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Foamed concrete (FC) shows advantageous applications in civil engineering, such as reduction in dead loads, contribution to energy conservation, or decrease the construction phase labor cost. Compressive Strength is considered the most important factor in terms of FC mechanical properties. In recent years, Artificial Neural Network (ANN) is one of popular and effective machine learning models, which can be used to accurately predict the FCCS. However, ANN’s structure and parameters are normally chosen by experience. In this study, therefore, the objective is to use particle swarm optimization (PSO) metaheuristic optimization (one of the effective soft computing techniques) to optimize the parameters and structure of a Levenberg–Marquardt-based Artificial Neural Network (LMA-ANN) for accurate and quick prediction of the FCCS. A total of 375 data of experiments on FC gathered from the available literature were used to generate the training and testing datasets. Various validation criteria such as mean absolute error, root mean square error, and correlation coefficient (R) were used for the validation of the models. The results showed that the PSO-LMA-ANN algorithm is a highly efficient predictor of the FCCS, achieving the highest value of R up to 0.959 with the optimized [5-7-6-1] structure. An interpretation of the mixture components and the FCCS using Partial Dependence Plots was also performed to understand the effect of each input on the FCCS. The dry density was the most important parameter for the prediction of FCCS, followed by the water/cement ratio, foam volume, sand/cement ratio, and the testing age. The results of the present work might help in accurate and quick prediction of the FCCS and the design optimization process of the FC.
Hai-Bang Ly; May Huu Nguyen; Binh Thai Pham. Metaheuristic optimization of Levenberg–Marquardt-based artificial neural network using particle swarm optimization for prediction of foamed concrete compressive strength. Neural Computing and Applications 2021, 1 -21.
AMA StyleHai-Bang Ly, May Huu Nguyen, Binh Thai Pham. Metaheuristic optimization of Levenberg–Marquardt-based artificial neural network using particle swarm optimization for prediction of foamed concrete compressive strength. Neural Computing and Applications. 2021; ():1-21.
Chicago/Turabian StyleHai-Bang Ly; May Huu Nguyen; Binh Thai Pham. 2021. "Metaheuristic optimization of Levenberg–Marquardt-based artificial neural network using particle swarm optimization for prediction of foamed concrete compressive strength." Neural Computing and Applications , no. : 1-21.
This paper presents an innovative development process of a Deep Neural Network model to predict the compressive strength of rubber concrete. To this goal, a rubber concrete database is carefully constructed, incorporating a set of binder, aggregate, and other related concrete variables as input parameters, whereas the compressive strength is considered as output. The development of the DNN model includes extensive analysis of the number of hidden layers and the neurons in each layer, combining with a statistical investigation of the models' prediction outputs. The results show that the DNN model outperforms other neural network structures according to several well-known performance indices, such as coefficient of determination, root mean square error, and mean absolute error. The proposed DNN model also exhibits higher prediction accuracy than previously published results, using different machine learning algorithms in the literature. A sensitivity analysis using partial dependence plots is performed within the DNN algorithm in order to achieve an in-depth examination of the influence of each single input variable on the predicted compressive strength of rubber concrete. Finally, the possibility of using other input variables is given to pave the way for applications in regular, high strength, or light-weight foamed concrete containing rubber particles.
Hai-Bang Ly; Thuy-Anh Nguyen; Hai-Van Thi Mai; Van Quan Tran. Development of deep neural network model to predict the compressive strength of rubber concrete. Construction and Building Materials 2021, 301, 124081 .
AMA StyleHai-Bang Ly, Thuy-Anh Nguyen, Hai-Van Thi Mai, Van Quan Tran. Development of deep neural network model to predict the compressive strength of rubber concrete. Construction and Building Materials. 2021; 301 ():124081.
Chicago/Turabian StyleHai-Bang Ly; Thuy-Anh Nguyen; Hai-Van Thi Mai; Van Quan Tran. 2021. "Development of deep neural network model to predict the compressive strength of rubber concrete." Construction and Building Materials 301, no. : 124081.
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.
Hai-Van Thi Mai; Thuy-Anh Nguyen; Hai-Bang Ly; Van Quan Tran. Investigation of ANN Model Containing One Hidden Layer for Predicting Compressive Strength of Concrete with Blast-Furnace Slag and Fly Ash. Advances in Materials Science and Engineering 2021, 2021, 1 -17.
AMA StyleHai-Van Thi Mai, Thuy-Anh Nguyen, Hai-Bang Ly, Van Quan Tran. Investigation of ANN Model Containing One Hidden Layer for Predicting Compressive Strength of Concrete with Blast-Furnace Slag and Fly Ash. Advances in Materials Science and Engineering. 2021; 2021 ():1-17.
Chicago/Turabian StyleHai-Van Thi Mai; Thuy-Anh Nguyen; Hai-Bang Ly; Van Quan Tran. 2021. "Investigation of ANN Model Containing One Hidden Layer for Predicting Compressive Strength of Concrete with Blast-Furnace Slag and Fly Ash." Advances in Materials Science and Engineering 2021, no. : 1-17.
