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Accurate prediction of the shear capacity of reinforced concrete shear walls (RCSW) is essential for the wind and seismic design of buildings. However, due to the diverse structural configurations, multitude of load scenarios, and highly nonlinear relations between the design parameters and the shear load capacity, this prediction is very complex. Existing pertinent design code provisions such as the American Concrete Institute ACI-318 and the Eurocode rely on empirical expressions that have various limitations and attain low predictive accuracy. Hence, in this paper, we pioneer a novel hybrid intelligent model to predict the ultimate shear capacity of RCSW. The support vector regression (SVR) and response surface model (RSM) were coupled based on two calibrating strategies in a novel hybrid modelling approach called RSM-SVR. The accuracy, tendency and uncertainty of the proposed SVR-RSM model along with that of three existing empirical relations and two design code provisions were assessed using various statistical metrics based on a comprehensive experimental database retrieved from the open literature. The existing design codes and empirical models were found to be inflicted with high variability and did not capture the influence of the key design parameters on the shear capacity in a robust and rational manner. Conversely, it is shown that the proposed RSM-SVR modeling approach achieved superior accurate predictions for the shear strength of RCSW. The proposed RSM-SVR model enhanced RMSE for the training (testing) dataset by 510% (150%) compared to the Baghi et al. model, 550% (190%) compared to the ACI 318-14 design code, 530% (155%) compared to the Chandra et al. model, 320% (145%) compared to the RSM model, and 450% (90%) compared to the SVR model. The novel approach also better captured the influence of the key design parameters, demonstrating robust tendency and much lower uncertainty. Thus, the proposed novel model could be harvested in intelligent generative design and for the enhancement of pertinent provisions in design codes. The proposed method achieves outstanding performance, while maintaining superior computational efficiency and low run time.
Behrooz Keshtegar; Moncef L. Nehdi; Nguyen-Thoi Trung; Reza Kolahchi. Predicting load capacity of shear walls using SVR–RSM model. Applied Soft Computing 2021, 112, 107739 .
AMA StyleBehrooz Keshtegar, Moncef L. Nehdi, Nguyen-Thoi Trung, Reza Kolahchi. Predicting load capacity of shear walls using SVR–RSM model. Applied Soft Computing. 2021; 112 ():107739.
Chicago/Turabian StyleBehrooz Keshtegar; Moncef L. Nehdi; Nguyen-Thoi Trung; Reza Kolahchi. 2021. "Predicting load capacity of shear walls using SVR–RSM model." Applied Soft Computing 112, no. : 107739.
This article proposes a finite element method (FEM) based on a quasi-3D nonlocal theory to study the free vibration of functionally graded material (FGM) nanoplates lying on the elastic foundation (EF) in the thermal environment. By applying Hamilton's principle, the governing equations of FGM nanoplates on the EF are obtained. Using the FEM helps solve many complicated problems that analytical solution (AS) cannot be performed yet, such as complex structures, asymmetric problems, variable thickness, etc. The numerical results of this work are compared with those of other published researches to verify accuracy and reliability. In addition, the effects of geometrical parameters, material properties such as the thickness, material exponents, nonlocal coefficients, elastic foundation stiffness, boundary conditions (BCs), and temperature on the free vibration of nanoplates are comprehensively investigated.
Quoc-Hoa Pham; Van Ke Tran; Trung Thanh Tran; Trung Nguyen-Thoi; Phu-Cuong Nguyen; Van Dong Pham. A nonlocal quasi-3D theory for thermal free vibration analysis of functionally graded material nanoplates resting on elastic foundation. Case Studies in Thermal Engineering 2021, 26, 101170 .
AMA StyleQuoc-Hoa Pham, Van Ke Tran, Trung Thanh Tran, Trung Nguyen-Thoi, Phu-Cuong Nguyen, Van Dong Pham. A nonlocal quasi-3D theory for thermal free vibration analysis of functionally graded material nanoplates resting on elastic foundation. Case Studies in Thermal Engineering. 2021; 26 ():101170.
Chicago/Turabian StyleQuoc-Hoa Pham; Van Ke Tran; Trung Thanh Tran; Trung Nguyen-Thoi; Phu-Cuong Nguyen; Van Dong Pham. 2021. "A nonlocal quasi-3D theory for thermal free vibration analysis of functionally graded material nanoplates resting on elastic foundation." Case Studies in Thermal Engineering 26, no. : 101170.
In recent years, the strong development of urban areas and rapid population growth have contributed significantly to environmental pollution issues, especially SW. Of those, municipal solid waste (MSW) is considered a major concern of waste treatment plants. Nowadays, with the development of science and technology, MSW has been treated and recycled to recover energy. However, the issue of energy recovery and optimization from MSW remains a challenge for waste treatment plants. Therefore, a novel artificial intelligence approach was proposed in this study for predicting the gas yield (GY) generated by energy recovery from MSW with high accuracy. Accordingly, a deep neural network (DNN) was developed to predict GY from MSW. Subsequently, the Moth-Flame optimization (MFO) algorithm was applied to optimize the DNN model and improve its accuracy, called MFO-DNN model. The findings revealed that both the DNN and MFO-DNN models predicted GY very well. Of those, the proposed MFO-DNN model provided dominant performance than the DNN model. Based on the proposed MFO-DNN model, the toxic gases can be thoroughly controlled and optimized to recover the gas field from MSW for waste treatment plants, minimizing negative impacts on the surrounding environment.
