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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.
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.
This study focuses on the use of deep neural network (DNN) to predict the soil friction angle, one of the crucial parameters in geotechnical design. Besides, particle swarm optimization (PSO) algorithm was used to improve the performance of DNN by selecting the best structural DNN parameters, namely, the optimal numbers of hidden layers and neurons in each hidden layer. For this aim, a database containing 245 laboratory tests collected from a project in Ho Chi Minh city, Vietnam, was used for the development of the proposed hybrid PSO-DNN model, including seven input factors (soil state, standard penetration test value, unit weight of soil, void ratio, thickness of soil layer, top elevation of soil layer, and bottom elevation of soil layer) and the friction angle was considered as the target. The data set was divided into three parts, namely, the training, validation, and testing sets for the construction, validation, and testing phases of the model. Various quality assessment criteria, namely, the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE), were used to estimate the performance of PSO-DNN models. The PSO algorithm showed a remarkable ability to find out an optimal DNN architecture for the prediction process. The results showed that the PSO-DNN model using 10 hidden layers outperformed the DNN model, in which the average correlation improvement increased R2 by 1.83%, MAE by 5.94%, and RMSE by 8.58%. Besides, a global sensitivity analysis technique was used to detect the most important inputs, and it showed that, among the seven input variables, the elevation of top and bottom of soil played an important role in predicting the friction angle of soil.
Tuan Anh Pham; Van Quan Tran; Huong-Lan Thi Vu. Evolution of Deep Neural Network Architecture Using Particle Swarm Optimization to Improve the Performance in Determining the Friction Angle of Soil. Mathematical Problems in Engineering 2021, 2021, 1 -17.
AMA StyleTuan Anh Pham, Van Quan Tran, Huong-Lan Thi Vu. Evolution of Deep Neural Network Architecture Using Particle Swarm Optimization to Improve the Performance in Determining the Friction Angle of Soil. Mathematical Problems in Engineering. 2021; 2021 ():1-17.
Chicago/Turabian StyleTuan Anh Pham; Van Quan Tran; Huong-Lan Thi Vu. 2021. "Evolution of Deep Neural Network Architecture Using Particle Swarm Optimization to Improve the Performance in Determining the Friction Angle of Soil." Mathematical Problems in Engineering 2021, no. : 1-17.
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.
In this paper, an extensive simulation program is conducted to find out the optimal ANN model to predict the shear strength of fiber-reinforced polymer (FRP) concrete beams containing both flexural and shear reinforcements. For acquiring this purpose, an experimental database containing 125 samples is collected from the literature and used to find the best architecture of ANN. In this database, the input variables consist of 9 inputs, such as the ratio of the beam width, the effective depth, the shear span to the effective depth, the compressive strength of concrete, the longitudinal FRP reinforcement ratio, the modulus of elasticity of longitudinal FRP reinforcement, the FRP shear reinforcement ratio, the tensile strength of FRP shear reinforcement, the modulus of elasticity of FRP shear reinforcement. Thereafter, the selection of the appropriate architecture of ANN model is performed and evaluated by common statistical measurements. The results show that the optimal ANN model is a highly efficient predictor of the shear strength of FRP concrete beams with a maximum R2 value of 0.9634 on the training part and an R2 of 0.9577 on the testing part, using the best architecture. In addition, a sensitivity analysis using the optimal ANN model over 500 Monte Carlo simulations is performed to interpret the influence of reinforcement type on the stability and accuracy of ANN model in predicting shear strength. The results of this investigation could facilitate and enhance the use of ANN model in different real-world problems in the field of civil engineering.
Quang Hung Nguyen; Hai-Bang Ly; Thuy-Anh Nguyen; Viet-Hung Phan; Long Khanh Nguyen; Van Quan Tran. Investigation of ANN architecture for predicting shear strength of fiber reinforcement bars concrete beams. PLOS ONE 2021, 16, e0247391 .
