• Steel and Composite Structures
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• v.32 no.6
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• pp.717-729
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• 2019

A horizontal cyclic test was carried out to study the seismic performance of lightweight aggregate concrete filled steel tube (LACFST). The constitutive and hysteretic model of core lightweight aggregate concrete (LAC) was proposed for finite element simulation. The stress and strain changes of the steel tube and concrete filled inside were measured in the experiment, and the failure mode, hysteresis curve, skeleton curve, and strain curve of the test specimens were obtained. The influence of axial compression ratio, diameter-thickness ratio and material strength were analysed based on finite element model. The results show that the hysteresis curve of LACFST indicated favourable ductility, energy dissipation, and seismic performance. The LACFST failed when the concrete in the bottom first crushed and the steel tube then bulged, thus axial force imposed by prestressing was proved to be feasible. The proposed constitutive model and hysteretic model of LAC under the constraint of its steel tube was reliable. The bearing capacity and ductility of the specimen increase significantly with increasing thickness of the steel tube. The bearing capacity of the member improves while the ductility and energy dissipation performance slightly decreased with the increasing strength of the steel and concrete.

• Geomechanics and Engineering
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• v.34 no.5
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• pp.577-595
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• 2023

A dynamic triaxial discrete element numerical model of lightweight soil was established using the discrete element method to study the microscopic mechanism of expanded polystyrene (EPS) particles in the soil under cyclic loading. The microscopic parameters of the discrete element model of the lightweight soil were calibrated depending on the dynamic triaxial test hysteresis curves. Based on the calibration results, the effects of the EPS particles volume ratio and amplitude on the contact force, displacement field, and velocity field of the lightweight soil under different accumulated strains were studied. The results showed that the hysteresis curves of lightweight soil exhibit nonlinearity, hysteresis, and strain accumulation. The strain accumulated in remolded soil is mainly tensile strain, and that in lightweight soil is mainly compressive strain. As the volume ratio of EPS particles increased, the contact force first increased and then decreased, and the displacement and velocity of the particles increased accordingly. With an increase in amplitude, the dynamic stress of the particle system increased, and the accumulation rate of the dynamic strain of the samples also increased. At 5% compressive strain, the contact force of the particles changed significantly and the number of particles deflected in the direction of velocity also increased considerably. These results indicated that the cemented structure of the lightweight soil began to fail at a compressive strain of 5%. Thus, a compressive strain of 5% is more reasonable than the dynamic strength failure standard of lightweight soil.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.7
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• pp.19-26
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• 2018

This study tested 25 lightweight aggregate concrete (LWAC) mixtures using the expanded bottom ash and dredged soil granules to examine the compressive strength gain of such concrete with different ages. The test parameters investigated were water-to-cement ratios and the natural sand content for the replacement of lightweight fine aggregate. The compressive strength gain rate in the basic equation specified in fib model code was experimentally determined in each mixture and then empirically formulated as a function of the water-to-cement ratio and oven-dried density of concrete. When compared with 28-day compressive strength, the tested LWAC mixtures exhibited relatively low gain ratios (0.49~0.82) at an age of 3 days whereas the gain ratios (1.16~1.41) at 91 days were higher than that (1.05~1.15) of the conventional normal-weight concrete. Thus, the fib model equations tend to overestimate the early strength gain of LWAC but underestimate the long-term strength gain. The proposed equations are in good agreement with the measured compressive strength development of LWAC at different ages, indicating that the mean and standard deviation of the normalized root mean square errors determined in each mixture are 0.101 and 0.053, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2505-2528
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• 2023

The field of facial expression recognition (FER) has been actively researched to improve human-computer interaction. In recent years, deep learning techniques have gained popularity for addressing FER, with numerous studies proposing end-to-end frameworks that stack or widen significant convolutional neural network layers. While this has led to improved performance, it has also resulted in larger model sizes and longer inference times. To overcome this challenge, our work introduces a novel lightweight model architecture. The architecture incorporates three key factors: Depth-wise Separable Convolution, Residual Block, and Attention Modules. By doing so, we aim to strike a balance between model size, inference speed, and accuracy in FER tasks. Through extensive experimentation on popular benchmark FER datasets, our proposed method has demonstrated promising results. Notably, it stands out due to its substantial reduction in parameter count and faster inference time, while maintaining accuracy levels comparable to other lightweight models discussed in the existing literature.

• Advances in concrete construction
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• v.15 no.6
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• pp.391-404
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• 2023

In this study, the modeling of the transverse connection of fully precast steel-UHPC (Ultra-High-Performance Concrete) lightweight composite bridges were conducted. The transverse connection between precast components plays a critical role in the overall performance and safety of the bridge. To achieve an accurate and reliable simulation of the interface behavior, the cohesive model in ABAQUS was employed, considering both bending-tension and compression-shear behaviors. The parameters of the cohesive model are obtained through interface bending and oblique shear tests on UHPC samples with different surface roughness. By validating the numerical simulation against actual joint tests, the effectiveness and accuracy of the proposed model in capturing the interface behavior of the fully precast steel-UHPC lightweight composite bridge were demonstrated.