Castellated steel beams (CSB) are an attractive option for the steel construction industry thanks to outstanding advantages, such as the ability to exceed large span, lightweight, and allowing flexible arrangement of the technical pipes through beams. In addition, the complex localized and global failures characterizing these structural members have led researchers to focus on the development of efficient design guidelines. This paper aims to propose an artificial neural network (ANN) model with optimal architecture to predict the load-carrying capacity of CSB with a scheme of the simple beam bearing load located at the center of the beam. The ANN model is built with 9 input variables, which are essential parameters equivalent to the geometrical properties and mechanical properties of the material, such as the overall depth of the castellated beam, the vertical projection of the inclined side of the opening, the web thickness, the flange width, the flange thickness, the width of web post at middepth, the horizontal projection of inclined side of the opening, the minimum web yield stress, and the minimum flange yield stress. The output variable is the load-carrying capacity of the CSB. With the optimal ANN architecture [9-1-1] containing one hidden layer, the performance of the ANN model is evaluated based on statistical criteria such as R2, RMSE, and MAE. The results show that the optimal ANN model is a highly effective predictor of the load-carrying capacity of the CSB with the best value of R2 = 0.989, RMSE = 3.328, and MAE = 2.620 for the testing part. The ANN model seems to be the best algorithm of machine learning for predicting the CSB load-carrying capacity.
Thuy-Anh Nguyen; Hai-Bang Ly; Van Quan Tran. Investigation of ANN Architecture for Predicting Load-Carrying Capacity of Castellated Steel Beams. Complexity 2021, 2021, 1 -14.
AMA StyleThuy-Anh Nguyen, Hai-Bang Ly, Van Quan Tran. Investigation of ANN Architecture for Predicting Load-Carrying Capacity of Castellated Steel Beams. Complexity. 2021; 2021 ():1-14.
Chicago/Turabian StyleThuy-Anh Nguyen; Hai-Bang Ly; Van Quan Tran. 2021. "Investigation of ANN Architecture for Predicting Load-Carrying Capacity of Castellated Steel Beams." Complexity 2021, no. : 1-14.
This study aims to predict the shear strength of reinforced concrete (RC) deep beams based on artificial neural network (ANN) using four training algorithms, namely, Levenberg–Marquardt (ANN-LM), quasi-Newton method (ANN-QN), conjugate gradient (ANN-CG), and gradient descent (ANN-GD). A database containing 106 results of RC deep beam shear strength tests is collected and used to investigate the performance of the four proposed algorithms. The ANN training phase uses 70% of data, randomly taken from the collected dataset, whereas the remaining 30% of data are used for the algorithms’ evaluation process. The ANN structure consists of an input layer with 9 neurons corresponding to 9 input parameters, a hidden layer of 10 neurons, and an output layer with 1 neuron representing the shear strength of RC deep beams. The performance evaluation of the models is performed using statistical criteria, including the correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results show that the ANN-CG model has the best prediction performance with R = 0.992, RMSE = 14.02, MAE = 14.24, and MAPE = 6.84. The results of this study show that the ANN-CG model can accurately predict the shear strength of RC deep beams, representing a promising and useful alternative design solution for structural engineers.
Thuy-Anh Nguyen; Hai-Bang Ly; Hai-Van Thi Mai; Van Quan Tran. On the Training Algorithms for Artificial Neural Network in Predicting the Shear Strength of Deep Beams. Complexity 2021, 2021, 1 -18.
AMA StyleThuy-Anh Nguyen, Hai-Bang Ly, Hai-Van Thi Mai, Van Quan Tran. On the Training Algorithms for Artificial Neural Network in Predicting the Shear Strength of Deep Beams. Complexity. 2021; 2021 ():1-18.
Chicago/Turabian StyleThuy-Anh Nguyen; Hai-Bang Ly; Hai-Van Thi Mai; Van Quan Tran. 2021. "On the Training Algorithms for Artificial Neural Network in Predicting the Shear Strength of Deep Beams." Complexity 2021, no. : 1-18.
Improvement of compressive strength prediction accuracy for concrete is crucial and is considered a challenging task to reduce costly experiments and time. Particularly, the determination of compressive strength of concrete using ground granulated blast furnace slag (GGBFS) is more difficult due to the complexity of the composition mix design. In this paper, an approach using random forest (RF), which is one of the powerful machine learning algorithms, is proposed for predicting the compressive strength of concrete using GGBFS. The RF model is first evaluated to determine the best architecture, which constitutes 500 growth trees and leaf size of 1. In the next step, the evaluation of the model is conducted over 500 simulations considering the effect of random sampling. Finally, the best prediction results are given in function of statistical measures such as the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), respectively, which are 0.9729, 4.9585, and 3.9423 for the testing dataset. The results show that the RF algorithm is an excellent predictor and practically used for engineers to reduce experimental cost.
Hai-Van Thi Mai; Thuy-Anh Nguyen; Hai-Bang Ly; Van Quan Tran. Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model. Advances in Civil Engineering 2021, 2021, 1 -12.