AMA StyleQuang Hung Nguyen, Hai-Bang Ly, Thuy-Anh Nguyen, Viet-Hung Phan, Long Khanh Nguyen, Van Quan Tran. Investigation of ANN architecture for predicting shear strength of fiber reinforcement bars concrete beams. PLOS ONE. 2021; 16 (4):e0247391.
Chicago/Turabian StyleQuang Hung Nguyen; Hai-Bang Ly; Thuy-Anh Nguyen; Viet-Hung Phan; Long Khanh Nguyen; Van Quan Tran. 2021. "Investigation of ANN architecture for predicting shear strength of fiber reinforcement bars concrete beams." PLOS ONE 16, no. 4: e0247391.
Stabilized dredged sediments are used as a backfilling material to reduce construction costs and a solution to environmental protection. Therefore, the compressive strength is an important criterion to determine the stabilized dredged sediments application such as road construction, building construction, and highway construction. Using the traditional method such as empirical approach and experimental methods, the determination of compressive strength of stabilized dredged sediments is difficult due to the complexity of this composite material. In this investigation, the artificial neural network (ANN) model is introduced to forecast the compressive strength. To perform the simulation, 51 experimental datasets were collected from the literature. The dataset consists of 4 input variables (water content, cement content, air foam content, and waste fishing net content) and output variable (compressive strength). Evaluation of the models was made and compared on training dataset (70% data) and testing dataset (30% remaining data) by the criteria of Pearson’s correlation coefficient (R), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The results show that the ANN model can accurately predict the compressive strength of stabilized dredged sediments with low water content. The cement content is the most important input affecting the unconfined compressive strength. The important input affecting the unconfined compressive strength can be in the following order: cement content > air foam content > water content > waste fishing net.
Van Quan Tran. Compressive Strength Prediction of Stabilized Dredged Sediments Using Artificial Neural Network. Advances in Civil Engineering 2021, 2021, 1 -8.
AMA StyleVan Quan Tran. Compressive Strength Prediction of Stabilized Dredged Sediments Using Artificial Neural Network. Advances in Civil Engineering. 2021; 2021 ():1-8.
Chicago/Turabian StyleVan Quan Tran. 2021. "Compressive Strength Prediction of Stabilized Dredged Sediments Using Artificial Neural Network." Advances in Civil Engineering 2021, no. : 1-8.
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.
Determination of pile bearing capacity is essential in pile foundation design. This study focused on the use of evolutionary algorithms to optimize Deep Learning Neural Network (DLNN) algorithm to predict the bearing capacity of driven pile. For this purpose, a Genetic Algorithm (GA) was developed to select the most significant features in the raw dataset. After that, a GA-DLNN hybrid model was developed to select optimal parameters for the DLNN model, including: network algorithm, activation function for hidden neurons, number of hidden layers, and the number of neurons in each hidden layer. A database containing 472 driven pile static load test reports was used. The dataset was divided into three parts, namely the training set (60%), validation (20%) and testing set (20%) for the construction, validation and testing phases of the proposed model, respectively. Various quality assessment criteria, namely the coefficient of determination (R2), Index of Agreement (IA), mean absolute error (MAE) and root mean squared error (RMSE), were used to evaluate the performance of the machine learning (ML) algorithms. The GA-DLNN hybrid model was shown to exhibit the ability to find the most optimal set of parameters for the prediction process.The results showed that the performance of the hybrid model using only the most critical features gave the highest accuracy, compared with those obtained by the hybrid model using all input variables.
Tuan Anh Pham; Van Quan Tran; Huong-Lan Thi Vu; Hai-Bang Ly. Design deep neural network architecture using a genetic algorithm for estimation of pile bearing capacity. PLOS ONE 2020, 15, e0243030 .
AMA StyleTuan Anh Pham, Van Quan Tran, Huong-Lan Thi Vu, Hai-Bang Ly. Design deep neural network architecture using a genetic algorithm for estimation of pile bearing capacity. PLOS ONE. 2020; 15 (12):e0243030.
