications

References 143 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

Backpropagation Neural Network-Based Machine Learning Model for Prediction of Soil Friction Angle

Rock-Burst Occurrence Prediction Based on Optimized Naïve Bayes Models

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