Jaehong Lee

Jaehong Lee
Sejong University | sejong · Deep Learning Architecture Research Center

Ph.D.

About

292
Publications
55,908
Reads
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6,765
Citations
Additional affiliations
March 1998 - present
Sejong University
Position
  • Professor (Full)
June 1992 - July 1993
Virginia Polytechnic Institute and State University
Position
  • Research Scientist

Publications

Publications (292)
Article
Full-text available
Surrogate modeling techniques are widely employed in solving constrained expensive black-box optimization problems. Therein, Kriging is among the most popular surrogates in which the trend function is considered as a constant mean. However, it also encounters several challenges related to capturing the overall trend with a relatively limited number...
Article
Thin cylindrical shells are widespread in structural engineering, such as civil engineering structures, oil rigs, oil pipes, rocket hull and aircraft fuselages. With the development of materials science, the behavior of the cylindrical shell structures made of advanced materials has attracted many scientists. This paper proposed an analytical appro...
Article
Because the proportion between the compressive strength of high‐performance concrete (HPC) and its composition is highly nonlinear, more advanced regression methods are demanded to obtain better results. Super learner models, which are based on several ensemble methods including random forest regression (RFR), an adaptive boosting (AdaBoost), gradi...
Article
This paper proposes an effective one-dimensional convolutional gated recurrent unit neural network (1D-CGRU) by combining a one-dimensional convolutional neural network (1D-CNN) and a gated recurrent unit neural network (GRU) for real-time structural damage detection (SDD) based on time-series vibration signals measured from a large number of accel...
Article
Porous structures with controllable mechanical and hydrodynamic characteristics are prospective candidates for coastal engineering applications. In this paper, a novel emerged porous breakwater based on triply periodic minimal surface (TPMS) cellular structure is proposed. Mechanical behaviour of TPMS structures is analyzed by conducting uniaxial c...
Article
Full-text available
Analytical paradigms have limited conventional form-finding methods of tensegrities; therefore, an innovative approach is urgently needed. This paper proposes a new form-finding method based on state-of-the-art deep learning techniques. One of the statical paradigms, a force density method, is substituted for trained deep neural networks to obtain...
Article
Full-text available
Isotropic ultra-thin shells or membranes, as well as cable–membrane structures, cannot resist loads at the initial state and always require a form-finding process to reach the steady state. After this stage, they can work in a pure membrane state and quickly experience large deflection behavior, even with a small amplitude of load. This paper aims...
Article
Full-text available
In this paper, an efficient deep unsupervised learning (DUL)-based framework is proposed to directly perform the design optimization of truss structures under multiple constraints for the first time. Herein, the members’ cross-sectional areas are parameterized using a deep neural network (DNN) with the middle spatial coordinates of truss elements a...
Article
This study proposes an adaptive 3-stage hybrid teaching-based differential evolution (ATDE) algorithm to deal with sizing and layout frequency-constrained truss designs more efficiently. The optimization process is divided into 3 stages, and suitable mutation formulae are applied adaptively in each stage. A novel operator inspired by the teaching p...
Article
A straightforward and effective scheme for adaptive initialization is proposed and coupled with the Linearly reduced population size Success History based Adaptive Differential Evolution (LSHADE) for solving constrained optimization problems. The novel contributions are in the initialization phase: (i) exploiting the information of problem dimensio...
Article
In this study, a robust and simple unsupervised neural network (NN) framework is proposed to perform the geometrically nonlinear analysis of inelastic truss structures. The core idea is to employ the NN to directly estimate nonlinear structural responses without utilizing any time-consuming incremental-iterative algorithms as those done in standard...
Article
Daylight analysis is essential in building design to ensure indoor environment quality, including health and thermal comfort vis-à-vis energy. It is a repeating and time-consuming process of design options. Several studies conducted machine learning models to accurately predict daylight performance in particular design situations. Therefore, develo...
Article
In recent years, the tensegrity structures have been studied and applied extensively in the engineering field because of their unique topology, shape, and stability. An effective force density-informed neural network (FDINN) approach for performing a robust force finding procedure is constructed in this paper by utilizing a fully connected neural n...