Libing Yang; Hoang Nguyen; Xuan-Nam Bui; Trung Nguyen-Thoi; Jian Zhou; Jianing Huang. Prediction of gas yield generated by energy recovery from municipal solid waste using deep neural network and moth-flame optimization algorithm. Journal of Cleaner Production 2021, 311, 127672 .
AMA StyleLibing Yang, Hoang Nguyen, Xuan-Nam Bui, Trung Nguyen-Thoi, Jian Zhou, Jianing Huang. Prediction of gas yield generated by energy recovery from municipal solid waste using deep neural network and moth-flame optimization algorithm. Journal of Cleaner Production. 2021; 311 ():127672.
Chicago/Turabian StyleLibing Yang; Hoang Nguyen; Xuan-Nam Bui; Trung Nguyen-Thoi; Jian Zhou; Jianing Huang. 2021. "Prediction of gas yield generated by energy recovery from municipal solid waste using deep neural network and moth-flame optimization algorithm." Journal of Cleaner Production 311, no. : 127672.
In this paper, we develop an extension of the Fourier Transform solution method in order to solve conduction equation with non periodic boundary conditions. The periodic Lippmann Schwinger equation for porous materials is extended to the case of non-periodicity with relevant source terms on the boundary. The method is formulated in Fourier space based on the temperature as unknown, using the exact periodic Green function and form factors to describe the boundaries. Different types of boundary conditions: flux, temperature, mixed and combined with periodicity can be treated by the method. Numerical simulations show that the method does not encounter convergence issues due to the infinite contrast and yields accurate results for both local fields and effective conductivity.
Quy‐Dong To; Guy Bonnet; Trung Nguyen‐Thoi. Fourier transform approach to nonperiodic boundary value problems in porous conductive media. International Journal for Numerical Methods in Engineering 2021, 122, 4864 -4885.
AMA StyleQuy‐Dong To, Guy Bonnet, Trung Nguyen‐Thoi. Fourier transform approach to nonperiodic boundary value problems in porous conductive media. International Journal for Numerical Methods in Engineering. 2021; 122 (18):4864-4885.
Chicago/Turabian StyleQuy‐Dong To; Guy Bonnet; Trung Nguyen‐Thoi. 2021. "Fourier transform approach to nonperiodic boundary value problems in porous conductive media." International Journal for Numerical Methods in Engineering 122, no. 18: 4864-4885.
Heavy metal adsorption onto biochar is an effective method for the treatment of the heavy metal contamination of water and wastewater. This study aims to evaluate the heavy metals sorption efficiency of different biochar characteristics and propose a novel intelligence method for predicting the sorption efficiency of heavy metal onto biochar with high accuracy based on the back-propagation neural network (BPNN) and fuzzy C-means clustering algorithm (FCM), named as FCM-BPNN. Accordingly, the FCM algorithm was used to simulate the properties of metal adsorption data and divide them into clusters with similar features. The clustering results showed that the FCM algorithm simulated metal adsorption data's properties very well and classified them based on biochar characteristics and adsorption conditions. Afterward, BPNN models were well-developed based on these clusters, and their outcomes were then combined (i.e., FCM-BPNN). The results indicated that the FCM-BPNN model could predict heavy metal's sorption efficiency onto biochar with a promising result (i.e., RMSE of 0.036, R2 of 0.987, RSE of 0.006, MAPE of 0.706, and VAF of 98.724). Whereas the BPNN model, without optimizing the FCM algorithm, was proved with lower performance (RMSE = 0.050, R2 = 0.977, RSE = 0.011, MAPE = 0.802, and VAF = 97.662). These findings revealed that the FCM algorithm's presence impressively improved the BPNN model's accomplishment in predicting heavy metal's sorption efficiency onto biochar, and the proposed FCM-BPNN model can improve water/wastewater treatment plants' quality and provide a more efficient process for heavy metals with performance superiority.
Bo Ke; Hoang Nguyen; Xuan-Nam Bui; Hoang-Bac Bui; Trung Nguyen-Thoi. Prediction of the sorption efficiency of heavy metal onto biochar using a robust combination of fuzzy C-means clustering and back-propagation neural network. Journal of Environmental Management 2021, 293, 112808 .
AMA StyleBo Ke, Hoang Nguyen, Xuan-Nam Bui, Hoang-Bac Bui, Trung Nguyen-Thoi. Prediction of the sorption efficiency of heavy metal onto biochar using a robust combination of fuzzy C-means clustering and back-propagation neural network. Journal of Environmental Management. 2021; 293 ():112808.
Chicago/Turabian StyleBo Ke; Hoang Nguyen; Xuan-Nam Bui; Hoang-Bac Bui; Trung Nguyen-Thoi. 2021. "Prediction of the sorption efficiency of heavy metal onto biochar using a robust combination of fuzzy C-means clustering and back-propagation neural network." Journal of Environmental Management 293, no. : 112808.
