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Dong Van Dao
University of Transport Technology, Hanoi 100000, Viet Nam

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Journal article
Published: 25 February 2021 in Knowledge-Based Systems
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In this paper, we proposed a novel approach for flood risk assessment, which is a combination of a deep learning algorithm and Multi-Criteria Decision Analysis (MCDA). The framework of the flood risk assessment involves three main elements: hazard, exposure, and vulnerability. For this purpose, one of the flood-prone areas of Vietnam, namely Quang Nam province was selected as the study area. Data of 847 past flood locations of this area was analyzed to generate training and testing datasets for the models. In this study, we have used one of the popular Deep Neural Networks (DNNs) algorithm for generation of flood susceptibility map while Analytic Hierarchy Process (AHP), which is a popular MCDA approach, was used to generate the hazard, exposure, and vulnerability maps. We have also used hybrid models namely BFPA and DFPA which are the ensembles of Bagging and Decorate with Forest by Penalizing Attributes algorithm for the comparison of performance with DNNs method. Various standard statistical indices including Receiver Operating Characteristic (ROC) curves were used for the performance evaluation and validation of the models. Results indicated that integration of DNNs and MCDA models is a promising approach for developing accurate flood risk assessment map of an area for the better flood hazard management.

ACS Style

Binh Thai Pham; Chinh Luu; Dong Van Dao; Tran Van Phong; Huu Duy Nguyen; Hiep Van Le; Jason von Meding; Indra Prakash. Flood risk assessment using deep learning integrated with multi-criteria decision analysis. Knowledge-Based Systems 2021, 219, 106899 .

AMA Style

Binh Thai Pham, Chinh Luu, Dong Van Dao, Tran Van Phong, Huu Duy Nguyen, Hiep Van Le, Jason von Meding, Indra Prakash. Flood risk assessment using deep learning integrated with multi-criteria decision analysis. Knowledge-Based Systems. 2021; 219 ():106899.

Chicago/Turabian Style

Binh Thai Pham; Chinh Luu; Dong Van Dao; Tran Van Phong; Huu Duy Nguyen; Hiep Van Le; Jason von Meding; Indra Prakash. 2021. "Flood risk assessment using deep learning integrated with multi-criteria decision analysis." Knowledge-Based Systems 219, no. : 106899.

Journal article
Published: 23 July 2020 in Materials
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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*).

ACS Style

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 Style

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 (15):3272.

Chicago/Turabian Style

Dong 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.

Journal article
Published: 28 February 2020 in Materials
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Development of Foamed Concrete (FC) and incessant increases in fabrication technology have paved the way for many promising civil engineering applications. Nevertheless, the design of FC requires a large number of experiments to determine the appropriate Compressive Strength (CS). Employment of machine learning algorithms to take advantage of the existing experiments database has been attempted, but model performance can still be improved. In this study, the performance of an Artificial Neural Network (ANN) was fully analyzed to predict the 28 days CS of FC. Monte Carlo simulations (MCS) were used to statistically analyze the convergence of the modeled results under the effect of random sampling strategies and the network structures selected. Various statistical measures such as Coefficient of Determination (R2), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) were used for validation of model performance. The results show that ANN is a highly efficient predictor of the CS of FC, achieving a maximum R2 value of 0.976 on the training part and an R2 of 0.972 on the testing part, using the optimized C-ANN-[3–4–5–1] structure, which compares with previous published studies. In addition, a sensitivity analysis using Partial Dependence Plots (PDP) over 1000 MCS was also performed to interpret the relationship between the input parameters and 28 days CS of FC. Dry density was found as the variable with the highest impact to predict the CS of FC. The results presented could facilitate and enhance the use of C-ANN in other civil engineering-related problems.

ACS Style

Dong Van Dao; Hai-Bang Ly; Huong-Lan Thi Vu; Tien-Thinh Le; Binh Thai Pham. Investigation and Optimization of the C-ANN Structure in Predicting the Compressive Strength of Foamed Concrete. Materials 2020, 13, 1072 .

AMA Style

Dong Van Dao, Hai-Bang Ly, Huong-Lan Thi Vu, Tien-Thinh Le, Binh Thai Pham. Investigation and Optimization of the C-ANN Structure in Predicting the Compressive Strength of Foamed Concrete. Materials. 2020; 13 (5):1072.