AMA StyleHai-Van Thi Mai, Thuy-Anh Nguyen, Hai-Bang Ly, Van Quan Tran. Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model. Advances in Civil Engineering. 2021; 2021 ():1-12.
Chicago/Turabian StyleHai-Van Thi Mai; Thuy-Anh Nguyen; Hai-Bang Ly; Van Quan Tran. 2021. "Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model." Advances in Civil Engineering 2021, no. : 1-12.
Accurate measurement of the critical buckling stress is crucial in the entire field of structural engineering. In this paper, the critical buckling load of Y-shaped cross-section steel columns was predicted by the Artificial Neural Network (ANN) using the Levenberg-Marquardt algorithm. The results of 57 buckling tests were used to generate the training and testing datasets. Seven input variables were considered, including the column length, column width, steel equal angles thickness, the width and thickness of the welded steel plate, and the total deviations following the Ox and Oy directions. The output was the critical buckling load of the columns. The accuracy assessment criteria used to evaluate the model were the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE). The selection of an appropriate structure of ANN was first addressed, followed by two investigations on the highest accuracy models. The first one consisted of the ANN model that gave the lowest values of MAE = 40.0835 and RMSE = 30.6669, whereas the second one gave the highest value of R = 0.98488. The results revealed that taking MAE and RMSE for model assessment was more accurate and reasonable than taking the R criterion. The RMSE and MAE criteria should be used in priority, compared with the correlation coefficient.
Thuy-Anh Nguyen; Hai-Bang Ly; Hai-Van Thi Mai; Van Quan Tran. Using ANN to Estimate the Critical Buckling Load of Y Shaped Cross-Section Steel Columns. Scientific Programming 2021, 2021, 1 -8.
AMA StyleThuy-Anh Nguyen, Hai-Bang Ly, Hai-Van Thi Mai, Van Quan Tran. Using ANN to Estimate the Critical Buckling Load of Y Shaped Cross-Section Steel Columns. Scientific Programming. 2021; 2021 ():1-8.
Chicago/Turabian StyleThuy-Anh Nguyen; Hai-Bang Ly; Hai-Van Thi Mai; Van Quan Tran. 2021. "Using ANN to Estimate the Critical Buckling Load of Y Shaped Cross-Section Steel Columns." Scientific Programming 2021, no. : 1-8.
Understanding the radon dispersion released from this mine are important targets as radon dispersion is used to assess radiological hazard to human. In this paper, the main objective is to develop and optimize a machine learning model namely Artificial Neural Network (ANN) for quick and accurate prediction of radon dispersion released from Sinquyen mine, Vietnam. For this purpose, a total of million data collected from the study area, which includes input variables (the gamma data of uranium concentration with 3x3m grid net survey inside mine, 21 of CR-39 detectors inside dwellings surrounding mine, and gamma dose at 1 m from ground surface data) and an output variable (radon dispersion) were used for training and validating the predictive model. Various validation methods namely coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) were used. In addition, Partial dependence plots (PDP) was used to evaluate the effect of each input variable on the predictive results of output variable. The results show that ANN performed well for prediction of radon dispersion, with low values of error (i.e., R2=0.9415, RMSE=0.0589, and MAE=0.0203 for the testing dataset). The increase of number of hidden layers in ANN structure leads the increase of accuracy of the predictive results. The sensitivity results show that all input variables govern the dispersion radon activity with different amplitudes and fitted with different equations but the gamma dose is the most influenced and important variable in comparison with strike, distance and uranium concentration variables for prediction of radon dispersion.
Hao Duong Van; Hai-Bang Ly; Trinh Dinh Huan; Son Nguyen Thai; Binh Thai Pham. Development of Artificial Neural Network for Prediction of Radon Dispersion Released from Sinquyen Mine, Vietnam. Environmental Pollution 2021, 282, 116973 .
AMA StyleHao Duong Van, Hai-Bang Ly, Trinh Dinh Huan, Son Nguyen Thai, Binh Thai Pham. Development of Artificial Neural Network for Prediction of Radon Dispersion Released from Sinquyen Mine, Vietnam. Environmental Pollution. 2021; 282 ():116973.
Chicago/Turabian StyleHao Duong Van; Hai-Bang Ly; Trinh Dinh Huan; Son Nguyen Thai; Binh Thai Pham. 2021. "Development of Artificial Neural Network for Prediction of Radon Dispersion Released from Sinquyen Mine, Vietnam." Environmental Pollution 282, no. : 116973.
Soil cohesion (C) is one of the critical soil properties and is closely related to basic soil properties such as particle size distribution, pore size, and shear strength. Hence, it is mainly determined by experimental methods. However, the experimental methods are often time-consuming and costly. Therefore, developing an alternative approach based on machine learning (ML) techniques to solve this problem is highly recommended. In this study, machine learning models, namely, support vector machine (SVM), Gaussian regression process (GPR), and random forest (RF), were built based on a data set of 145 soil samples collected from the Da Nang-Quang Ngai expressway project, Vietnam. The database also includes six input parameters, that is, clay content, moisture content, liquid limit, plastic limit, specific gravity, and void ratio. The performance of the model was assessed by three statistical criteria, namely, the correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE). The results demonstrated that the proposed RF model could accurately predict soil cohesion with high accuracy (R = 0.891) and low error (RMSE = 3.323 and MAE = 2.511), and its predictive capability is better than SVM and GPR. Therefore, the RF model can be used as a cost-effective approach in predicting soil cohesion forces used in the design and inspection of constructions.