Article
We in this paper propose an efficient and vigorous approach based on flexible polygonal meshes for solving stress-constrained topology optimization problems involving both compressible and nearly incompressible materials. The core idea is to employ a polygonal composite finite element technique to deal with volumetric locking phenomena in nearly in...
Article
Nowadays, there are a lot of iterative algorithms which have been proposed for nonlinear problems of solid mechanics. The existing biggest drawback of iterative algorithms is the requirement of numerous iterations and computation to solve these problems. This can be found clearly when the large or complex problems with thousands or millions of degr...
Article
This study presents vibration and buckling optimization of thin-walled functionally graded (FG) beams with bi-symmetric I-shape and channel-section. Material properties are assumed to vary through-the-contour direction by a non-monotonic function. A piece-wise cubic Hermite interpolation is used to estimate the volume fractions of the constituent p...
Article
This article aims to introduce a multigrid preconditioned conjugate gradient (MPCG)-oriented topology optimization (TO) methodology using multiple bi-directional functionally graded (BFG) models for the first time. For that purpose, the MPCG paradigm is integrated into the TO procedure to more effectively and quickly resolve linear algebraic system...
Article
Full-text available
As initial endeavors, this paper presents an in-depth study to investigate the influence of nanoscale parameters on bending and free vibration responses of functionally graded carbon nanotube-reinforced composite (FG-CNTRC) nanoshells with double curvature. Carbon nanotubes (CNTs) are considered as reinforcements that are distributed across the she...
Article
Design optimization of geometrically nonlinear structures is well known as a computationally expensive problem by using incremental-iterative solution techniques. To handle the problem effectively the optimization algorithm needs to ensure that the trade-off between the computational time and the quality of the solution is found. In this study, a d...
Article
Recent advances in chip and sensor technology allow a large amount of data to be collected for serving structural damage detection (SDD). However, how to efficiently utilize and transform these complex sensing data into useful engineering information has remained many challenges. One of the major challenges in SDD using sensing data is how to effec...
Article
In this article, we explore a three-dimensional solid isogeometric analysis (3D-IGA) approach based on a nonlocal elasticity theory to investigate size effects on natural frequency and critical buckling load for multi-directional functionally graded (FG) nanoshells. The multi-directional FG material uses a power law rule with three power exponent i...
Article
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 dir...
Article
This article introduces a simple and effective adaptive surrogate model to structural reliability analysis using deep neural network (DNN). In this paradigm, initial design of experiments (DoEs) are randomly selected from a given Monte Carlo Simulation (MCS) population to build the global approximate model of performance function (PF). More importa...
Article
In the paper, an analysis of thin-walled functionally graded straight and curved beams for general non-uniform polygonal cross-sections has been introduced for vibration problems. Higher order beam theory that has fully taken into account major deformations, e.g. in-plane distortion, out-of-plane warping, is adopted by means of beam frame modal app...
Article
The purpose of this study is to present a quasi-three-dimensional (quasi-3D) shear deformation theory for static bending and free vibration analyses of porous sandwich functionally graded (FG) plates with graphene nanoplatelets (GPLs) reinforcement. In addition, we propose a novel sandwich plate model with various outstanding features in terms of s...
Article
Due to complexities from the interaction between steel tube and concrete filling of concrete-filled steel tubular (CFST) columns, their strengths are very complicated, which is a highly nonlinear relation with material strengths and geometry. Categorical gradient Boosting (CatBoost), which is advanced boosting machine, is presented to solve the pro...
Article
Full-text available
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)...
Article
The operations of metaheuristic optimization algorithms depend heavily on the setting of control parameters. Therefore the addition of adaptive control parameter has been widely studied and shown to enhance the problem flexibility and overall performance of the algorithms. This paper proposes Q-learning Differential Evolution (qlDE) algorithm, an a...
Article
This paper presents a novel combination of Generative Adversarial Networks (GANs) and Clustering Analysis (CA) for topology optimization. Based on the results from the topology analysis, new data are generated by the GANs and Deep Convolutional GANs (DCGANs). K-means Clustering Analysis is employed to select optimized valid data with the minimum co...
Article
The buckling and lateral buckling of thin-walled functionally graded (FG) open-section beams with various types of material distributions are studied. The approach is based on assumption that the volume fraction of particles varies through the contour direction according to a power law. The governing buckling equation and a finite element method ha...