Chicago/Turabian Style

Dong Van Dao; Hai-Bang Ly; Huong-Lan Thi Vu; Tien-Thinh Le; Binh Thai Pham. 2020. "Investigation and Optimization of the C-ANN Structure in Predicting the Compressive Strength of Foamed Concrete." Materials 13, no. 5: 1072.

Journal article
Published: 22 January 2020 in Sustainability
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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.

ACS Style

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 Style

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 (3):830.

Chicago/Turabian Style

Dong 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.

Journal article
Published: 08 January 2020 in CATENA
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With the increasing threat of recurring landslides, susceptibility maps are expected to play a bigger role in promoting our understanding of future landslides and their magnitude. This study describes the development and validation of a spatially explicit deep learning (DL) neural network model for the prediction of landslide susceptibility. A geospatial database was generated based on 217 landslide events from the Muong Lay district (Vietnam), for which a suite of nine landslide conditioning factors was derived. The Relief-F feature selection method was employed to quantify the utility of the conditioning factors for developing the landslide predictive model. Several performance metrics demonstrated that the DL model performed well both in terms of the goodness-of-fit with the training dataset (AUC = 0.90; accuracy = 82%; RMSE = 0.36) and the ability to predict future landslides (AUC = 0.89; accuracy = 82%; RMSE = 0.38). The efficiency of the model was compared to the quadratic discriminant analysis, Fisher's linear discriminant analysis, and multi-layer perceptron neural network. A comparative analysis using the Wilcoxon signed-rank tests revealed a significant improvement of landslide prediction using the spatially explicit DL model over these other models. The insights provided from this study will be valuable for further development of landslide predictive models and spatially explicit assessment of landslide-prone regions around the world.

ACS Style

Dong Van Dao; Abolfazl Jaafari; Mahmoud Bayat; Davood Mafi-Gholami; Chongchong Qi; Hossein Moayedi; Tran Van Phong; Hai-Bang Ly; Tien-Thinh Le; Phan Trong Trinh; Chinh Luu; Nguyen Kim Quoc; Bui Nhi Thanh; Binh Thai Pham. A spatially explicit deep learning neural network model for the prediction of landslide susceptibility. CATENA 2020, 188, 104451 .

AMA Style

Dong Van Dao, Abolfazl Jaafari, Mahmoud Bayat, Davood Mafi-Gholami, Chongchong Qi, Hossein Moayedi, Tran Van Phong, Hai-Bang Ly, Tien-Thinh Le, Phan Trong Trinh, Chinh Luu, Nguyen Kim Quoc, Bui Nhi Thanh, Binh Thai Pham. A spatially explicit deep learning neural network model for the prediction of landslide susceptibility. CATENA. 2020; 188 ():104451.

Chicago/Turabian Style

Dong Van Dao; Abolfazl Jaafari; Mahmoud Bayat; Davood Mafi-Gholami; Chongchong Qi; Hossein Moayedi; Tran Van Phong; Hai-Bang Ly; Tien-Thinh Le; Phan Trong Trinh; Chinh Luu; Nguyen Kim Quoc; Bui Nhi Thanh; Binh Thai Pham. 2020. "A spatially explicit deep learning neural network model for the prediction of landslide susceptibility." CATENA 188, no. : 104451.

Journal article
Published: 12 September 2019 in Applied Sciences
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Use of manufactured sand to replace natural sand is increasing in the last several decades. This study is devoted to the assessment of using Principal Component Analysis (PCA) together with Teaching-Learning-Based Optimization (TLBO) for enhancing the prediction accuracy of individual Adaptive Neuro Fuzzy Inference System (ANFIS) in predicting the compressive strength of manufactured sand concrete (MSC). The PCA technique was applied for reducing the noise in the input space, whereas, TLBO was employed to increase the prediction performance of single ANFIS model in searching the optimal weights of input parameters. A number of 289 configurations of MSC were used for the simulation, especially including the sand characteristics and the MSC long-term compressive strength. Using various validation criteria such as Correlation Coefficient (R), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), the proposed method was validated and compared with several models, including individual ANFIS, Artificial Neural Networks (ANN) and existing empirical equations. The results showed that the proposed model exhibited great prediction capability compared with other models. Thus, it appeared as a robust alternative computing tool or an efficient soft computing technique for quick and accurate prediction of the MSC compressive strength.