The main goal of this research paper is to present the modeling and analysis of bi-directional functionally graded (BDFG) nanobeams within the framework of the Timoshenko beam theory and nonlocal strain gradient theory. According to the DBFG material model, the material properties of the nanobeams are simultaneously distributed in two different directions (thickness and length directions). Besides, the volume fraction of component material is described by a function that combines the power and exponential distribution rules. The study focuses strongly on understanding the mechanical behavior of the BDFG nanobeams and in calculating important parameters of materials and nonlocal strain gradient coefficients. In addition, equilibrium and stability equations for DBFG nanobeams are systematically formulated to static bending and buckling problems with the corresponding boundary condition. The highlight is the combination of two different technical solutions as Navier solution and the Galerkin technique. In the numerical results section, some specific examples are presented to verify the proposed solution, and thereby, a good agreement is observed. Finally, a detailed investigation is performed, with a particular focus on the influences of material properties, nonlocal parameter on the critical buckling load and transverse deflection of the BDFG nanobeams.
Pham Toan Thang; T. Nguyen-Thoi; Jaehong Lee. Modeling and analysis of bi-directional functionally graded nanobeams based on nonlocal strain gradient theory. Applied Mathematics and Computation 2021, 407, 126303 .
AMA StylePham Toan Thang, T. Nguyen-Thoi, Jaehong Lee. Modeling and analysis of bi-directional functionally graded nanobeams based on nonlocal strain gradient theory. Applied Mathematics and Computation. 2021; 407 ():126303.
Chicago/Turabian StylePham Toan Thang; T. Nguyen-Thoi; Jaehong Lee. 2021. "Modeling and analysis of bi-directional functionally graded nanobeams based on nonlocal strain gradient theory." Applied Mathematics and Computation 407, no. : 126303.
In the current study, an ability of a novel regression-based method is evaluated in modeling daily reference evapotranspiration (ET0), which is an important issue in water resources management and planning. The method was developed by hybridizing radial basis function and M5 model tree and called as radial basis M5 model tree (RM5Tree). The new model results were compared with traditional M5 model tree (M5Tree), response surface method (RSM), and two neural networks (multi-layer perceptron neural networks, MLPNN & radial basis function neural network, RBFNN) with respect to several statistical indices. Daily climatic data (relative humidity, RH, solar radiation, SR, wind speed, air temperature, T) recorded at three stations in Turkey, Mediterranean Region, were used. The effect of each weather data on ET0 was also investigated by utilizing three different input scenarios with various combinations of input variables. On the whole, the RM5Tree provided the best results (Nash and Sutcliffe efficiency, NES > 0.997) followed by the MLPNN (NES > 0.990), and M5Tree (NES > 0.945) in modeling daily ET0. The SR was observed as the most effective input parameter on ET0 which was followed by the T and RH. However, the findings of the third modeling scenario revealed that taking into account of all variables would considerably increase models’ accuracies for the three stations.
Ozgur Kisi; Behrooz Keshtegar; Mohammad Zounemat-Kermani; Salim Heddam; Nguyen-Thoi Trung. Modeling reference evapotranspiration using a novel regression-based method: radial basis M5 model tree. Theoretical and Applied Climatology 2021, 145, 639 -659.
AMA StyleOzgur Kisi, Behrooz Keshtegar, Mohammad Zounemat-Kermani, Salim Heddam, Nguyen-Thoi Trung. Modeling reference evapotranspiration using a novel regression-based method: radial basis M5 model tree. Theoretical and Applied Climatology. 2021; 145 (1-2):639-659.
Chicago/Turabian StyleOzgur Kisi; Behrooz Keshtegar; Mohammad Zounemat-Kermani; Salim Heddam; Nguyen-Thoi Trung. 2021. "Modeling reference evapotranspiration using a novel regression-based method: radial basis M5 model tree." Theoretical and Applied Climatology 145, no. 1-2: 639-659.
The article proposes a new efficient two-stage approach for damage localization and quantification in shell structures using a modal flexibility sensitivity-based damage indicator abbreviated as MFBDI and a recently developed parameter-free optimization algorithm named golden ratio optimization method (GROM). In the first stage, the damage indicator MFBDI is employed to localize possible damage elements in the monitored shell structure. These possible damage elements also help define the search space of optimization problem in the next step. In the second stage, the GROM as a robust optimization solver is implemented to update the finite element (FE) model of the shell structure for refined localization of damage and quantification of its severity. The accuracy and efficiency of the proposed two-stage approach are demonstrated by two numerical simulation examples including a hypar shell and a spherical shell. The simultaneous influences of spatially-incomplete and inaccurate vibration data on damage prediction results are also taken into consideration. The obtained results reveal that the proposed approach can provide an efficient and accurate damage localization and quantification procedure for the studied shell structures.
D. Dinh-Cong; T. Nguyen-Thoi. A new efficient two-stage method for damage localization and quantification in shell structures. Applied Soft Computing 2021, 108, 107468 .
AMA StyleD. Dinh-Cong, T. Nguyen-Thoi. A new efficient two-stage method for damage localization and quantification in shell structures. Applied Soft Computing. 2021; 108 ():107468.
Chicago/Turabian StyleD. Dinh-Cong; T. Nguyen-Thoi. 2021. "A new efficient two-stage method for damage localization and quantification in shell structures." Applied Soft Computing 108, no. : 107468.