Chicago/Turabian StyleTuan Anh Pham; Van Quan Tran; Huong-Lan Thi Vu; Hai-Bang Ly. 2020. "Design deep neural network architecture using a genetic algorithm for estimation of pile bearing capacity." PLOS ONE 15, no. 12: e0243030.
Accurate prediction of the concrete compressive strength is an important task that helps to avoid costly and time-consuming experiments. Notably, the determination of the later-age concrete compressive strength is more difficult due to the time required to perform experiments. Therefore, predicting the compressive strength of later-age concrete is crucial in specific applications. In this investigation, an approach using a feedforward neural network (FNN) machine learning algorithm was proposed to predict the compressive strength of later-age concrete. The proposed model was fully evaluated in terms of performance and prediction capability over statistical results of 1000 simulations under a random sampling effect. The results showed that the proposed algorithm was an excellent predictor and might be useful for engineers to avoid time-consuming experiments with the statistical performance indicators, namely, the Pearson correlation coefficient (R), root-mean-squared error (RMSE), and mean squared error (MAE) for the training and testing parts of 0.9861, 2.1501, 1.5650 and 0.9792, 2.8510, 2.1361, respectively. The results also indicated that the FNN model was superior to classical machine learning algorithms such as random forest and Gaussian process regression, as well as empirical formulations proposed in the literature.
Thuy-Anh Nguyen; Hai-Bang Ly; Hai-Van Thi Mai; Van Quan Tran. Prediction of Later-Age Concrete Compressive Strength Using Feedforward Neural Network. Advances in Materials Science and Engineering 2020, 2020, 1 -8.
AMA StyleThuy-Anh Nguyen, Hai-Bang Ly, Hai-Van Thi Mai, Van Quan Tran. Prediction of Later-Age Concrete Compressive Strength Using Feedforward Neural Network. Advances in Materials Science and Engineering. 2020; 2020 ():1-8.
Chicago/Turabian StyleThuy-Anh Nguyen; Hai-Bang Ly; Hai-Van Thi Mai; Van Quan Tran. 2020. "Prediction of Later-Age Concrete Compressive Strength Using Feedforward Neural Network." Advances in Materials Science and Engineering 2020, no. : 1-8.
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.
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.
Understanding shear behavior is crucial for the design of reinforced concrete beams and sustainability in construction and civil engineering. Although numerous studies have been proposed, predicting such behavior still needs further improvement. This study proposes a soft-computing tool to predict the ultimate shear capacities (USCs) of concrete beams reinforced with steel fiber, one of the most important factors in structural design. Two hybrid machine learning (ML) algorithms were created that combine neural networks (NNs) with two distinct optimization techniques (i.e., the Real-Coded Genetic Algorithm (RCGA) and the Firefly Algorithm (FFA)): the NN-RCGA and the NN-FFA. A database of 463 experimental data was gathered from reliable literature for the development of the models. After the construction, validation, and selection of the best model based on common statistical criteria, a comparison with the empirical equations available in the literature was carried out. Further, a sensitivity analysis was conducted to evaluate the importance of 16 inputs and reveal the dependency of structural parameters on the USC. The results showed that the NN-RCGA (R = 0.9771) was better than the NN-FFA and other analytical models (R = 0.5274–0.9075). The sensitivity analysis results showed that web width, effective depth, and a clear depth ratio were the most important parameters in modeling the shear capacity of steel fiber-reinforced concrete beams.
Hai-Bang Ly; Tien-Thinh Le; Huong-Lan Thi Vu; Van Quan Tran; Lu Minh Le; Binh Thai Pham. Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams. Sustainability 2020, 12, 2709 .
AMA StyleHai-Bang Ly, Tien-Thinh Le, Huong-Lan Thi Vu, Van Quan Tran, Lu Minh Le, Binh Thai Pham. Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams. Sustainability. 2020; 12 (7):2709.