ACS Style

Hai-Bang Ly; Binh Thai Pham; Dong Van Dao; Vuong Minh Le. Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete. Applied Sciences 2019, 9, 3841 .

AMA Style

Hai-Bang Ly, Binh Thai Pham, Dong Van Dao, Vuong Minh Le. Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete. Applied Sciences. 2019; 9 (18):3841.

Chicago/Turabian Style

Hai-Bang Ly; Binh Thai Pham; Dong Van Dao; Vuong Minh Le. 2019. "Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete." Applied Sciences 9, no. 18: 3841.

Journal article
Published: 07 May 2019 in Science of The Total Environment
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In this study, we developed Different Artificial Intelligence (AI) models namely Artificial Neural Network (ANN), Adaptive Network based Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) for the prediction of Compression Coefficient of soil (Cc) which is one of the most important geotechnical parameters. A Monte Carlo approach was used for the sensitivity analysis of the AI models and input parameters. For the construction and validation of the models, 189 soft clayey soil samples were analyzed. In the models study, 13 input parameters: depth of sample, bulk density, plasticity index, moisture content, clay content, specific gravity, void ratio, liquid limit, dry density, porosity, plastic limit, degree of saturation, and liquidity index were used to obtain one output parameter “Cc”. Validation of the models was done using statistical methods such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of determination (R2). Results of the model validation indicate that though performance of all the three models is good but SVM model is the best in the prediction of Cc. The Monte Carlo method based sensitivity analysis results show that out of the 13 input parameters considered for the models study, four parameters namely clay, degree of saturation, specific gravity and depth of sample are the most relevant in the prediction of Cc, and other parameters (bulk density, dry density, void ratio and porosity) are the most insignificant parameters for the prediction of Cc. Removal of these insignificant parameters helped to reduce the dimension of the input space and also model running time, and improved significantly the performance of the AI models. The results of this study might help in selecting the suitable AI models and input parameters for better and quick prediction of the Cc of soil.

ACS Style

Binh Thai Pham; Manh Duc Nguyen; Dong Van Dao; Indra Prakash; Hai-Bang Ly; Tien-Thinh Le; Loc Ho; Kien Trung Nguyen; Trinh Quoc Ngo; Vu Hoang; Le Hoang Son; Huong Thanh Thi Ngo; Hieu Trung Tran; Ngoc Minh Do; Hiep Van Le; Huu Loc Ho; Dieu Tien Bui. Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis. Science of The Total Environment 2019, 679, 172 -184.

AMA Style

Binh Thai Pham, Manh Duc Nguyen, Dong Van Dao, Indra Prakash, Hai-Bang Ly, Tien-Thinh Le, Loc Ho, Kien Trung Nguyen, Trinh Quoc Ngo, Vu Hoang, Le Hoang Son, Huong Thanh Thi Ngo, Hieu Trung Tran, Ngoc Minh Do, Hiep Van Le, Huu Loc Ho, Dieu Tien Bui. Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis. Science of The Total Environment. 2019; 679 ():172-184.

Chicago/Turabian Style

Binh Thai Pham; Manh Duc Nguyen; Dong Van Dao; Indra Prakash; Hai-Bang Ly; Tien-Thinh Le; Loc Ho; Kien Trung Nguyen; Trinh Quoc Ngo; Vu Hoang; Le Hoang Son; Huong Thanh Thi Ngo; Hieu Trung Tran; Ngoc Minh Do; Hiep Van Le; Huu Loc Ho; Dieu Tien Bui. 2019. "Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis." Science of The Total Environment 679, no. : 172-184.