The estimation of the failure probability for complex systems is a crucial issue for sustainability. Reliability analysis methods are needed to be developed to provide accurate estimations of the safety levels for the complex systems and structures of today. In this paper, a novel hybrid framework for the reliability analysis of engineering systems and structures is extended to reduce the computational burden. The proposed hybrid framework is named as SVR–CFORM and consists of coupling two parts: the first is an enhanced first-order reliability method (FORM) using nonlinear conjugate map (CFORM); the second is an artificial intelligence technique called support vector regression (SVR). The conjugate FORM (CFORM) is adaptively formulated to improve the robustness of the original iterative FORM algorithm, whereas the SVR technique is used to enhance the efficiency of the reliability analysis by reducing the computational burden. The performance of the proposed SVR–CFORM formulation is compared in terms of efficiency and robustness with several FORM formulas (i.e. HL–RF, directional stability transformation method, conjugate HL–RF and finite step length) through different numerical/structural reliability examples. Results indicate that the proposed SVR–CFORM formulation is more accurate and efficient than other reliability methods. Based on the comparative analysis results, the SVR technique can highly reduce the computational costs and accurately model the response of complex performance functions, while the iterative CFORM formulation found to provide stable and robust reliability index results compared to the others reliability methods.
Behrooz Keshtegar; Mohamed El Amine Ben Seghier; Enrico Zio; José A.F.O. Correia; Shun-Peng Zhu; Nguyen-Thoi Trung. Novel efficient method for structural reliability analysis using hybrid nonlinear conjugate map-based support vector regression. Computer Methods in Applied Mechanics and Engineering 2021, 381, 113818 .
AMA StyleBehrooz Keshtegar, Mohamed El Amine Ben Seghier, Enrico Zio, José A.F.O. Correia, Shun-Peng Zhu, Nguyen-Thoi Trung. Novel efficient method for structural reliability analysis using hybrid nonlinear conjugate map-based support vector regression. Computer Methods in Applied Mechanics and Engineering. 2021; 381 ():113818.
Chicago/Turabian StyleBehrooz Keshtegar; Mohamed El Amine Ben Seghier; Enrico Zio; José A.F.O. Correia; Shun-Peng Zhu; Nguyen-Thoi Trung. 2021. "Novel efficient method for structural reliability analysis using hybrid nonlinear conjugate map-based support vector regression." Computer Methods in Applied Mechanics and Engineering 381, no. : 113818.
Pervious concrete (PC) has been widely used to construct concrete pavements and to increase the permeable surfaces all over the world. With the boost in the use of PC, it is necessary to make it more environmental-friendly and cost efficient. This study investigates employing recycled concrete aggregate (RCA) and pozzolanic additives as a partial replacement (PR) of natural coarse aggregate (NCA) and Portland cement, respectively. For this purpose, the NCA was replaced with 10%, 25%, 50% and 100% RCA and the Portland cement was replaced with 10%, 25% and 50% pumice used in combination with 1–3% nano-clay (NC). The compressive and flexural strengths, void content, density, and permeability of concrete were evaluated. Moreover, the effect of adding three different types of fibers including steel fiber (STF), macro-fiber (MF), and waste plastic fiber (WPF) at volume fractions of 1% and 2% on the properties of concrete was studied. A total number of 7791 specimens from 371 mixtures were cast and tested. Using RCA decreased density, compressive strength (CS) (up to 58%) and flexural strength (FS) (up to 64%) and increased the void content and permeability (up to 15%) of concrete. The use of pumice generally reduced the early-age strength of concrete; however, using 10–25% pumice increased the mechanical strength at 90 days. Incorporating 1–3% NC also had positive effects on the strength properties and led to a minor reduction in permeability. STF performed better than MF and WPF, and adding 1% STF, MF, and WPF increased the 180-day FS of RC25 by 78.9%, 67.4% and 37.1%, respectively. The effectiveness of fibers declined with the increase in RCA content, which could be related to the poor compaction of concrete. According to the test results, the 90-day CS of mix RC50Pu25 with 2% STF and mix RC100Pu10NC1 was equal to the control mix. Therefore, it sounds that it is a feasible approach to significantly reduce the consumption of NCA and cement by using specific dosages of the other materials used in this study.
Peyman Mehrabi; Mahdi Shariati; Kamyar Kabirifar; Majid Jarrah; Haleh Rasekh; Nguyen Thoi Trung; Ali Shariati; Soheil Jahandari. Effect of pumice powder and nano-clay on the strength and permeability of fiber-reinforced pervious concrete incorporating recycled concrete aggregate. Construction and Building Materials 2021, 287, 122652 .
AMA StylePeyman Mehrabi, Mahdi Shariati, Kamyar Kabirifar, Majid Jarrah, Haleh Rasekh, Nguyen Thoi Trung, Ali Shariati, Soheil Jahandari. Effect of pumice powder and nano-clay on the strength and permeability of fiber-reinforced pervious concrete incorporating recycled concrete aggregate. Construction and Building Materials. 2021; 287 ():122652.
Chicago/Turabian StylePeyman Mehrabi; Mahdi Shariati; Kamyar Kabirifar; Majid Jarrah; Haleh Rasekh; Nguyen Thoi Trung; Ali Shariati; Soheil Jahandari. 2021. "Effect of pumice powder and nano-clay on the strength and permeability of fiber-reinforced pervious concrete incorporating recycled concrete aggregate." Construction and Building Materials 287, no. : 122652.
