Practical machine learning-based prediction model for axial capacity of square CFST columns

Le, Tien-Thinh

doi:10.1080/15376494.2020.1839608

Cited by 67 publications

(17 citation statements)

References 80 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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“…The material strength range for the database was 259-835 MPa and 7.9-164.1 MPa for steel and concrete, respectively. Le (2022) predicted the strength of the CFST columns by using Gaussian Process Regression (GPR) ML model. GPR has an exceptional capacity to handle large-dimension databases (S5 for the study).…”

Section: Cfst Columnsmentioning

confidence: 99%

Machine learning applications to predict the axial compression capacity of concrete filled steel tubular columns: a systematic review

Narang

Kumar

Dhiman

2022

MMMS

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PurposeThis study seeks to understand the connection of methodology by finding relevant papers and their full review using the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA).Design/methodology/approachConcrete-filled steel tubular (CFST) columns have gained popularity in construction in recent decades as they offer the benefit of constituent materials and cost-effectiveness. Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Gene Expression Programming (GEP) and Decision Trees (DTs) are some of the approaches that have been widely used in recent decades in structural engineering to construct predictive models, resulting in effective and accurate decision making. Despite the fact that there are numerous research studies on the various parameters that influence the axial compression capacity (ACC) of CFST columns, there is no systematic review of these Machine Learning methods.FindingsThe implications of a variety of structural characteristics on machine learning performance parameters are addressed and reviewed. The comparison analysis of current design codes and machine learning tools to predict the performance of CFST columns is summarized. The discussion results indicate that machine learning tools better understand complex datasets and intricate testing designs.Originality/valueThis study examines machine learning techniques for forecasting the axial bearing capacity of concrete-filled steel tubular (CFST) columns. This paper also highlights the drawbacks of utilizing existing techniques to build CFST columns, and the benefits of Machine Learning approaches over them. This article attempts to introduce beginners and experienced professionals to various research trajectories.

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“…MLP consists of three main layers, fully connected: the input layer, hidden layer, and output layer [29]. anks to its properties, MLP has been used to predict tool wear flank and surface roughness [30][31][32].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Applying Bayesian Optimization for Machine Learning Models in Predicting the Surface Roughness in Single-Point Diamond Turning Polycarbonate

Nguyen

Truong

et al. 2021

Mathematical Problems in Engineering

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This paper deals with the prediction of surface roughness in manufacturing polycarbonate (PC) by applying Bayesian optimization for machine learning models. The input variables of ultraprecision turning—namely, feed rate, depth of cut, spindle speed, and vibration of the X-, Y-, and Z-axis—are the main factors affecting surface quality. In this research, six machine learning- (ML-) based models—artificial neural network (ANN), Cat Boost Regression (CAT), Support Vector Machine (SVR), Gradient Boosting Regression (GBR), Decision Tree Regression (DTR), and Extreme Gradient Boosting Regression (XGB)—were applied to predict the surface roughness (Ra). The predictive performance of the baseline models was quantitatively assessed through error metrics: root means square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The overall results indicate that the XGB and CAT models predict Ra with the greatest accuracy. In improving baseline models such as XGB and CAT, the Bayesian optimization (BO) is next used to determine their best hyperparameters, and the results indicate that XGB is the best model according to the evaluation metrics. Results have shown that the performance of the models has been improved significantly with BO. For example, the values of RMSE and MAE of XGB have decreased from 0.0076 to 0.0047 and from 0.0063 to 0.0027, respectively, for the training dataset. Using the testing dataset, the values of RMSE and MAE of XGB have decreased from 0.4033 to 0.2512 and from 0.2845 to 0.2225, respectively. Moreover, the vibrations of the X, Y, and Z axes and feed rate are the most significant feature in predicting the results, which is in high accordance with the literature. We find that, in a specified value domain, the vibration of the axes has a greater influence on the surface quality than does the cutting condition.

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Practical machine learning-based prediction model for axial capacity of square CFST columns

Cited by 67 publications

References 80 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

Machine learning applications to predict the axial compression capacity of concrete filled steel tubular columns: a systematic review

Applying Bayesian Optimization for Machine Learning Models in Predicting the Surface Roughness in Single-Point Diamond Turning Polycarbonate

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