Application of GMDH model for predicting the fundamental period of regular RC infilled frames

Viet-Linh Tran,Seung-Eock Kim 국제구조공학회 2022 Steel and Composite Structures, An International J Vol.42 No.1

The fundamental period (FP) is one of the most critical parameters for the seismic design of structures. In the reinforced concrete (RC) infilled frame, the infill walls significantly affect the FP because they change the stiffness and mass of the structure. Although several formulas have been proposed for estimating the FP of the RC infilled frame, they are often associated with high bias and variance. In this study, an efficient soft computing model, namely the group method of data handling (GMDH), is proposed to predict the FP of regular RC infilled frames. For this purpose, 4026 data sets are obtained from the open literature, and the quality of the database is examined and evaluated in detail. Based on the cleaning database, several GMDH models are constructed and the best prediction model, which considers the height of the building, the span length, the opening percentage, and the infill wall stiffness as the input variables for predicting the FP of regular RC infilled frames, is chosen. The performance of the proposed GMDH model is further underscored through comparison of its FP predictions with those of existing design codes and empirical models. The accuracy of the proposed GMDH model is proven to be superior to others. Finally, explicit formulas and a graphical user-friendly interface (GUI) tool are developed to apply the GMDH model for practical use. They can provide a rapid prediction and design for the FP of regular RC infilled frames.

Improving the axial compression capacity prediction of elliptical CFST columns using a hybrid ANN-IP model

Viet-Linh Tran,Yun Jang,Seung-Eock Kim 국제구조공학회 2021 Steel and Composite Structures, An International J Vol.39 No.3

This study proposes a new and highly-accurate artificial intelligence model, namely ANN-IP, which combines an interior-point (IP) algorithm and artificial neural network (ANN), to improve the axial compression capacity prediction of elliptical concrete-filled steel tubular (CFST) columns. For this purpose, 145 tests of elliptical CFST columns extracted from the literature are used to develop the ANN-IP model. In this regard, axial compression capacity is considered as a function of the column length, the major axis diameter, the minor axis diameter, the thickness of the steel tube, the yield strength of the steel tube, and the compressive strength of concrete. The performance of the ANN-IP model is compared with the ANN-LM model, which uses the robust Levenberg–Marquardt (LM) algorithm to train the ANN model. The comparative results show that the ANN-IP model obtains more magnificent precision ( 2 = 0.983, = 59.963 kN, 20− = 0.979) than the ANN-LM model ( 2 = 0.938, = 116.634 kN, 20− = 0.890). Finally, a new Graphical User Interface (GUI) tool is developed to use the ANN-IP model for the practical design. In conclusion, this study reveals that the proposed ANN-IP model can properly predict the axial compression capacity of elliptical CFST columns and eliminate the need for conducting costly experiments to some extent.

Application of GMDH model for predicting the fundamental period of regular RC infilled frames

Viet-Linh Tran,Seung-Eock Kim 국제구조공학회 2022 Steel and Composite Structures, An International J Vol.42 No.1

The fundamental period (FP) is one of the most critical parameters for the seismic design of structures. In the reinforced concrete (RC) infilled frame, the infill walls significantly affect the FP because they change the stiffness and mass of the structure. Although several formulas have been proposed for estimating the FP of the RC infilled frame, they are often associated with high bias and variance. In this study, an efficient soft computing model, namely the group method of data handling (GMDH), is proposed to predict the FP of regular RC infilled frames. For this purpose, 4026 data sets are obtained from the open literature, and the quality of the database is examined and evaluated in detail. Based on the cleaning database, several GMDH models are constructed and the best prediction model, which considers the height of the building, the span length, the opening percentage, and the infill wall stiffness as the input variables for predicting the FP of regular RC infilled frames, is chosen. The performance of the proposed GMDH model is further underscored through comparison of its FP predictions with those of existing design codes and empirical models. The accuracy of the proposed GMDH model is proven to be superior to others. Finally, explicit formulas and a graphical user-friendly interface (GUI) tool are developed to apply the GMDH model for practical use. They can provide a rapid prediction and design for the FP of regular RC infilled frames.

Investigating the Behavior of Steel Flush Endplate Connections at Elevated Temperatures Using FEM and ANN

Viet-Linh Tran 한국강구조학회 2022 International Journal of Steel Structures Vol.22 No.5

This paper investigates the moment-rotation (M-θ) behavior of fl ush endplate (FEP) connections at elevated temperatures using the fi nite element (FE) method and an artifi cial neural network (ANN). Firstly, a three-dimensional nonlinear FE model of fl ush endplate connection is carried out and verifi ed with the tests conducted by others using ABAQUS. Then, an extensive database is created by varying several parameters (i.e., the endplate thickness, the bolt row distance, the pitch of bolts, the gage distance, the outer edge distance, the number of bolt rows, the bolt diameter, and the material properties) to get insight into the infl uences of each parameter on the connection behaviors at elevated temperatures. Additionally, a simple and accurate model with two shape parameters for the M-θ relationship of semi-rigid fl ush endplate connections at elevated temperatures is proposed based on this database. Accordingly, two shape parameters and the ultimate moment (M u ) of the model are determined using the ANN model. Finally, the performance of the proposed model is verifi ed and has a good agreement with various test data.