This paper proposes an intelligent multi-objective optimization approach using the deep feedforward neural network (DNN) integrated with the speed-constrained multi-objective particle swarm optimization (SMPSO) to give the so-called DNN-SMPSO algorithm for solving multi-objective optimization problems of two-dimensional functionally graded (2D-FG) beams under a static load and free vibration. In the proposed approach, a high accurate DNN integrated with an intelligent sampling technique is used as a surrogate model to replace time-consuming numerical models in predicting objectives and constraints during the optimization process. Meanwhile, the SMPSO algorithm is utilized to search a set of Pareto-optimal solutions which show the best trade-off solutions of the required objectives. The ceramic volume fraction values at control points defined by the isogeometric analysis (IGA) framework are taken into account as continuous design variables and input parameters of the DNN model while the objectives and constraints are considered as output signals. In order to avoid the overfitting phenomena and speed up the training process of the DNN model, the state-of-the-art dropout and mini-batch techniques are applied. Additionally, various activation functions, optimizers, and hyper-parameters such as number of hidden layers and hidden units of the DNN model are surveyed. The accuracy, efficiency, and applicability of the proposed method are illustrated through two different multi-objective optimization examples of the 2D-FG beams with various boundary conditions. Optimal results obtained by the DNN-SMPSO method are compared with those of other methods to investigate the reliability of the proposed method. The optimal material distribution of the 2D-FG beams is described by two-dimensional Non-Uniform Rational B-spline (2D-NURBS) basis functions. Through the obtained numerical results, the DNN-SMPSO shows its accuracy, effectiveness, and capability in solving multi-objective optimization problems of engineering structures, especially in aspect of saving the computational cost. In addition, the attained optimal material distribution is useful for the 2D-FG beam fabrication.
Tam T. Truong; Jaehong Lee; T. Nguyen-Thoi. Multi-objective optimization of multi-directional functionally graded beams using an effective deep feedforward neural network-SMPSO algorithm. Structural and Multidisciplinary Optimization 2021, 63, 2889 -2918.
AMA StyleTam T. Truong, Jaehong Lee, T. Nguyen-Thoi. Multi-objective optimization of multi-directional functionally graded beams using an effective deep feedforward neural network-SMPSO algorithm. Structural and Multidisciplinary Optimization. 2021; 63 (6):2889-2918.
Chicago/Turabian StyleTam T. Truong; Jaehong Lee; T. Nguyen-Thoi. 2021. "Multi-objective optimization of multi-directional functionally graded beams using an effective deep feedforward neural network-SMPSO algorithm." Structural and Multidisciplinary Optimization 63, no. 6: 2889-2918.
The dynamic condensation method has been recognized as an effective alternative for structural damage identification using spatially-incomplete modal measurements. However, comparative studies of different dynamic condensation techniques applied to the subject of structural damage identification have been scarcely found, especially for composite structures. In this regard, we conduct a comparative study of six typical dynamic condensation techniques utilized for addressing damage identification problems of composite plates made of functionally graded materials (FGM) and functionally graded carbon nanotube-reinforced composite (FG-CNTRC) materials. Firstly, the six techniques consisting of Guyan’s method, Kidder’s method, Neumann series expansion-based second-order model reduction (NSEMR-II) method, improved reduced system (IRS) method, iterated IRS (IIRS) method, and iterative order reduction (IOR) method are reviewed. Then, their performance for reduced Eigen and optimization-damage identification problems are evaluated by studying two numerical examples of FGM plate and FG-CNTRC plate. For solving the optimization-damage identification problem of plate structures, the article proposes to use a hybrid global–local algorithm, Manta Ray Foraging Optimization—Sequential Quadratic Programming (MRFO-SQP), where the MRFO algorithm is utilized for global exploration and the SQP algorithm is used for the local searching process. The comparative study indicates that the IOR technique is the best dynamic condensation technique and is effective for addressing the structural damage identification problems when comparing with the other five techniques. It is also found that the damage identification approach based on the hybrid MRFO–SQP algorithm combined with the IOR technique can archive the high accuracy and low computational cost for damage localization and quantification.
D. Dinh-Cong; Tam T. Truong; T. Nguyen-Thoi. A comparative study of different dynamic condensation techniques applied to multi-damage identification of FGM and FG-CNTRC plates. Engineering with Computers 2021, 1 -25.
AMA StyleD. Dinh-Cong, Tam T. Truong, T. Nguyen-Thoi. A comparative study of different dynamic condensation techniques applied to multi-damage identification of FGM and FG-CNTRC plates. Engineering with Computers. 2021; ():1-25.
Chicago/Turabian StyleD. Dinh-Cong; Tam T. Truong; T. Nguyen-Thoi. 2021. "A comparative study of different dynamic condensation techniques applied to multi-damage identification of FGM and FG-CNTRC plates." Engineering with Computers , no. : 1-25.