• Journal of Power System Engineering
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• v.15 no.4
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• pp.31-36
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• 2011

The need for the lightweight of special vehicle trailer frame is substantially growing due to high gasoline prices and serious environmental issues. In this study, we develop a new lightweight sub-frame for large container trailers and evaluate its durability through a fatigue test. To this end, a reliable three-dimensional parametric finite element model of a sub-frame is constructed and then an optimized lightweight sub-frame is newly developed by using the Taguchi method. Next, we make a trial product of the optimized lightweight sub-frame and conduct a driving test to identify the driving load history at vulnerable areas. Finally, we evaluate the durability of the developed lightweight sub-frame through a fatigue test based on the load history.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.5 s.248
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• pp.488-495
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• 2006

The demand for the use of 3D CAD data over the Internet environment has been increased. However, transmission of 3D CAD data has delayed the communication effectiveness because of the CAD data size. Lightweight CAD file design methodology is required for rapid transmission in the distributed environment. In this paper, to derive lightweight CAD files from commercial CAD systems, a file translation system producing a native file is constructed first by using the InterOp and API of the ACIS kernel. Using the B-rep model and mesh data extracted from the native file, the lightweight CAD file with topological information is constructed as a binary file. Since the lightweight CAD file retains topological information, it is applied to the dimensional verification, digital mock-ups and visualization of CAD files. Effectiveness of the proposed lightweight CAD file is confirmed through various case studies.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.1
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• pp.102-110
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• 2022

This paper proposes a solution to the problems of manufacturing cost and processability by applying discrete material and thickness optimization (DMTO) and minimizing the use of high-strength, lightweight materials in the optimization process. A simple infant pop-up seat model was selected as the application target, and the weight reduction effect and variation in strength according to the optimization results were observed. In this study, a simplified finite element model of an infant pop-up seat frame was first constructed. The model was used to perform a static structural analysis to verify the weight and strength of each part. The D-optimal design of the experimental method was then used to observe the influence of each part on the weight and strength. This process was applied using discrete thickness optimization (DTO) (which applies high-strength, lightweight materials and optimizes only the thickness) and DMTO (which considers both the material and thickness). The DTO and DMTO results were compared to verify the design method that determines the major parts and simultaneously considers the material and thickness. Accordingly, in this study, an optimal lightweight design that satisfied the strength standards of the seat frame was derived. Furthermore, discretization parameters were used to minimize the application of high-strength, lightweight materials.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.55-67
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• 2023

Lightweight face recognition models, as one of the most popular and long-standing topics in the field of computer vision, has achieved vigorous development and has been widely used in many real-world applications due to fewer number of parameters, lower floating-point operations, and smaller model size. However, few surveys reviewed lightweight models and reimplemented these lightweight models by using the same calculating resource and training dataset. In this survey article, we present a comprehensive review about the recent research advances on the end-to-end efficient lightweight face recognition models and reimplement several of the most popular models. To start with, we introduce the overview of face recognition with lightweight models. Then, based on the construction of models, we categorize the lightweight models into: (1) artificially designing lightweight FR models, (2) pruned models to face recognition, (3) efficient automatic neural network architecture design based on neural architecture searching, (4) Knowledge distillation and (5) low-rank decomposition. As an example, we also introduce the SqueezeFaceNet and EfficientFaceNet by pruning SqueezeNet and EfficientNet. Additionally, we reimplement and present a detailed performance comparison of different lightweight models on the nine different test benchmarks. At last, the challenges and future works are provided. There are three main contributions in our survey: firstly, the categorized lightweight models can be conveniently identified so that we can explore new lightweight models for face recognition; secondly, the comprehensive performance comparisons are carried out so that ones can choose models when a state-of-the-art end-to-end face recognition system is deployed on mobile devices; thirdly, the challenges and future trends are stated to inspire our future works.

• Computers and Concrete
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• v.19 no.2
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• pp.217-226
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• 2017

This paper summarizes the results of experimental research, and artificial intelligence methods focused on determination of compressive strength of lightweight cement mortar with silica fume and fly ash after sulfate attack. The artificial neural network and the support vector machine were selected as artificial intelligence methods. Lightweight cement mortar mixtures containing silica fume and fly ash were prepared in this study. After specimens were cured in $20{\pm}2^{\circ}C$ waters for 28 days, the specimens were cured in different sulfate concentrations (0%, 1% $MgSO_4^{-2}$, 2% $MgSO_4^{-2}$, and 4% $MgSO_4^{-2}$ for 28, 60, 90, 120, 150, 180, 210 and 365 days. At the end of these curing periods, the compressive strengths of lightweight cement mortars were tested. The input variables for the artificial neural network and the support vector machine were selected as the amount of cement, the amount of fly ash, the amount of silica fumes, the amount of aggregates, the sulfate percentage, and the curing time. The compressive strength of the lightweight cement mortar was the output variable. The model results were compared with the experimental results. The best prediction results were obtained from the artificial neural network model with the Powell-Beale conjugate gradient backpropagation training algorithm.