Article
Application of nano/micro structures has become popular in a wide range of advanced engineering systems in recent years. For a better insight, this paper presents an intensive numerical study on the static and dynamic responses of smart functionally graded microplates with graphene platelets (GPLs) reinforcement under concurrently both mechanical a...
Article
In this study, we have explored the structural damage detection of truss structures using the state-of-the-art deep learning techniques. The surrogate models, deep neural networks, are used to train the knowledge of the patterns in the response of the undamaged and the damaged structures. The limited sensors are then used to collect the response fr...
Article
The paper adequately presents an analysis of thin-walled functionally graded straight and curved beams for general non-uniform polygonal cross-sections. In order to mathematically model a complex beam which property information in both material distribution and geometric continuity need to be collected from multiple patches through blade thickness...
Article
For the first time, an investigation on the shape and material optimization for buckling behavior of functionally graded (FG) toroidal shells using differential evolution (DE) algorithm is presented in this paper. For buckling analysis, an analytical approach is used to derive governing equations, then combining with the Galerkin procedure to obtai...
Article
An investigation of free vibration of a thin-walled beams with functionally graded materials (FGMs) is presented. The mechanical properties of thin-walled beam are assumed to vary along the contour direction of the thin-walled cross section. Governing differential equations are derived by means of Hamilton's principle. A finite element model is dev...
Article
The postbuckling and geometrically nonlinear behaviors of imperfect functionally graded carbon nanotube-reinforced composite (FG-CNTRC) shells under axial compression are investigated in this paper. For the first time, a new type of instability named “snap-backward” with the presence of three limit points on the equilibrium path is proposed. A nove...
Article
Different from large deflection analyses of plates and shells, for analysis of an isotropic membrane structure a form finding procedure is first performed to find the new geometry of membrane. With this new geometry, large deflection analysis is conducted and the membrane works in a pure membrane state. In the conventionally numerical approaches fo...
Article
This paper concerns a new fabrication method and experimental studies for tensegrity systems. Numerous studies have observed fabrications of the tensegrity systems, however, full-scale tests under load control are rarely reported. In this work, we first develop a form-finding method to draw the equilibrium status with the corresponding force densit...
Article
A new methodology for making design decisions of structures using multi-material optimum topology information is presented. Multi-material analysis contributes significant applications to enhance the bearing capacity and performance of structures. A method that chooses an appropriate material combination satisfying design stiffness requirement econ...
Chapter
In this work, the shell formulation for postbuckling analysis of functionally graded carbon nanotube-reinforced composite shells based on isogeometric analysis and first-order shear deformation shell theory (FSDT) is proposed. The nonlinearity of shells is formed in the Total Lagrangian approach considering the von Karman assumption. The nonlinear...
Article
The paper is aimed at enhancing computational performance for optimizing the material distribution of tri-directional functionally graded (FG) plates. We exploit advantages of using a non-uniform rational B-spline (NURBS) basis function for describing material distribution varying through all three directions of functionally graded (FG) plates. Two...
Article
In this study, we numerically investigate static and free vibration responses of functionally graded (FG) porous plates with graphene platelets (GPLs) reinforcement using an efficient polygonal finite element method (PFEM). While the bending strain field is approximated through quadratic serendipity shape functions, the shear strain field is calcul...
Article
Effectiveness of several currently popular topology optimization methods is closely related to the number of design variables consisted of discretized finite elements. Since the number of design variables is proportional to the number of finite element meshes, a very fine discretization needs more computational cost to carry out a finite element an...
Article
The fatigue crack growth analysis of an interfacial cracked structure plays an essential role in the design process as well as the safety assessment for various engineering fields in real life. This paper proposes a novel and effective computational approach based on polygonal meshes for crack growth simulation of interfacial crack problems. Polygo...
Article
Structural damage assessment is a challenging problem of study due to lack of information in data measurement and the difficulty of extracting noisy features from the structural responses. Therefore, this paper proposes an effective deep feedforward neural networks (DFNN) method for damage identification of truss structures based on noisy incomplet...