This study constructs and verifies a new statistical meta based-model to predict tunnel-boring machine (TBM) performance, namely, polynomial chaos expansion (PCE). To test the validity of the proposed PCE, two well-known mathematical models, namely, response surface method (RSM) and multivariate adaptive regression spline (MARS) were developed. According to the results, it can be found that the PCE model, with a coefficient of determination (R2 ) of 0.843, was superior in comparison with the RSM and MARS models as well as those formerly presented in the literature for the same database and rock conditions. Abbreviations: ANFIS: Adaptive Neuro-Fuzzy Inference System; ANN: Artificial Neural Networks; AR: Advance Rate; BI: Rock Brittleness; BTS: Brazilian Tensile Strength; CP: Cutterhead Power; CT: Cutterhead Torque; d: Modified Agreement Index; DNN: Deep Neural Networks; DPW: Distance between Planes of Weakness; ICA: Imperialist Competitive Algorithm; MAE: Mean Absolute Error; MARS: Multivariate Adaptive Regression Spline; NSE: Modified Nash and Sutcliffe Efficiency; NTNU: Norwegian Institute of Technology; PCE: Polynomial Chaos Expansion; PR: Penetration Rate; PSI: Point Strength Index; PSO: Particle Swarm Optimisation; R2: Coefficient of Determination; RF: Random Forests; RMR: Rock Mass Rating; RMSE: Root Mean Square Error; RQD: Rock Quality Designation; RSM: Response Surface Method; RSR: Rock Structure Rating; SE: Specific Energy; SVR: Support Vector Regression; TBM: Tunnel-Boring Machine; TF: Thrust Force; UCS: Uniaxial Compressive Strength; WZ: Weathering Zone; α: Planes Of weakness.
Behrooz Keshtegar; Mahdi Hasanipanah; Troung Nguyen-Thoi; Saffet Yagiz; Hassan Bakhshandeh Amnieh. Potential efficacy and application of a new statistical meta based-model to predict TBM performance. International Journal of Mining, Reclamation and Environment 2021, 35, 471 -487.
AMA StyleBehrooz Keshtegar, Mahdi Hasanipanah, Troung Nguyen-Thoi, Saffet Yagiz, Hassan Bakhshandeh Amnieh. Potential efficacy and application of a new statistical meta based-model to predict TBM performance. International Journal of Mining, Reclamation and Environment. 2021; 35 (7):471-487.
Chicago/Turabian StyleBehrooz Keshtegar; Mahdi Hasanipanah; Troung Nguyen-Thoi; Saffet Yagiz; Hassan Bakhshandeh Amnieh. 2021. "Potential efficacy and application of a new statistical meta based-model to predict TBM performance." International Journal of Mining, Reclamation and Environment 35, no. 7: 471-487.
Accurate prediction of the ultimate shear capacity of reinforced concrete shear walls (RCSWs) is essential for robust design of buildings under seismic and wind loads. However, the shear capacity of RCSWs depends on multiple complex design variables characterized by diverse geometric and materials properties. Thus, a powerful modeling framework is required. In this paper, a hybrid artificial intelligence model is proposed for predicting the ultimate shear capacity of RCSWs named artificial neural network (ANN) coupled with adaptive harmony search optimization (AHS) algorithm. Different statistical metrics were used to compare the performances of the ANN model coupled with AHS (ANN-AHS) to three existing empirical relations and two ANN models combined with harmony search (ANN-HS) and global-best harmony search (ANN-GHS). Results show that the proposed ANN-AHS achieved superior performance in modelling the shear strength of RCSWs compared to ANN-HS and ANN-GHS models. The soft-computing models have proven to be more accurate than existing empirical relations.
Behrooz Keshtegar; Moncef L. Nehdi; Reza. Kolahchi; Nguyen-Thoi Trung; Mansour Bagheri. Novel hybrid machine leaning model for predicting shear strength of reinforced concrete shear walls. Engineering with Computers 2021, 1 -12.
AMA StyleBehrooz Keshtegar, Moncef L. Nehdi, Reza. Kolahchi, Nguyen-Thoi Trung, Mansour Bagheri. Novel hybrid machine leaning model for predicting shear strength of reinforced concrete shear walls. Engineering with Computers. 2021; ():1-12.
Chicago/Turabian StyleBehrooz Keshtegar; Moncef L. Nehdi; Reza. Kolahchi; Nguyen-Thoi Trung; Mansour Bagheri. 2021. "Novel hybrid machine leaning model for predicting shear strength of reinforced concrete shear walls." Engineering with Computers , no. : 1-12.
The accurate design-oriented model for concrete confined with fiber-reinforced polymer (FRP) is important to provide safe design of this composite system. In this paper, the response surface model (RSM) is coupled with support vector regression (SVR) for developing a novel hybrid model, namely RSM-SVR, with the aim of predicting the ultimate condition of FRP-confined concrete. Predictions obtained by the proposed model were compared with those by six empirical models and two data-driven models of RSM and SVR for database containing 780-test column results with circular cross section. Statistical analysis reveals that the proposed RSM-SVR model predicts the compressive strength and corresponding axial strain of the concrete confined with FRPs more accurately in comparison with the existing models. The results also show that RSM-SVR and SVR models provide stable predictions of strength and strain enhancement ratios for lateral confining ratio of >1 while the other models exhibit chaotic model error. The high accuracy and stable predictions by the proposed model are achieved based on its high flexibility and robustness in capturing the effect of lateral confining pressure as the interaction between the concrete core and FRP jacket in comparison with the existing models.