Hai-Bang Ly; Thuy-Anh Nguyen; Binh Thai Pham. Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach. Advances in Civil Engineering 2021, 2021, 1 -14.
AMA StyleHai-Bang Ly, Thuy-Anh Nguyen, Binh Thai Pham. Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach. Advances in Civil Engineering. 2021; 2021 ():1-14.
Chicago/Turabian StyleHai-Bang Ly; Thuy-Anh Nguyen; Binh Thai Pham. 2021. "Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach." Advances in Civil Engineering 2021, no. : 1-14.
The cemented paste backfill (CPB) technology has matured as a promising way for tailings recycling in the mining industry. Nevertheless, the current CPB design requires a large number of lab experiments to determine the unconfined compressive strength (UCS) of CPB. The utilisation of artificial intelligence (AI) prediction to reduce the lab experiments has been attempted without reaching its full potential. In this study, a hybrid model based on adaptive neuro fuzzy inference system (ANFIS) and artificial bee colony (ABC) was used for performance improvement. The ANFIS was used to learn the relationship between inputs and UCS while the ABC algorithm was used to tune the parameters of the initial ANFIS. The convergence of the prediction performance was tested using Monte Carlo simulations. A comparison between this study and previous studies was conducted and a sensitivity analysis was performed to investigate the importance of input variables. The results show that the ABC algorithm was efficient in tunning parameters of the ANFIS model. The representative ANFIS-ABC model yielded an R2 of 0.967 on the training part and an R2 of 0.976 on the testing part, indicating an excellent prediction. 310 Monte Carlo simulations were needed before a stable performance was achieved for all quality assessment criteria. The proposed hybrid model outperformed AI models in the literature (R2 was increased from 0.83/0.958/0.86 to 0.976 on the testing set). Solid content, cement-tailings ratio and curing time were found to be the most significant input parameters for the UCS of CPB.
Chongchong Qi; Hai-Bang Ly; Lu Minh Le; Xingyu Yang; Li Guo; Binh Thai Pham. Improved strength prediction of cemented paste backfill using a novel model based on adaptive neuro fuzzy inference system and artificial bee colony. Construction and Building Materials 2021, 284, 122857 .
AMA StyleChongchong Qi, Hai-Bang Ly, Lu Minh Le, Xingyu Yang, Li Guo, Binh Thai Pham. Improved strength prediction of cemented paste backfill using a novel model based on adaptive neuro fuzzy inference system and artificial bee colony. Construction and Building Materials. 2021; 284 ():122857.
Chicago/Turabian StyleChongchong Qi; Hai-Bang Ly; Lu Minh Le; Xingyu Yang; Li Guo; Binh Thai Pham. 2021. "Improved strength prediction of cemented paste backfill using a novel model based on adaptive neuro fuzzy inference system and artificial bee colony." Construction and Building Materials 284, no. : 122857.
The main objective of this study is to evaluate and compare the performance of different machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme Learning Machine (ELM), and Boosting Trees (Boosted) algorithms, considering the influence of various training to testing ratios in predicting the soil shear strength, one of the most critical geotechnical engineering properties in civil engineering design and construction. For this aim, a database of 538 soil samples collected from the Long Phu 1 power plant project, Vietnam, was utilized to generate the datasets for the modeling process. Different ratios (i.e., 10/90, 20/80, 30/70, 40/60, 50/50, 60/40, 70/30, 80/20, and 90/10) were used to divide the datasets into the training and testing datasets for the performance assessment of models. Popular statistical indicators, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R), were employed to evaluate the predictive capability of the models under different training and testing ratios. Besides, Monte Carlo simulation was simultaneously carried out to evaluate the performance of the proposed models, taking into account the random sampling effect. The results showed that although all three ML models performed well, the ANN was the most accurate and statistically stable model after 1000 Monte Carlo simulations (Mean R = 0.9348) compared with other models such as Boosted (Mean R = 0.9192) and ELM (Mean R = 0.8703). Investigation on the performance of the models showed that the predictive capability of the ML models was greatly affected by the training/testing ratios, where the 70/30 one presented the best performance of the models. Concisely, the results presented herein showed an effective manner in selecting the appropriate ratios of datasets and the best ML model to predict the soil shear strength accurately, which would be helpful in the design and engineering phases of construction projects.
Quang Hung Nguyen; Hai-Bang Ly; Lanh Si Ho; Nadhir Al-Ansari; Hiep Van Le; Van Quan Tran; Indra Prakash; Binh Thai Pham. Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil. Mathematical Problems in Engineering 2021, 2021, 1 -15.