Chicago/Turabian StyleHai-Bang Ly; Tien-Thinh Le; Huong-Lan Thi Vu; Van Quan Tran; Lu Minh Le; Binh Thai Pham. 2020. "Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams." Sustainability 12, no. 7: 2709.
Machine Learning (ML) has been applied widely in solving a lot of real-world problems. However, this approach is very sensitive to the selection of input variables for modeling and simulation. In this study, the main objective is to analyze the sensitivity of an advanced ML method, namely the Extreme Learning Machine (ELM) algorithm under different feature selection scenarios for prediction of shear strength of soil. Feature backward elimination supported by Monte Carlo simulations was applied to evaluate the importance of factors used for the modeling. A database constructed from 538 samples collected from Long Phu 1 power plant project was used for analysis. Well-known statistical indicators, such as the correlation coefficient (R), root mean squared error (RMSE), and mean absolute error (MAE), were utilized to evaluate the performance of the ELM algorithm. In each elimination step, the majority vote based on six elimination indicators was selected to decide the variable to be excluded. A number of 30,000 simulations were conducted to find out the most relevant variables in predicting the shear strength of soil using ELM. The results show that the performance of ELM is good but very different under different combinations of input factors. The moisture content, liquid limit, and plastic limit were found as the most critical variables for the prediction of shear strength of soil using the ML model.
Binh Thai Pham; Trung Nguyen-Thoi; Hai-Bang Ly; Manh Duc Nguyen; Nadhir Al-Ansari; Van-Quan Tran; Tien-Thinh Le. Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination. Sustainability 2020, 12, 2339 .
AMA StyleBinh Thai Pham, Trung Nguyen-Thoi, Hai-Bang Ly, Manh Duc Nguyen, Nadhir Al-Ansari, Van-Quan Tran, Tien-Thinh Le. Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination. Sustainability. 2020; 12 (6):2339.
Chicago/Turabian StyleBinh Thai Pham; Trung Nguyen-Thoi; Hai-Bang Ly; Manh Duc Nguyen; Nadhir Al-Ansari; Van-Quan Tran; Tien-Thinh Le. 2020. "Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination." Sustainability 12, no. 6: 2339.
Axial bearing capacity of piles is the most important parameter in pile foundation design. In this paper, artificial neural network (ANN) and random forest (RF) algorithms were utilized to predict the ultimate axial bearing capacity of driven piles. An unprecedented database containing 2314 driven pile static load test reports were gathered, including the pile diameter, length of pile segments, natural ground elevation, pile top elevation, guide pile segment stop driving elevation, pile tip elevation, average standard penetration test (SPT) value along the embedded length of pile, and average SPT blow counts at the tip of pile as input variables, whereas the ultimate load on pile top was considered as output variable. The dataset was divided into the training (70%) and testing (30%) parts for the construction and validation phases, respectively. Various error criteria, namely mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination (R2) were used to evaluate the performance of RF and ANN algorithms. In addition, the predicted results of pile load tests were compared with five empirical equations derived from the literature and with classical multi-variable regression. The results showed that RF outperformed ANN and other methods. Sensitivity analysis was conducted to reveal that the average SPT value and pile tip elevation were the most important factors in predicting the axial bearing capacity of piles.
Tuan Anh Pham; Hai-Bang Ly; Van Quan Tran; Loi Van Giap; Huong-Lan Thi Vu; Hong-Anh Thi Duong. Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest. Applied Sciences 2020, 10, 1871 .
AMA StyleTuan Anh Pham, Hai-Bang Ly, Van Quan Tran, Loi Van Giap, Huong-Lan Thi Vu, Hong-Anh Thi Duong. Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest. Applied Sciences. 2020; 10 (5):1871.
Chicago/Turabian StyleTuan Anh Pham; Hai-Bang Ly; Van Quan Tran; Loi Van Giap; Huong-Lan Thi Vu; Hong-Anh Thi Duong. 2020. "Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest." Applied Sciences 10, no. 5: 1871.