Behrooz Keshtegar; Aliakbar Gholampour; Duc-Kien Thai; Osman Taylan; Nguyen-Thoi Trung. Hybrid regression and machine learning model for predicting ultimate condition of FRP-confined concrete. Composite Structures 2021, 262, 113644 .
AMA StyleBehrooz Keshtegar, Aliakbar Gholampour, Duc-Kien Thai, Osman Taylan, Nguyen-Thoi Trung. Hybrid regression and machine learning model for predicting ultimate condition of FRP-confined concrete. Composite Structures. 2021; 262 ():113644.
Chicago/Turabian StyleBehrooz Keshtegar; Aliakbar Gholampour; Duc-Kien Thai; Osman Taylan; Nguyen-Thoi Trung. 2021. "Hybrid regression and machine learning model for predicting ultimate condition of FRP-confined concrete." Composite Structures 262, no. : 113644.
The aim of this study is to develop a novel computer-aided method for the prediction of the deflection of reinforced concrete beams (DRCB) under concentrated loads. To this end, in the present work, a Levenberg–Marquardt-based backpropagation novel neural network model, optimized by the whale optimization algorithm (WOA), called WOA-LMBPNN, has been developed. Specifically, a neural network, using the Levenberg–Marquardt backpropagation training algorithm with multiple hidden layers, was optimized by the WOA, aiming to obtain higher accuracy in predicting DRCB. For the training of the models, 120 experiments with the geometrical and mechanical properties of concrete beams were compiled using were used as the input parameters. Seven datasets with different number of input variables were investigated to evaluate the effect of the input variables on DRCB. For comparison purposes, another swarm optimization algorithm (i.e., particle swarm optimization—PSO) was also used to optimize the LMBPNN model (i.e., PSO-LMBPNN model). The results obtained by the PSO-LMBPNN and WOA-LMBPNN models are then compared based on the different datasets. Finally, the results revealed the effective role of the WOA, as well as the efficiency and robustness of the new hybrid WOA-LMBPNN model in predicting DRCB.
Jue Zhao; Hoang Nguyen; Trung Nguyen-Thoi; Panagiotis G. Asteris; Jian Zhou. Improved Levenberg–Marquardt backpropagation neural network by particle swarm and whale optimization algorithms to predict the deflection of RC beams. Engineering with Computers 2021, 1 -23.
AMA StyleJue Zhao, Hoang Nguyen, Trung Nguyen-Thoi, Panagiotis G. Asteris, Jian Zhou. Improved Levenberg–Marquardt backpropagation neural network by particle swarm and whale optimization algorithms to predict the deflection of RC beams. Engineering with Computers. 2021; ():1-23.
Chicago/Turabian StyleJue Zhao; Hoang Nguyen; Trung Nguyen-Thoi; Panagiotis G. Asteris; Jian Zhou. 2021. "Improved Levenberg–Marquardt backpropagation neural network by particle swarm and whale optimization algorithms to predict the deflection of RC beams." Engineering with Computers , no. : 1-23.
The phase-field theory is a well-known mathematical model for solving interface problems, including crack problems in fracture mechanics. In this study, the formula is derived by variational approaches based on the Reissner-Mindlin plate kinematics and the multi-phase-field theory for simulation of the buckling phenomenon in cracked laminates. Phase-field parameters are defined independently in different plies of laminate to capture the crack behavior of each ply. Simulation is carried out to numerically investigate the stiffness reduction and buckling behavior of transverse cracked laminated composite plates. This paper focuses on the consideration of laminated composite plates, which have a crack in each layer. Therefore, this work is more complicated than the case of the plate has one crack throughout the plate thickness. The significant advancement of the phase-field approach for laminated composite plates with complex crack geometries is demonstrated.
Duc Hong Doan; Thom Van Do; Nguyen Xuan Nguyen; Pham Van Vinh; Nguyen Thoi Trung. Multi-phase-field modelling of the elastic and buckling behaviour of laminates with ply cracks. Applied Mathematical Modelling 2021, 94, 68 -86.
AMA StyleDuc Hong Doan, Thom Van Do, Nguyen Xuan Nguyen, Pham Van Vinh, Nguyen Thoi Trung. Multi-phase-field modelling of the elastic and buckling behaviour of laminates with ply cracks. Applied Mathematical Modelling. 2021; 94 ():68-86.
Chicago/Turabian StyleDuc Hong Doan; Thom Van Do; Nguyen Xuan Nguyen; Pham Van Vinh; Nguyen Thoi Trung. 2021. "Multi-phase-field modelling of the elastic and buckling behaviour of laminates with ply cracks." Applied Mathematical Modelling 94, no. : 68-86.
In this research paper, as initial endeavors, the vibrational responses of functionally graded carbon nanotube-reinforced composite (FG-CNTRC) nanoplates taking into account the effect of nonlocal parameter and strain gradient coefficient are investigated. The study aims at developing mathematical modeling via an analytical solution to FG-CNTRC nanoplate structure with allowance for the nonlocal strain gradient effect. The four types of CNT distribution are used and compared in the context of the vibration of nanoplate in the presence of the small length scale effects, namely the (a) UD, (b) FG-V, (c) FG-O, and (d) FG-X. Some theoretical equations based on the first-order shear deformation plate theory (FSDT) are presented to provide a lucid understanding of how the small length-scale influences the FG-CNTRC nanoplate. For the vibrational analysis of a nanoplate, which is simply supported boundary condition, Navier solutions are obtained. Also, in contrast to earlier studies, an analytical approach is used to establish the governing equations of the FG-CNTRC nanoplate. Some specific numerical examples are given and compared with the results presented in the literature. In the section of numerical results, the influence of the nonlocal parameter, strain gradient coefficient, geometric parameters and vibrational modes on the non-dimensional natural frequency are investigated and discussed in detail. These could be useful to analysts and designers to estimate the fundamental natural frequencies in each of the four CNT distributions that the FG-CNTRC nanoplate possesses.