AMA StyleQuang Hung Nguyen, Hai-Bang Ly, Lanh Si Ho, Nadhir Al-Ansari, Hiep Van Le, Van Quan Tran, Indra Prakash, Binh Thai Pham. Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil. Mathematical Problems in Engineering. 2021; 2021 ():1-15.
Chicago/Turabian StyleQuang Hung Nguyen; Hai-Bang Ly; Lanh Si Ho; Nadhir Al-Ansari; Hiep Van Le; Van Quan Tran; Indra Prakash; Binh Thai Pham. 2021. "Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil." Mathematical Problems in Engineering 2021, no. : 1-15.
Estimation of pressure drops of fresh cemented paste backfill slurry is a novel idea with great potentials. This paper presented a hybrid machine learning (ML) method for improved pressure drops estimation using a combination of artificial neural network and differential evolution. A comprehensive parametric study was conducted on training dataset size (Nsize), ML methods, and Monte Carlo random sampling. Moreover, dependent analysis of pressure drops to each influencing variable was performed. The results indicate that 300 Monte Carlo realizations were sufficient for the converged and reliable results. The optimum Nsize was determined to be 70%, and the proposed hybrid method outperformed six individual ML methods. The estimation performance has been significantly improved compared to the methods used in the literature (R2 increased from 0.83 to 0.95 on the testing dataset). Solids content, inlet velocity, SiO2, CaO, and Fe2O3 were determined to be the most significant variables for pressure drops.
Chongchong Qi; Li Guo; Hai-Bang Ly; Hiep Van Le; Binh Thai Pham. Improving pressure drops estimation of fresh cemented paste backfill slurry using a hybrid machine learning method. Minerals Engineering 2021, 163, 106790 .
AMA StyleChongchong Qi, Li Guo, Hai-Bang Ly, Hiep Van Le, Binh Thai Pham. Improving pressure drops estimation of fresh cemented paste backfill slurry using a hybrid machine learning method. Minerals Engineering. 2021; 163 ():106790.
Chicago/Turabian StyleChongchong Qi; Li Guo; Hai-Bang Ly; Hiep Van Le; Binh Thai Pham. 2021. "Improving pressure drops estimation of fresh cemented paste backfill slurry using a hybrid machine learning method." Minerals Engineering 163, no. : 106790.
The authors would like to make the following corrections to the published paper
Hai-Bang Ly; Tien-Thinh Le; Huong-Lan Vu; Van Tran; Lu Le; Binh Pham. Erratum: Ly, H.-B., et al. Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams. Sustainability2020, 12, 2709. Sustainability 2020, 12, 7029 .
AMA StyleHai-Bang Ly, Tien-Thinh Le, Huong-Lan Vu, Van Tran, Lu Le, Binh Pham. Erratum: Ly, H.-B., et al. Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams. Sustainability2020, 12, 2709. Sustainability. 2020; 12 (17):7029.
Chicago/Turabian StyleHai-Bang Ly; Tien-Thinh Le; Huong-Lan Vu; Van Tran; Lu Le; Binh Pham. 2020. "Erratum: Ly, H.-B., et al. Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams. Sustainability2020, 12, 2709." Sustainability 12, no. 17: 7029.
In this study, a novel hybrid surrogate machine learning model based on a feedforward neural network (FNN) and one step secant algorithm (OSS) was developed to predict the load-bearing capacity of concrete-filled steel tube columns (CFST), whereas the OSS was used to optimize the weights and bias of the FNN for developing a hybrid model (FNN-OSS). For achieving this goal, an experimental database containing 422 instances was firstly gathered from the literature and used to develop the FNN-OSS algorithm. The input variables in the database contained the geometrical characteristics of CFST columns, and the mechanical properties of two CFST constituent materials, i.e., steel and concrete. Thereafter, the selection of the appropriate parameters of FNN-OSS was performed and evaluated by common statistical measurements, for instance, the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). In the next step, the prediction capability of the best FNN-OSS structure was evaluated in both global and local analyses, showing an excellent agreement between actual and predicted values of the load-bearing capacity. Finally, an in-depth investigation of the performance and limitations of FNN-OSS was conducted from a structural engineering point of view. The results confirmed the effectiveness of the FNN-OSS as a robust algorithm for the prediction of the CFST load-bearing capacity.
Quang Nguyen; Hai-Bang Ly; Van Tran; Thuy-Anh Nguyen; Viet-Hung Phan; Tien-Thinh Le; Binh Pham. A Novel Hybrid Model Based on a Feedforward Neural Network and One Step Secant Algorithm for Prediction of Load-Bearing Capacity of Rectangular Concrete-Filled Steel Tube Columns. Molecules 2020, 25, 3486 .
AMA StyleQuang Nguyen, Hai-Bang Ly, Van Tran, Thuy-Anh Nguyen, Viet-Hung Phan, Tien-Thinh Le, Binh Pham. A Novel Hybrid Model Based on a Feedforward Neural Network and One Step Secant Algorithm for Prediction of Load-Bearing Capacity of Rectangular Concrete-Filled Steel Tube Columns. Molecules. 2020; 25 (15):3486.