A new empirical formula for prediction of the axial compression capacity of CCFT columns

Viet-Linh Tran,Duc Kien Thai,김승억 국제구조공학회 2019 Steel and Composite Structures, An International J Vol.33 No.2

This paper presents an efficient approach to generate a new empirical formula to predict the axial compression capacity (ACC) of circular concrete-filled tube (CCFT) columns using the artificial neural network (ANN). A total of 258 test results extracted from the literature were used to develop the ANN models. The ANN model having the highest correlation coefficient (<i>R</i>) and the lowest mean square error (<i>MSE</i>) was determined as the best model. Stability analysis, sensitivity analysis, and a parametric study were carried out to estimate the stability of the ANN model and to investigate the main contributing factors on the ACC of CCFT columns. Stability analysis revealed that the ANN model was more stable than several existing formulae. Whereas, the sensitivity analysis and parametric study showed that the outer diameter of the steel tube was the most sensitive parameter. Additionally, using the validated ANN model, a new empirical formula was derived for predicting the ACC of CCFT columns. Obviously, a higher accuracy of the proposed empirical formula was achieved compared to the existing formulae.

JAYA-GBRT model for predicting the shear strength of RC slender beams without stirrups

Viet-Linh Tran,Jin-Kook Kim 국제구조공학회 2022 Steel and Composite Structures, An International J Vol.44 No.5

Shear failure in reinforced concrete (RC) structures is very hazardous. This failure is rarely predicted and may occur without any prior signs. Accurate shear strength prediction of the RC members is challenging, and traditional methods have difficulty solving it. This study develops a JAYA-GBRT model based on the JAYA algorithm and the gradient boosting regression tree (GBRT) to predict the shear strength of RC slender beams without stirrups. Firstly, 484 tests are carefully collected and divided into training and test sets. Then, the hyperparameters of the GBRT model are determined using the JAYA algorithm and 10-fold cross-validation. The performance of the JAYA-GBRT model is compared with five well-known empirical models. The comparative results show that the JAYA-GBRT model ( 2 = 0.982, = 9.466 kN, = 6.299 kN, = 1.018, and = 0.116) outperforms the other models. Moreover, the predictions of the JAYA-GBRT model are globally and locally explained using the Shapley Additive exPlanation (SHAP) method. The effective depth is determined as the most crucial parameter influencing the shear strength through the SHAP method. Finally, a Graphic User Interface (GUI) tool and a web application (WA) are developed to apply the JAYA-GBRT model for rapidly predicting the shear strength of RC slender beams without stirrups.

Synthesis of a Novel Fluorescent Cyanide Chemosensor Based on Photoswitching Poly(pyrene-1-ylmethyl-methacrylate-randommethyl methacrylate-random-methacrylate spirooxazine)

Hoan Minh Tran,Tam Huu Nguyen,Viet Quoc Nguyen,Phuc Huynh Tran,Linh Duy Thai,Thuy Thu Truong,Le-Thu T. Nguyen,Ha Tran Nguyen 한국고분자학회 2019 Macromolecular Research Vol.27 No.1

The photoswitching poly(pyrene-1-ylmethyl-methacrylate-random-methyl methacrylate-random-methacrylate spirooxazine) was synthesized via atom transfer radical polymerization and characterized by proton nuclear magnetic resonance (1H NMR), gel permeation chromatography (GPC), Fourier transform infrared (FTIR) spectroscopy, UV-visible spectroscopy, and differential scanning calorimetry (DSC). The obtained copolymer exhibited the capability of erasable and rewritable photoimaging, making it a potential candidate for optical data storage materials. Moreover, the copolymer also showed the sensing ability for cyanide anions effect in aqueous solutions.

Influence of Crystallite Size on Magnetocaloric Effect and Critical Behavior La_0.7Sr_0.3Mn_0.92Co_0.08O₃ Nanoparticles

Tran Dang Thanh,Dinh Chi Linh,Le Viet Bau,Thi Anh Ho,Tien Van Manh,The-Long Phan,Seong-Cho Yu IEEE 2015 IEEE transactions on magnetics Vol.51 No.1