Pham Toan Thang; Phuong Tran; T. Nguyen-Thoi. Applying nonlocal strain gradient theory to size-dependent analysis of functionally graded carbon nanotube-reinforced composite nanoplates. Applied Mathematical Modelling 2021, 93, 775 -791.
AMA StylePham Toan Thang, Phuong Tran, T. Nguyen-Thoi. Applying nonlocal strain gradient theory to size-dependent analysis of functionally graded carbon nanotube-reinforced composite nanoplates. Applied Mathematical Modelling. 2021; 93 ():775-791.
Chicago/Turabian StylePham Toan Thang; Phuong Tran; T. Nguyen-Thoi. 2021. "Applying nonlocal strain gradient theory to size-dependent analysis of functionally graded carbon nanotube-reinforced composite nanoplates." Applied Mathematical Modelling 93, no. : 775-791.
The accurate result of heuristic models combined by social inspired optimization methods is interesting issue for optimizations of hierarchical stiffened shells (HSS). In this paper, six heuristic combined by social-inspired optimization is compared for both ability and accuracy in optimization of load-carrying capacities of HSS. A three level optimization method is employed as (1) explicit dynamic method to provide the train database of optimization model, (2) six heuristic models including response surface method (RSM), multivariate adaptive regression splines (MARS), Kriging, artificial neural network, radial basis function neural network (RBFNN), and support vector regression (SVR) for approximating load-carrying capacity of HSS and (3) an improved partial swarm optimization (IPSO) to search for the optimum results of HSS. In IPSO as optimizer operator, a random adjusting process is presented to update the positions of particles using best particle by a dynamical bandwidth generated by normal standard distribution. Optimization performances for accuracy and ability of six heuristic models coupled by IPSO are compared for optimum model as maximum load-carrying capacity under mass constraint of HSS. The SVR, Kriging and RSM combined by IPSO can be introduced as efficient and accurate modeling-based optimization method to evaluate the optimum design of HSS. The best optimal result is obtained by RBFNN while the worst optimum result is given using MARS among other models.
Shun-Peng Zhu; Behrooz Keshtegar; Kuo Tian; Nguyen-Thoi Trung. Optimization of Load-Carrying Hierarchical Stiffened Shells: Comparative Survey and Applications of Six Hybrid Heuristic Models. Archives of Computational Methods in Engineering 2021, 28, 4153 -4166.
AMA StyleShun-Peng Zhu, Behrooz Keshtegar, Kuo Tian, Nguyen-Thoi Trung. Optimization of Load-Carrying Hierarchical Stiffened Shells: Comparative Survey and Applications of Six Hybrid Heuristic Models. Archives of Computational Methods in Engineering. 2021; 28 (5):4153-4166.
Chicago/Turabian StyleShun-Peng Zhu; Behrooz Keshtegar; Kuo Tian; Nguyen-Thoi Trung. 2021. "Optimization of Load-Carrying Hierarchical Stiffened Shells: Comparative Survey and Applications of Six Hybrid Heuristic Models." Archives of Computational Methods in Engineering 28, no. 5: 4153-4166.
This paper deals with the nonlinear buckling and post-buckling of sandwich cylindrical panels with non-uniform porous core and functionally graded face sheets. The sandwich cylindrical panels are subjected to axial compression load. Two cases of boundary conditions are considered. Based on the Donnell shell theory with von Kármán geometrical nonlinearity in conjunction, the governing equations are derived. To validate the proposed method, comparisons are made with available results and show good agreements. The effects of various panel geometrical characteristics, boundary conditions, porosity parameters, the thickness of the porous core, and material parameters are investigated.
Do Quang Chan; Pham Van Hoan; Nguyen Thoi Trung; Le Kha Hoa; Duong Thanh Huan. Nonlinear buckling and post-buckling of imperfect FG porous sandwich cylindrical panels subjected to axial loading under various boundary conditions. Acta Mechanica 2021, 232, 1163 -1179.
AMA StyleDo Quang Chan, Pham Van Hoan, Nguyen Thoi Trung, Le Kha Hoa, Duong Thanh Huan. Nonlinear buckling and post-buckling of imperfect FG porous sandwich cylindrical panels subjected to axial loading under various boundary conditions. Acta Mechanica. 2021; 232 (3):1163-1179.
Chicago/Turabian StyleDo Quang Chan; Pham Van Hoan; Nguyen Thoi Trung; Le Kha Hoa; Duong Thanh Huan. 2021. "Nonlinear buckling and post-buckling of imperfect FG porous sandwich cylindrical panels subjected to axial loading under various boundary conditions." Acta Mechanica 232, no. 3: 1163-1179.