Chicago/Turabian StyleQuang Nguyen; Hai-Bang Ly; Van Tran; Thuy-Anh Nguyen; Viet-Hung Phan; Tien-Thinh Le; Binh Pham. 2020. "A Novel Hybrid Model Based on a Feedforward Neural Network and One Step Secant Algorithm for Prediction of Load-Bearing Capacity of Rectangular Concrete-Filled Steel Tube Columns." Molecules 25, no. 15: 3486.
Stone Mastic Asphalt (SMA) is a tough, stable, rut-resistant mixture that takes advantage of the stone-to-stone contact to provide strength and durability for the material. Besides, the warm mix asphalt (WMA) technology allows reducing emissions and energy consumption by reducing the production temperature by 30–50 °C, compared to conventional hot mix asphalt technology (HMA). The dynamic modulus |E*| has been acknowledged as a vital material property in the mechanistic-empirical design and analysis and further reflects the strains and displacements of such layered pavement structures. The objective of this study is twofold, aiming at favoring the potential use of SMA with WMA technique. To this aim, first, laboratory tests were conducted to compare the performance of SMA and HMA through the dynamic modulus. Second, an advanced hybrid artificial intelligence technique to accurately predict the dynamic modulus of asphalt mixtures was developed. This hybrid model (ANN-TLBO) was based on an Artificial Neural Network (ANN) algorithm and Teaching Learning Based Optimization (TLBO) technique. A database containing the as-obtained experimental tests (96 data) was used for the development and assessment of the ANN-TLBO model. The experimental results showed that SMA mixtures exhibited higher values of the dynamic modulus |E*| than HMA, and the WMA technology increased the dynamic modulus values compared with the hot technology. Furthermore, the proposed hybrid algorithm could successfully predict the dynamic modulus with remarkable values of R2 of 0.989 and 0.985 for the training and testing datasets, respectively. Lastly, the effects of temperature and frequency on the dynamic modulus were evaluated and discussed.
Thanh-Hai Le; Hoang-Long Nguyen; Binh Pham; May Nguyen; Cao-Thang Pham; Ngoc-Lan Nguyen; Tien-Thinh Le; Hai-Bang Ly. Artificial Intelligence-Based Model for the Prediction of Dynamic Modulus of Stone Mastic Asphalt. Applied Sciences 2020, 10, 5242 .
AMA StyleThanh-Hai Le, Hoang-Long Nguyen, Binh Pham, May Nguyen, Cao-Thang Pham, Ngoc-Lan Nguyen, Tien-Thinh Le, Hai-Bang Ly. Artificial Intelligence-Based Model for the Prediction of Dynamic Modulus of Stone Mastic Asphalt. Applied Sciences. 2020; 10 (15):5242.
Chicago/Turabian StyleThanh-Hai Le; Hoang-Long Nguyen; Binh Pham; May Nguyen; Cao-Thang Pham; Ngoc-Lan Nguyen; Tien-Thinh Le; Hai-Bang Ly. 2020. "Artificial Intelligence-Based Model for the Prediction of Dynamic Modulus of Stone Mastic Asphalt." Applied Sciences 10, no. 15: 5242.
The main objective of the present work is to estimate the load-carrying capacity of concrete-filled steel tubes (CFST) under axial compression using hybrid artificial intelligence (AI) algorithms. In particular, the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic optimization methods, such as the biogeography-based optimization (ANFIS-BBO), the particle swarm optimization (ANFIS-PSO), and the genetic algorithm (ANFIS-GA), have been employed taking into account the variability of input parameters. Commonly used statistical criteria, such as the coefficient of determination (R2), the a20-index, and the root mean squared error (RMSE), were utilized to evaluate and compare the effectiveness of the proposed AI models. The Monte Carlo approach was used to propagate the variability in the input space to the predicted output. The results showed that the ANFIS system, optimized by PSO, was the most effective and robust model with respect to three considered criteria (a20-index = 0.881, R2 = 0.942 and RMSE = 185.631). Sensitivity analysis was performed, indicating that the minor axis length and thickness of the steel tube exhibited the highest contribution to the axial compression load-carrying capacity of the CFST.
Hai-Bang Ly; Binh Thai Pham; Lu Minh Le; Tien-Thinh Le; Vuong Minh Le; Panagiotis G. Asteris. Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models. Neural Computing and Applications 2020, 33, 3437 -3458.
AMA StyleHai-Bang Ly, Binh Thai Pham, Lu Minh Le, Tien-Thinh Le, Vuong Minh Le, Panagiotis G. Asteris. Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models. Neural Computing and Applications. 2020; 33 (8):3437-3458.
Chicago/Turabian StyleHai-Bang Ly; Binh Thai Pham; Lu Minh Le; Tien-Thinh Le; Vuong Minh Le; Panagiotis G. Asteris. 2020. "Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models." Neural Computing and Applications 33, no. 8: 3437-3458.
