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This study aims to analyze the sensitivity and robustness of two Artificial Intelligence (AI) techniques, namely Gaussian Process Regression (GPR) with five different kernels (Matern32, Matern52, Exponential, Squared Exponential, and Rational Quadratic) and an Artificial Neural Network (ANN) using a Monte Carlo simulation for prediction of High-Performance Concrete (HPC) compressive strength. To this purpose, 1030 samples were collected, including eight input parameters (contents of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregates, fine aggregates, and concrete age) and an output parameter (the compressive strength) to generate the training and testing datasets. The proposed AI models were validated using several standard criteria, namely coefficient of determination (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). To analyze the sensitivity and robustness of the models, Monte Carlo simulations were performed with 500 runs. The results showed that the GPR using the Matern32 kernel function outperforms others. In addition, the sensitivity analysis showed that the content of cement and the testing age of the HPC were the most sensitive and important factors for the prediction of HPC compressive strength. In short, this study might help in selecting suitable AI models and appropriate input parameters for accurate and quick estimation of the HPC compressive strength.
Dong Van Dao; Hojjat Adeli; Hai-Bang Ly; Lu Minh Le; Vuong Minh Le; Tien-Thinh Le; Binh Thai Pham. A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation. Sustainability 2020, 12, 830 .
AMA StyleDong Van Dao, Hojjat Adeli, Hai-Bang Ly, Lu Minh Le, Vuong Minh Le, Tien-Thinh Le, Binh Thai Pham. A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation. Sustainability. 2020; 12 (3):830.
Chicago/Turabian StyleDong Van Dao; Hojjat Adeli; Hai-Bang Ly; Lu Minh Le; Vuong Minh Le; Tien-Thinh Le; Binh Thai Pham. 2020. "A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation." Sustainability 12, no. 3: 830.
The principal purpose of this work is to develop three hybrid machine learning (ML) algorithms, namely ANFIS-RCSA, ANFIS-CA, and ANFIS-SFLA which are a combination of adaptive neuro-fuzzy inference system (ANFIS) with metaheuristic optimization techniques such as real-coded simulated annealing (RCSA), cultural algorithm (CA) and shuffled frog leaping algorithm (SFLA), respectively, to predict the critical buckling load of I-shaped cellular steel beams with circular openings. For this purpose, the existing database of buckling tests on I-shaped steel beams were extracted from the available literature and used to generate the datasets for modeling. Eight inputs, considered as independent variables, including the beam length, beam end-opening distance, opening diameter, inter-opening distance, section height, web thickness, flange width, and flange thickness, as well as one output of the critical buckling load of cellular steel beams considered as a dependent variable, were used in the datasets. Three quality assessment criteria, namely correlation coefficient (R), root mean squared error (RMSE) and mean absolute error (MAE) were employed for assessment of three developed hybrid ML models. The obtained results indicate that all three hybrid ML models have a strong ability to predict the buckling load of steel beams with circular openings, but ANFIS-SFLA (R = 0.960, RMSE = 0.040 and MAE = 0.017) exhibits the best effectiveness as compared with other hybrid models. In addition, sensitivity analysis was investigated and compared with linear statistical correlation between inputs and output to validate the importance of input variables in the models. The sensitivity results show that the most influenced variable affecting beam buckling capacity is the beam length, following by the flange width, the flange thickness, and the web thickness, respectively. This study shows that the hybrid ML techniques could help in establishing a robust numerical tool for beam buckling analysis. The proposed methodology is also promising to predict other types of failure, as well as other types of perforated beams.
Hai-Bang Ly; Tien-Thinh Le; Lu Minh Le; Van Quan Tran; Vuong Minh Le; Huong-Lan Thi Vu; Quang Hung Nguyen; Binh Thai Pham. Development of Hybrid Machine Learning Models for Predicting the Critical Buckling Load of I-Shaped Cellular Beams. Applied Sciences 2019, 9, 5458 .
AMA StyleHai-Bang Ly, Tien-Thinh Le, Lu Minh Le, Van Quan Tran, Vuong Minh Le, Huong-Lan Thi Vu, Quang Hung Nguyen, Binh Thai Pham. Development of Hybrid Machine Learning Models for Predicting the Critical Buckling Load of I-Shaped Cellular Beams. Applied Sciences. 2019; 9 (24):5458.