Journal article
Published: 25 March 2019 in Materials
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Geopolymer concrete (GPC) has been used as a partial replacement of Portland cement concrete (PCC) in various construction applications. In this paper, two artificial intelligence approaches, namely adaptive neuro fuzzy inference (ANFIS) and artificial neural network (ANN), were used to predict the compressive strength of GPC, where coarse and fine waste steel slag were used as aggregates. The prepared mixtures contained fly ash, sodium hydroxide in solid state, sodium silicate solution, coarse and fine steel slag aggregates as well as water, in which four variables (fly ash, sodium hydroxide, sodium silicate solution, and water) were used as input parameters for modeling. A total number of 210 samples were prepared with target-specified compressive strength at standard age of 28 days of 25, 35, and 45 MPa. Such values were obtained and used as targets for the two AI prediction tools. Evaluation of the model’s performance was achieved via criteria such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The results showed that both ANN and ANFIS models have strong potential for predicting the compressive strength of GPC but ANFIS (MAE = 1.655 MPa, RMSE = 2.265 MPa, and R2 = 0.879) is better than ANN (MAE = 1.989 MPa, RMSE = 2.423 MPa, and R2 = 0.851). Sensitivity analysis was then carried out, and it was found that reducing one input parameter could only make a small change to the prediction performance.

ACS Style

Dong Van Dao; Hai-Bang Ly; Son Hoang Trinh; Tien-Thinh Le; Binh Thai Pham. Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete. Materials 2019, 12, 983 .

AMA Style

Dong Van Dao, Hai-Bang Ly, Son Hoang Trinh, Tien-Thinh Le, Binh Thai Pham. Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete. Materials. 2019; 12 (6):983.

Chicago/Turabian Style

Dong Van Dao; Hai-Bang Ly; Son Hoang Trinh; Tien-Thinh Le; Binh Thai Pham. 2019. "Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete." Materials 12, no. 6: 983.

Journal article
Published: 16 March 2019 in Applied Sciences
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Geopolymer concrete (GPC) is applied successfully in the construction of civil engineering structures. This outcome confirmed that GPC can be used as an alternative material to conventional ordinary Portland cement concrete (OPC). Recent investigations were attempted to incorporate recycled aggregates into GPC to reduce the use of natural materials such as stone and sand. However, traditional methodology used to predict compressive strength and to find out an optimum mix for GPC is yet to be formulated, especially in cases of GPC using by-products as aggregates. In this study, we propose novel hybrid artificial intelligence (AI) approaches, namely a particle swarm optimization (PSO)-based adaptive network-based fuzzy inference system (PSOANFIS) and a genetic algorithm (GA)-based adaptive network-based fuzzy inference system (GAANFIS) to predict the 28-day compressive strength of GPC containing 100% waste slag aggregates. To construct and validate these models, 21 different mixes with 210 specimens were casted and tested. Three input parameters were used to predict the tested compressive strength of GPC, i.e., the sodium solution (NaOH) concentration (varied from 10 to 14 M), the mass ratio of alkaline activation solution to fly ash (AAS/FA), ranging from 0.4 to 0.5, and the mass ratio of sodium silicate (Na2SiO3) to sodium hydroxide solution (SS/SH) which was varied from 2 to 3. The compressive strength of the fabricated GPC was used as output parameter for the prediction models. Validation of the models was done using several statistical criteria such as mean absolute error (MAE), root-mean-square error (RMSE), and correlation coefficient (R). The results show that the PSOANFIS and GAANFIS models have strong potential for predicting the 28-day compressive strength of GPC, but the PSOANFIS (MAE = 1.847 MPa, RMSE = 2.251 MPa, and R = 0.934) was slightly better than the GAANFIS (MAE = 2.115 MPa, RMSE = 2.531 MPa, and R = 0.927). This study will help in reducing the time and cost for the implementation of laboratory experiments in designing the optimum proportions of GPC.

ACS Style

Dong Van Dao; Son Hoang Trinh; Hai-Bang Ly; Binh Thai Pham. Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches. Applied Sciences 2019, 9, 1113 .

AMA Style

Dong Van Dao, Son Hoang Trinh, Hai-Bang Ly, Binh Thai Pham. Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches. Applied Sciences. 2019; 9 (6):1113.

Chicago/Turabian Style

Dong Van Dao; Son Hoang Trinh; Hai-Bang Ly; Binh Thai Pham. 2019. "Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches." Applied Sciences 9, no. 6: 1113.