Warm mix asphalt (WMA) technology, taking advantage of reclaimed asphalt pavements, has gained increasing attention from the scientific community. The determination of technical specifications of such a type of asphalt concrete is crucial for pavement design, in which the asphalt concrete dynamic modulus (E*) of elasticity is amongst the most critical parameters. However, the latter could only be determined by complicated, costly, and time-consuming experiments. This paper presents an alternative cost-effective approach to determine the dynamic elastic modulus (E*) of WMA based on various machine learning-based algorithms, namely the artificial neural network (ANN), support vector machine (SVM), Gaussian process regression (GPR), and ensemble boosted trees (Boosted). For this, a total of 300 samples were fabricated by warm mix asphalt technology. The mixtures were prepared with 0%, 20%, 30%, 40%, and 50% content of reclaimed asphalt pavement (RAP) and modified bitumen binder using Sasobit and Zycotherm additives. The dynamic elastic modulus tests were conducted by varying the temperature from 10 °C to 50 °C at different frequencies from 0.1 Hz to 25 Hz. Various common quantitative indications, such as root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) were used to validate and compare the prediction capability of different models. The results showed that machine learning models could accurately predict the dynamic elastic modulus of WMA using up to 50% RAP and fabricated by warm mix asphalt technology. Out of these models, the Boosted algorithm (R = 0.9956) was found as the best predictor compared with those obtained by ANN-LMN (R = 0.9954), SVM (R = 0.9654), and GPR (R= 0.9865). Thus, it could be concluded that Boosted is a promising cost-effective tool for the prediction of the dynamic elastic modulus (E*) of WMA. This study might help in reducing the cost of laboratory experiments for the determination of the dynamic modulus (E*).
Dong Dao; Ngoc-Lan Nguyen; Hai-Bang Ly; Binh Pham; Tien-Thinh Le. Cost-Effective Approaches Based on Machine Learning to Predict Dynamic Modulus of Warm Mix Asphalt with High Reclaimed Asphalt Pavement. Materials 2020, 13, 3272 .
AMA StyleDong Dao, Ngoc-Lan Nguyen, Hai-Bang Ly, Binh Pham, Tien-Thinh Le. Cost-Effective Approaches Based on Machine Learning to Predict Dynamic Modulus of Warm Mix Asphalt with High Reclaimed Asphalt Pavement. Materials. 2020; 13 (15):3272.
Chicago/Turabian StyleDong Dao; Ngoc-Lan Nguyen; Hai-Bang Ly; Binh Pham; Tien-Thinh Le. 2020. "Cost-Effective Approaches Based on Machine Learning to Predict Dynamic Modulus of Warm Mix Asphalt with High Reclaimed Asphalt Pavement." Materials 13, no. 15: 3272.
Coefficient of consolidation (Cv) is an important parameter in the designing of civil engineering structures founded on soil. Determination of the Cv in the laboratory is beset with complexity, therefore several attempts have been made to correlate it with the index properties of soil. In this paper, various advanced soft computing approaches namely Biogeography-Based Optimization based Artificial Neural Networks (ANN-BBO), Artificial Neural Networks (ANN), Adaptive Network based Fuzzy Inference System (ANFIS), and Support Vector Machines (SVM) were applied for quick and accurate prediction of the Cv of soft soil. For this, data of engineering properties of soil of Ha Noi–Hai Phong highway project of Vietnam was utilized as a case study for training and validating the models. Data pre-processing techniques namely correlation matrix and Principal Component Analysis (PCA) were applied in order to identify relevant variables for reducing data dimension while doing predictive analysis. Validation of the models was performed using statistical criteria namely Coefficient of determination (R2), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Monte Carlo simulation method was performed to estimate the robustness of the models. Comparison of results of these models indicated that all the studied models performed well but performance of the ANN-BBO model (R2 = 0.965, RMSE = 0.149, and MAE = 0.108) is the best in predicting the Cv of soil compared with other models such as ANFIS (R2 = 0.921, RMSE = 0.222, and MAE = 0.182), ANN (R2 = 0.922, RMSE = 0.302, and MAE = 0.178), and SVM (R2 = 0.949, RMSE = 0.199, and MAE = 0.112). Therefore, ANN-BBO can be used for better prediction of the Cv based on limited engineering parameters of soil.
Manh Duc Nguyen; Binh Thai Pham; Lanh Si Ho; Hai-Bang Ly; Tien-Thinh Le; Chongchong Qi; Vuong Minh Le; Lu Minh Le; Indra Prakash; Le Hoang Son; Dieu Tien Bui. Soft-computing techniques for prediction of soils consolidation coefficient. CATENA 2020, 195, 104802 .
AMA StyleManh Duc Nguyen, Binh Thai Pham, Lanh Si Ho, Hai-Bang Ly, Tien-Thinh Le, Chongchong Qi, Vuong Minh Le, Lu Minh Le, Indra Prakash, Le Hoang Son, Dieu Tien Bui. Soft-computing techniques for prediction of soils consolidation coefficient. CATENA. 2020; 195 ():104802.