Chicago/Turabian StyleHai-Bang Ly; Tien-Thinh Le; Lu Minh Le; Van Quan Tran; Vuong Minh Le; Huong-Lan Thi Vu; Quang Hung Nguyen; Binh Thai Pham. 2019. "Development of Hybrid Machine Learning Models for Predicting the Critical Buckling Load of I-Shaped Cellular Beams." Applied Sciences 9, no. 24: 5458.
The main objective of this study is to develop and compare hybrid Artificial Intelligence (AI) approaches, namely Adaptive Network-based Fuzzy Inference System (ANFIS) optimized by Genetic Algorithm (GAANFIS) and Particle Swarm Optimization (PSOANFIS) and Support Vector Machine (SVM) for predicting the Marshall Stability (MS) of Stone Matrix Asphalt (SMA) materials. Other important properties of the SMA, namely Marshall Flow (MF) and Marshall Quotient (MQ) were also predicted using the best model found. With that goal, the SMA samples were fabricated in a local laboratory and used to generate datasets for the modeling. The considered input parameters were coarse and fine aggregates, bitumen content and cellulose. The predicted targets were Marshall Parameters such as MS, MF and MQ. Models performance assessment was evaluated thanks to criteria such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and correlation coefficient (R). A Monte Carlo approach with 1000 simulations was used to deduce the statistical results to assess the performance of the three proposed AI models. The results showed that the SVM is the best predictor regarding the converged statistical criteria and probability density functions of RMSE, MAE and R. The results of this study represent a contribution towards the selection of a suitable AI approach to quickly and accurately determine the Marshall Parameters of SMA mixtures.
Hoang Nguyen; Thanh-Hai Le; Cao-Thang Pham; Tien-Thinh Le; Lanh Si Ho; Vuong Minh Le; Binh Thai Pham; Hai-Bang Ly. Development of Hybrid Artificial Intelligence Approaches and a Support Vector Machine Algorithm for Predicting the Marshall Parameters of Stone Matrix Asphalt. Applied Sciences 2019, 9, 3172 .
AMA StyleHoang Nguyen, Thanh-Hai Le, Cao-Thang Pham, Tien-Thinh Le, Lanh Si Ho, Vuong Minh Le, Binh Thai Pham, Hai-Bang Ly. Development of Hybrid Artificial Intelligence Approaches and a Support Vector Machine Algorithm for Predicting the Marshall Parameters of Stone Matrix Asphalt. Applied Sciences. 2019; 9 (15):3172.
Chicago/Turabian StyleHoang Nguyen; Thanh-Hai Le; Cao-Thang Pham; Tien-Thinh Le; Lanh Si Ho; Vuong Minh Le; Binh Thai Pham; Hai-Bang Ly. 2019. "Development of Hybrid Artificial Intelligence Approaches and a Support Vector Machine Algorithm for Predicting the Marshall Parameters of Stone Matrix Asphalt." Applied Sciences 9, no. 15: 3172.
This study is devoted to the modeling and simulation of uncertainties in the constitutive elastic properties of material constituting a circular column under axial compression. To this aim, a probabilistic model dedicated to the construction of positive-definite random elasticity matrices was first used, involving two stochastic parameters: the mean value and a dispersion parameter. In order to compute the nonlinear effects between load and lateral deflection for the buckling problem of the column, a finite element framework combining a Newton-Raphson solver was developed. The finite element tool was validated by comparing the as-obtained critical buckling loads with those from Euler’s formula at zero-fluctuation of the elasticity matrix. Three levels of fluctuations of material uncertainties were then propagated through the validated finite element tool using the probabilistic method as a stochastic solver. Results showed that uncertain material properties considerably influenced the buckling behavior of columns under axial loading. The coefficient of variation of a critical buckling load over 500 realizations were 15.477%, 26.713% and 41.555% when applying dispersion parameters of 0.3, 0.5 and 0.7, respectively. The 95% confidence intervals of column buckling response were finally given. The methodology of modeling presented in this paper is a potential candidate for accounting material uncertainties with some instabilities of structural elements under compression.