Article
This study presents applications of the multivariate adaptive regression splines (MARS) method for predicting the ultimate loading carrying capacity (Nu) of rectangular concrete-filled steel tubular (CFST) columns subjected to eccentric loading. A database containing 141 experimental data was collected from available literature to develop the MARS...
Chapter
A recently developed adaptive hybrid evolutionary firefly algorithm (AHEFA) as a cross-breed of differential evolution (DE) approach and firefly algorithm (FA) is utilized to address inverse optimization problems in two-stage damage detection of truss structures. In the first step, the most potentially damaged elements are recognized utilizing a mo...
Article
A novel and effective artificial neural network (ANN)-differential evolution (DE) approach as an integration of ANN into DE is first introduced to the material distribution optimization of bidirectional functionally graded (BFG) beams under free vibration. In this methodology, ANN is utilized as an analyzer to predict responses of BFG beams instead...
Article
Full-text available
This paper aims at forecasting the crack propagation in risk assessment of engineering structures based on time series algorithms named “long short-term memory” and “multi-layer neural network”. The core idea is how to predict precisely the accuracy of solution of crack growth in engineering fracture structures without requiring re-modeling and re-...
Article
A novel analysis-prediction (ANP) approach for geometrically nonlinear problems of solid mechanics is for the first time proposed in this paper. The key concept of this approach is: (1) A part of equilibrium path is traced by numerical analysis; (2) Data of this part is then used for training predictive network; (3) Applying the trained network, th...
Article
This study contributes to a possibility of evaluating composite structures configuration such as steel and concrete using buckling and volume constraints based on multi-material topology optimization. A Jacobi active-phase algorithm is used to generate multiphase topology optimization. It provides a rational solution appropriated to the topology op...
Article
Full-text available
This paper introduces an automatically connected graph representation for structural topology optimization. Structural members of optimal topologies are constructed based on a graph whose each edge is represented by a B-spline curve with varying thickness. A square matrix including connective coefficients with either 0 or 1 is first proposed to aut...
Article
A reliability-based design optimization (RBDO) methodology is first proposed to simultaneously optimize material and thickness distribution of multidirectional functionally graded (MFG) plates for compliance minimization under uncertainties of design variables and system parameters. The modified sequential optimization and reliability assessment (M...
Article
In this study, an efficient damage detection technique using the differential evolution (DE) algorithm and vibration data is proposed to properly detect the locations and extents of multiple damages of truss structures. The general equilibrium equations in which the reaction forces at notes are taken into account are considered. The compatibility e...
Article
This paper investigates free vibration and dynamic responses of smart FG metal foam plate structures reinforced by graphene platelets (GPLs). We then analyze active control of FG metal foam plates with piezoelectric sensor and actuator layers. To provide numerical solution of underlying problems, we develop a computational approach based on a gener...
Article
An investigation into the postbuckling and geometrically nonlinear behaviors of functionally graded carbon nanotube-reinforced composite (FG-CNTRC) shells is carried out in this study. The discrete nonlinear equation system is established based on non-uniform rational B-Spline (NURBS) basis functions and the first-order shear deformation shell theo...
Preprint
In this study, we propose an effective numerical approach to analyse and control geometrically nonlinear responses for the functionally graded (FG) porous plates reinforced by graphene platelets (GPLs) integrated with piezoelectric layers. The basis idea is to use isogeometric analysis (IGA) based on the B\'ezier extraction and the $C^0$-type highe...
Article
The paper deals with the nonlinear buckling analysis of imperfect cylindrical shells made of porous metal foam subjected to axial compression. For the metal foam shells, porosities are dispersed by uniform, symmetric, and asymmetric distributions in the thickness direction. Using Donnell shell theory and von-Karman nonlinear kinematics, nonlinear e...
Article
The paper is aimed at improving computational cost enhanced by a new combination of deep neural network (DNN) and modified symbiotic organisms search (mSOS) algorithm for optimal material distribution of functionally graded (FG) plates. The material distribution is described by control points, in which coordinates of these points are located along...
Article
The circular cylindrical shells have been widely used in modern engineering structures, especially in the aerospace industry such as the oil pipeline, the missile, spacecraft hull, storage tanks. In recent years, functionally graded carbon nanotube composites (FG-CNTRCs) have emerged, as a promising type of composites. Due to the increasing demands...