Concrete filled steel tubes (CFSTs) show advantageous applications in the field of construction, especially for a high axial load capacity. The challenge in using such structure lies in the selection of many parameters constituting CFST, which necessitates defining complex relationships between the components and the corresponding properties. The axial capacity (Pu) of CFST is among the most important mechanical properties. In this study, the possibility of using a feedforward neural network (FNN) to predict Pu was investigated. Furthermore, an evolutionary optimization algorithm, namely invasive weed optimization (IWO), was used for tuning and optimizing the FNN weights and biases to construct a hybrid FNN–IWO model and improve its prediction performance. The results showed that the FNN–IWO algorithm is an excellent predictor of Pu, with a value of R2 of up to 0.979. The advantage of FNN–IWO was also pointed out with the gains in accuracy of 47.9%, 49.2%, and 6.5% for root mean square error (RMSE), mean absolute error (MAE), and R2, respectively, compared with simulation using the single FNN. Finally, the performance in predicting the Pu in the function of structural parameters such as depth/width ratio, thickness of steel tube, yield stress of steel, concrete compressive strength, and slenderness ratio was investigated and discussed.
Hung Quang Nguyen; Hai-Bang Ly; Van Quan Tran; Thuy-Anh Nguyen; Tien-Thinh Le; Binh Thai Pham. Optimization of Artificial Intelligence System by Evolutionary Algorithm for Prediction of Axial Capacity of Rectangular Concrete Filled Steel Tubes under Compression. Materials 2020, 13, 1205 .
AMA StyleHung Quang Nguyen, Hai-Bang Ly, Van Quan Tran, Thuy-Anh Nguyen, Tien-Thinh Le, Binh Thai Pham. Optimization of Artificial Intelligence System by Evolutionary Algorithm for Prediction of Axial Capacity of Rectangular Concrete Filled Steel Tubes under Compression. Materials. 2020; 13 (5):1205.
Chicago/Turabian StyleHung Quang Nguyen; Hai-Bang Ly; Van Quan Tran; Thuy-Anh Nguyen; Tien-Thinh Le; Binh Thai Pham. 2020. "Optimization of Artificial Intelligence System by Evolutionary Algorithm for Prediction of Axial Capacity of Rectangular Concrete Filled Steel Tubes under Compression." Materials 13, no. 5: 1205.
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.
Gas multisensor devices offer an effective approach to monitor air pollution, which has become a pandemic in many cities, especially because of transport emissions. To be reliable, properly trained models need to be developed that combine output from sensors with weather data; however, many factors can affect the accuracy of the models. The main objective of this study was to explore the impact of several input variables in training different air quality indexes using fuzzy logic combined with two metaheuristic optimizations: simulated annealing (SA) and particle swarm optimization (PSO). In this work, the concentrations of NO2 and CO were predicted using five resistivities from multisensor devices and three weather variables (temperature, relative humidity, and absolute humidity). In order to validate the results, several measures were calculated, including the correlation coefficient and the mean absolute error. Overall, PSO was found to perform the best. Finally, input resistivities of NO2 and nonmetanic hydrocarbons (NMHC) were found to be the most sensitive to predict concentrations of NO2 and CO.
Hai-Bang Ly; Lu Minh Le; Luong Van Phi; Viet-Hung Phan; Van Quan Tran; Binh Thai Pham; Tien-Thinh Le; Sybil Derrible. Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data. Sensors 2019, 19, 4941 .
AMA StyleHai-Bang Ly, Lu Minh Le, Luong Van Phi, Viet-Hung Phan, Van Quan Tran, Binh Thai Pham, Tien-Thinh Le, Sybil Derrible. Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data. Sensors. 2019; 19 (22):4941.
Chicago/Turabian StyleHai-Bang Ly; Lu Minh Le; Luong Van Phi; Viet-Hung Phan; Van Quan Tran; Binh Thai Pham; Tien-Thinh Le; Sybil Derrible. 2019. "Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data." Sensors 19, no. 22: 4941.