Hai-Bang Ly; Christophe Desceliers; Lu Minh Le; Tien-Thinh Le; Binh Thai Pham; Long Nguyen-Ngoc; Van Thuan Doan; Minh Le. Quantification of Uncertainties on the Critical Buckling Load of Columns under Axial Compression with Uncertain Random Materials. Materials 2019, 12, 1828 .
AMA StyleHai-Bang Ly, Christophe Desceliers, Lu Minh Le, Tien-Thinh Le, Binh Thai Pham, Long Nguyen-Ngoc, Van Thuan Doan, Minh Le. Quantification of Uncertainties on the Critical Buckling Load of Columns under Axial Compression with Uncertain Random Materials. Materials. 2019; 12 (11):1828.
Chicago/Turabian StyleHai-Bang Ly; Christophe Desceliers; Lu Minh Le; Tien-Thinh Le; Binh Thai Pham; Long Nguyen-Ngoc; Van Thuan Doan; Minh Le. 2019. "Quantification of Uncertainties on the Critical Buckling Load of Columns under Axial Compression with Uncertain Random Materials." Materials 12, no. 11: 1828.
The main aim of this study is to develop different hybrid artificial intelligence (AI) approaches, such as an adaptive neuro-fuzzy inference system (ANFIS) and two ANFISs optimized by metaheuristic techniques, namely simulated annealing (SA) and biogeography-based optimization (BBO) for predicting the critical buckling load of structural members under compression, taking into account the influence of initial geometric imperfections. With this aim, the existing results of compression tests on steel columns were collected and used as a dataset. Eleven input parameters, representing the slenderness ratios and initial geometric imperfections, were considered. The predicted target was the critical buckling load of columns. Statistical criteria, namely the correlation coefficient (R), the root mean squared error (RMSE), and the mean absolute error (MAE) were used to evaluate and validate the three proposed AI models. The results showed that SA and BBO were able to improve the prediction performance of the original ANFIS. Excellent results using the BBO optimization technique were achieved (i.e., an increase in R by 7.15%, RMSE by 40.48%, and MAE by 38.45%), and those using the SA technique were not much different (i.e., an increase in R by 5.03%, RMSE by 26.68%, and MAE by 20.40%). Finally, sensitivity analysis was performed, and the most important imperfections affecting column buckling capacity was found to be the initial in-plane loading eccentricity at the top and bottom ends of the columns. The methodology and the developed AI models herein could pave the way to establishing an advanced approach to forecasting damages of columns under compression.
Hai-Bang Ly; Lu Minh Le; Huan Thanh Duong; Thong Chung Nguyen; Tuan Anh Pham; Tien-Thinh Le; Vuong Minh Le; Long Nguyen-Ngoc; Binh Thai Pham. Hybrid Artificial Intelligence Approaches for Predicting Critical Buckling Load of Structural Members under Compression Considering the Influence of Initial Geometric Imperfections. Applied Sciences 2019, 9, 2258 .
AMA StyleHai-Bang Ly, Lu Minh Le, Huan Thanh Duong, Thong Chung Nguyen, Tuan Anh Pham, Tien-Thinh Le, Vuong Minh Le, Long Nguyen-Ngoc, Binh Thai Pham. Hybrid Artificial Intelligence Approaches for Predicting Critical Buckling Load of Structural Members under Compression Considering the Influence of Initial Geometric Imperfections. Applied Sciences. 2019; 9 (11):2258.
Chicago/Turabian StyleHai-Bang Ly; Lu Minh Le; Huan Thanh Duong; Thong Chung Nguyen; Tuan Anh Pham; Tien-Thinh Le; Vuong Minh Le; Long Nguyen-Ngoc; Binh Thai Pham. 2019. "Hybrid Artificial Intelligence Approaches for Predicting Critical Buckling Load of Structural Members under Compression Considering the Influence of Initial Geometric Imperfections." Applied Sciences 9, no. 11: 2258.