<P>Four samples of La<SUB>0.7</SUB>Sr<SUB>0.3</SUB>Mn<SUB>0.92</SUB>Co<SUB>0.08</SUB>O<SUB>3</SUB> (LSMCO) with different crystallite sizes were prepared by the combination of solid-state reaction and mechanical milling methods. Based on isothermal magnetization data, M(H), temperature dependences of magnetic entropy change, ΔS<SUB>m</SUB>T, of the samples under a magnetic field change of 10 kOe were calculated. The maximum values of magnetic entropy change (|ΔS<SUB>max</SUB>|) at room temperature are in the range of 0.9-1.4 J · kg<SUP>-1</SUP> · K<SUP>-1</SUP>, corresponding to ferromagnetic (FM)-paramagnetic phase transition. In addition, M<SUP>2</SUP> versus H/M curves at temperatures around TC prove the samples exhibiting a second-order magnetic phase transition. The critical exponents β, γ, and δ were determined using the modified Arrott plot method and critical isotherm analysis. Here, these exponent values are located in between those expected for the mean-field theory and 3-D Heisenberg model. It means the coexistence of short-range and long-range FM interactions in LSMCO nanoparticles.</P>

Magnetic and magnetocaloric properties in second-order phase transition La_1−xK_xMnO₃ and their composites

Thanh, Tran Dang,Linh, Dinh Chi,Yen, Pham Duc Huyen,Bau, Le Viet,Ky, Vu Hong,Wang, Zhihao,Piao, Hong-Guang,An, Nguyen Manh,Yu, Seong-Cho Elsevier 2018 PHYSICA B-CONDENSED MATTER - Vol.532 No.-

<P><B>Abstract</B></P> <P>In this work, we present a detailed study on the magnetic properties and the magnetocaloric effect (MCE) of La<SUB>1−x</SUB>K<SUB>x</SUB>MnO<SUB>3</SUB> compounds with <I>x</I>=0.05–0.2. Our results pointed out that the Curie temperature (<I>T</I> <SUB>C</SUB>) could be controlled easily from 213 to 306K by increasing K-doping concentration (<I>x</I>) from 0.05 to 0.2. In the paramagnetic region, the inverse of the susceptibility can be analyzed by using the Curie-Weiss law, <I>χ</I>(<I>T</I>)=<I>C</I>/(<I>T</I>−<I>θ</I>). The results have proved an existence of ferromagnetic clusters at temperatures above <I>T</I> <SUB>C</SUB>. Based on Banerjee's criteria, we also pointed out that the samples are the second-order phase transition materials. Their magnetic entropy change was calculated by using the Maxwell relation and a phenomenological model. Interestingly, the samples with <I>x</I>=0.1–0.2 exhibit a large MCE in a range of 282–306K, which are suitable for room-temperature magnetic refrigeration applications. The composites obtained from single phase samples (<I>x</I>=0.1–0.2) exhibit the high relative cooling power values in a wide temperature range. From the viewpoint of the refrigerant capacity, the composites formed out of La<SUB>1−x</SUB>K<SUB>x</SUB>MnO<SUB>3</SUB> will become more useful for magnetic refrigeration applications around room-temperature.</P>

Na-doped La_0.7Ca_0.3MnO₃ compounds exhibiting a large magnetocaloric effect near room temperature

Chi Linh, Dinh,Thi Ha, Nguyen,Huu Duc, Nguyen,Giang Nam, Le Huu,Bau, Le Viet,Manh An, Nguyen,Yu, Seong-Cho,Dang Thanh, Tran Elsevier 2018 PHYSICA B-CONDENSED MATTER - Vol.532 No.-

<P><B>Abstract</B></P> <P>In this work, we have investigated the magnetic properties and the magnetocaloric effect of La<SUB>0.7−x</SUB>Na<SUB>x</SUB>Ca<SUB>0.3</SUB>MnO<SUB>3</SUB> compounds, which were prepared by a conventional solid-state reaction technique. The Rietveld refinement results suggested that the samples are single phase belonging to an orthorhombic structure (space group <I>Pnma</I>). Analyzing temperature dependence of magnetization <I>M</I>(<I>T</I>) revealed that the Curie temperature (<I>T</I> <SUB>C</SUB>) increases with increasing Na content (<I>x</I>). Their <I>T</I> <SUB>C</SUB> value is found to be 260–298K for <I>x</I>=0.0–0.1, respectively. Base on <I>M</I>(<I>T</I>) data measured at different applied magnetic fields (<I>H</I>), temperature dependence of magnetic entropy change Δ<I>S</I> <SUB>m</SUB>(<I>T</I>) data for all the samples was calculated by using a phenomenological model. In the vicinity of <I>T</I> <SUB>C,</SUB> -Δ<I>S</I> <SUB>m</SUB>(<I>T</I>) curve reaches a maximum value (denoted as |Δ<I>S</I> <SUB>max</SUB>|), which gradually increases with increasing <I>H</I>. Under 12kOe, the value of |Δ<I>S</I> <SUB>max</SUB>| is in a range of 1.47–5.19J/kgK corresponding to the relative cooling power RCP=57.12–75.88J/kg. Applied the universal master curve method for the magnetic entropy change, we concluded that Na-doped in La<SUB>0.7−x</SUB>Na<SUB>x</SUB>Ca<SUB>0.3</SUB>MnO<SUB>3</SUB> compounds leads to modification the nature of the magnetic phase transition from the first- to the second-order.</P>