Chicago/Turabian StyleManh Duc Nguyen; Binh Thai Pham; Lanh Si Ho; Hai-Bang Ly; Tien-Thinh Le; Chongchong Qi; Vuong Minh Le; Lu Minh Le; Indra Prakash; Le Hoang Son; Dieu Tien Bui. 2020. "Soft-computing techniques for prediction of soils consolidation coefficient." CATENA 195, no. : 104802.
The main objective of this study is to calibrate Discrete Element Modeling (DEM) input parameters for Vietnamese DT84 variety soybeans. For this purpose, the shape of the soybeans was firstly analyzed through digital images (captured by an imaging platform), which enabled the deviation of the shape of particles compared to a sphere through shape indicators to be quantified. Secondly, the physical characteristics of the soybeans (size distribution and gravimetric properties) and the coefficient of static friction between particles and material surfaces were measured by experimentation. The rest of the DEM input parameters were calibrated by combining selected particle flow experiments and corresponding DEM simulations, including bulk density cylinder, silo discharge and inclined cylinder. The final set of DEM input parameters was compared with previous research in the literature for other varieties of soybeans, showing strong correlation. This study proves that: (i) spherical particles can be used to model DT84 soybeans in DEM simulations and (ii) the equivalent diameter of the grains obtained from image analysis can be employed to approximate particle weight. The DEM input parameters of soybean obtained from this work could help enhance the design and development of seeders, by exploring the contact behavior between particle flow and machine parts.
Thiet Xuan Nguyen; Lu Minh Le; Thong Chung Nguyen; Nguyen Thi Hanh Nguyen; Tien-Thinh Le; Binh Thai Pham; Vuong Minh Le; Hai-Bang Ly. Characterization of soybeans and calibration of their DEM input parameters. Particulate Science and Technology 2020, 39, 530 -548.
AMA StyleThiet Xuan Nguyen, Lu Minh Le, Thong Chung Nguyen, Nguyen Thi Hanh Nguyen, Tien-Thinh Le, Binh Thai Pham, Vuong Minh Le, Hai-Bang Ly. Characterization of soybeans and calibration of their DEM input parameters. Particulate Science and Technology. 2020; 39 (5):530-548.
Chicago/Turabian StyleThiet Xuan Nguyen; Lu Minh Le; Thong Chung Nguyen; Nguyen Thi Hanh Nguyen; Tien-Thinh Le; Binh Thai Pham; Vuong Minh Le; Hai-Bang Ly. 2020. "Characterization of soybeans and calibration of their DEM input parameters." Particulate Science and Technology 39, no. 5: 530-548.
In this paper, the main objectives are to investigate and select the most suitable parameters used in particle swarm optimization (PSO), namely the number of rules (nrule), population size (npop), initial weight (wini), personal learning coefficient (c1), global learning coefficient (c2), and velocity limits (fv), in order to improve the performance of the adaptive neuro-fuzzy inference system in determining the buckling capacity of circular opening steel beams. This is an important mechanical property in terms of the safety of structures under subjected loads. An available database of 3645 data samples was used for generation of training (70%) and testing (30%) datasets. Monte Carlo simulations, which are natural variability generators, were used in the training phase of the algorithm. Various statistical measurements, such as root mean square error (RMSE), mean absolute error (MAE), Willmott’s index of agreement (IA), and Pearson’s coefficient of correlation (R), were used to evaluate the performance of the models. The results of the study show that the performance of ANFIS optimized by PSO (ANFIS-PSO) is suitable for determining the buckling capacity of circular opening steel beams, but is very sensitive under different PSO investigation and selection parameters. The findings of this study show that nrule = 10, npop = 50, wini = 0.1 to 0.4, c1 = [1, 1.4], c2 = [1.8, 2], fv = 0.1, which are the most suitable selection values to ensure the best performance for ANFIS-PSO. In short, this study might help in selection of suitable PSO parameters for optimization of the ANFIS model.
Quang Hung Nguyen; Hai-Bang Ly; Tien-Thinh Le; Thuy-Anh Nguyen; Viet-Hung Phan; Van Quan Tran; Binh Thai Pham. Parametric Investigation of Particle Swarm Optimization to Improve the Performance of the Adaptive Neuro-Fuzzy Inference System in Determining the Buckling Capacity of Circular Opening Steel Beams. Materials 2020, 13, 2210 .
AMA StyleQuang Hung Nguyen, Hai-Bang Ly, Tien-Thinh Le, Thuy-Anh Nguyen, Viet-Hung Phan, Van Quan Tran, Binh Thai Pham. Parametric Investigation of Particle Swarm Optimization to Improve the Performance of the Adaptive Neuro-Fuzzy Inference System in Determining the Buckling Capacity of Circular Opening Steel Beams. Materials. 2020; 13 (10):2210.
Chicago/Turabian StyleQuang Hung Nguyen; Hai-Bang Ly; Tien-Thinh Le; Thuy-Anh Nguyen; Viet-Hung Phan; Van Quan Tran; Binh Thai Pham. 2020. "Parametric Investigation of Particle Swarm Optimization to Improve the Performance of the Adaptive Neuro-Fuzzy Inference System in Determining the Buckling Capacity of Circular Opening Steel Beams." Materials 13, no. 10: 2210.