This study aims to investigate the prediction of critical buckling load of steel columns using two hybrid Artificial Intelligence (AI) models such as Adaptive Neuro-Fuzzy Inference System optimized by Genetic Algorithm (ANFIS-GA) and Adaptive Neuro-Fuzzy Inference System optimized by Particle Swarm Optimization (ANFIS-PSO). For this purpose, a total number of 57 experimental buckling tests of novel high strength steel Y-section columns were collected from the available literature to generate the dataset for training and validating the two proposed AI models. Quality assessment criteria such as coefficient of determination (R2), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used to validate and evaluate the performance of the prediction models. Results showed that both ANFIS-GA and ANFIS-PSO had a strong ability in predicting the buckling load of steel columns, but ANFIS-PSO (R2 = 0.929, RMSE = 60.522 and MAE = 44.044) was slightly better than ANFIS-GA (R2 = 0.916, RMSE = 65.371 and MAE = 48.588). The two models were also robust even with the presence of input variability, as investigated via Monte Carlo simulations. This study showed that the hybrid AI techniques could help constructing an efficient numerical tool for buckling analysis.
Lu Minh Le; Hai-Bang Ly; Binh Thai Pham; Vuong Minh Le; Tuan Anh Pham; Duy-Hung Nguyen; Xuan-Tuan Tran; Tien-Thinh Le. Hybrid Artificial Intelligence Approaches for Predicting Buckling Damage of Steel Columns Under Axial Compression. Materials 2019, 12, 1670 .
AMA StyleLu Minh Le, Hai-Bang Ly, Binh Thai Pham, Vuong Minh Le, Tuan Anh Pham, Duy-Hung Nguyen, Xuan-Tuan Tran, Tien-Thinh Le. Hybrid Artificial Intelligence Approaches for Predicting Buckling Damage of Steel Columns Under Axial Compression. Materials. 2019; 12 (10):1670.
Chicago/Turabian StyleLu Minh Le; Hai-Bang Ly; Binh Thai Pham; Vuong Minh Le; Tuan Anh Pham; Duy-Hung Nguyen; Xuan-Tuan Tran; Tien-Thinh Le. 2019. "Hybrid Artificial Intelligence Approaches for Predicting Buckling Damage of Steel Columns Under Axial Compression." Materials 12, no. 10: 1670.
The presence of defects like gas bubble in fabricated parts is inherent in the selective laser sintering process and the prediction of bubble shrinkage dynamics is crucial. In this paper, two artificial intelligence (AI) models based on Decision Trees algorithm were constructed in order to predict bubble dissolution time, namely the Ensemble Bagged Trees (EDT Bagged) and Ensemble Boosted Trees (EDT Boosted). A metadata including 68644 data were generated with the help of our previously developed numerical tool. The AI models used the initial bubble size, external domain size, diffusion coefficient, surface tension, viscosity, initial concentration, and chamber pressure as input parameters, whereas bubble dissolution time was considered as output variable. Evaluation of the models' performance was achieved by criteria such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and coefficient of determination (R2). The results showed that EDT Bagged outperformed EDT Boosted. Sensitivity analysis was then conducted thanks to the Monte Carlo approach and it was found that three most important inputs for the problem were the diffusion coefficient, initial concentration, and bubble initial size. This study might help in quick prediction of bubble dissolution time to improve the production quality from industry.
Hai-Bang Ly; Eric Monteiro; Tien-Thinh Le; Vuong Minh Le; Morgan Dal; Gilles Regnier; Binh Thai Pham. Prediction and Sensitivity Analysis of Bubble Dissolution Time in 3D Selective Laser Sintering Using Ensemble Decision Trees. Materials 2019, 12, 1544 .
AMA StyleHai-Bang Ly, Eric Monteiro, Tien-Thinh Le, Vuong Minh Le, Morgan Dal, Gilles Regnier, Binh Thai Pham. Prediction and Sensitivity Analysis of Bubble Dissolution Time in 3D Selective Laser Sintering Using Ensemble Decision Trees. Materials. 2019; 12 (9):1544.
Chicago/Turabian StyleHai-Bang Ly; Eric Monteiro; Tien-Thinh Le; Vuong Minh Le; Morgan Dal; Gilles Regnier; Binh Thai Pham. 2019. "Prediction and Sensitivity Analysis of Bubble Dissolution Time in 3D Selective Laser Sintering Using Ensemble Decision Trees." Materials 12, no. 9: 1544.