• Smart Structures and Systems
• /
• v.30 no.6
• /
• pp.557-569
• /
• 2022

Vibration-based structural health monitoring (SHM) is crucial for the dynamic maintenance of civil building structures to protect property security and the lives of the public. Analyzing these vibrations with modern artificial intelligence and deep learning (DL) methods is a new trend. This paper proposed an unsupervised deep learning method based on a convolutional autoencoder (CAE), which can overcome the limitations of conventional supervised deep learning. With the convolutional core applied to the DL network, the method can extract features self-adaptively and efficiently. The effectiveness of the method in detecting damage is then tested using a benchmark model. Thereafter, this method is used to detect damage and instant disaster events in a rubber bearing-isolated gymnasium structure. The results indicate that the method enables the CAE network to learn the intact vibrations, so as to distinguish between different damage states of the benchmark model, and the outcome meets the high-dimensional data distribution characteristics visualized by the t-SNE method. Besides, the CAE-based network trained with daily vibrations of the isolating layer in the gymnasium can precisely recover newly collected vibration and detect the occurrence of the ground motion. The proposed method is effective at identifying nonlinear variations in the dynamic responses and has the potential to be used for structural condition assessment and safety warning.

• Journal of the Korean Wood Science and Technology
• /
• v.50 no.6
• /
• pp.490-498
• /
• 2022

The current study aimed to verify the image segmentation ability of rays in tangential thin sections of conifers using artificial intelligence technology. The applied model was Mask region-based convolutional neural network (Mask R-CNN) and softwoods (viz. Picea jezoensis, Larix gmelinii, Abies nephrolepis, Abies koreana, Ginkgo biloba, Taxus cuspidata, Cryptomeria japonica, Cedrus deodara, Pinus koraiensis) were selected for the study. To take digital pictures, thin sections of thickness 10-15 ㎛ were cut using a microtome, and then stained using a 1:1 mixture of 0.5% astra blue and 1% safranin. In the digital images, rays were selected as detection objects, and Computer Vision Annotation Tool was used to annotate the rays in the training images taken from the tangential sections of the woods. The performance of the Mask R-CNN applied to select rays was as high as 0.837 mean average precision and saving the time more than half of that required for Ground Truth. During the image analysis process, however, division of the rays into two or more rays occurred. This caused some errors in the measurement of the ray height. To improve the image processing algorithms, further work on combining the fragments of a ray into one ray segment, and increasing the precision of the boundary between rays and the neighboring tissues is required.

• Korean journal of aerospace and environmental medicine
• /
• v.30 no.1
• /
• pp.18-24
• /
• 2020

In 2019, the Aerospace Medical Association of Korea celebrated its 30th anniversary. On the other side of the world, it was also the 62nd anniversary of Russian launch Sputnik 1 of the world's first artificial satellite on October 4, 1957. In additionally, the world, especially the United States was shocked, when on November 3, 1957, Sputnik 2 blasted into Earth orbit with a dog named "Laika"; it was the role of veterinarian's activities for aerospace medical research and exploration. Veterinarians (Vets) are responsible for the health of all the animals for aerospace medicine whether on the ground or in space. Vets can enhance animal and public health and this knowledge of Vets and astronauts can extend their mission durations, go to nearby Earth Asteroids, Mars and other heavenly bodies to study their living and non-living characteristics. This review article is the brief history of the original growth of the veterinarian's activities for the aerospace medical research, in order to stimulate future strategies for improvements in the space life sciences and exploration.

• Geomechanics and Engineering
• /
• v.34 no.3
• /
• pp.303-316
• /
• 2023

With the development and utilization of urban underground space, the artificial ground freezing technology has been widely used in the construction of underground engineering in soft soil areas. The mechanical properties of soft clay changed greatly after freezing and thawing, which affected the seismic performance of underground structures. In this paper, a series of triaxial tests were carried out to study the dynamic response of the freezing-thawing clay under the seismic load considering different dynamic stress amplitudes and different confining pressures. The reduction factor of dynamic shear stress was determined to correct the amplitude of the seismic load. The deformation development mode, the stress-strain relationship and the energy dissipation behavior of the soft clay under the seismic load were analyzed. An empirical model for predicting accumulative plastic strain was proposed and validated considering the loading times, the confining pressures and the dynamic stress amplitudes. The relevant research results can provide a theoretical reference to the seismic design of underground structures in soft clay areas.

• Geomechanics and Engineering
• /
• v.31 no.3
• /
• pp.291-304
• /
• 2022

Maximum shear modulus (G_max or G₀) is an important soil property useful for many engineering applications, such as the analysis of soil-structure interactions, soil stability, liquefaction evaluation, ground deformation and performance of seismic design. In the current study, bender element (BE) tests are used to evaluate the effect of the void ratio, effective confining pressure, grading characteristics (D₅₀, C_u and C_c), anisotropic consolidation and initial fabric anisotropy produced during specimen preparation on the G_max of sand-gravel mixtures. Based on the tests results, an empirical equation is proposed to predict G_max in granular soils, evaluated by the experimental data. The artificial neural network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) models were also applied. Coefficient of determination (R²) and Root Mean Square Error (RMSE) between predicted and measured values of G_max were calculated for the empirical equation, ANN and ANFIS. The results indicate that all methods accuracy is high; however, ANFIS achieves the highest accuracy amongst the presented methods.

• Journal of Environmental Science International
• /
• v.32 no.12
• /
• pp.861-881
• /
• 2023

This study investigated the flora of the Wang-ime oreum located on Seogwipo-si, Jeju-do, to basic data for the Jeju Island plant distribution. A total of 366 taxa were found with 94 families, 240 genera, 358 species, 1 subspecies, 6 varieties, and 1 forma. The floristic target species in Korea appeared as 2 taxa of grade V, 13 taxa of grade IV, 46 taxa of grade III, 5 taxa of grade II, and 49 taxa of grade I. There were 14 taxa for naturalized plants. The different plant life forms that appeared were large ground plants (M)(54 taxa), small land plants (N) (52 taxa), epiphyte (E) (6 taxa), indicator plants (Ch) (4 taxa), and semi-aquatic plants (H) (168 taxa), There were 34 and 48 taxa of plants(G) and annuals (Th) respectively. From these results, Wang-ime oreum, which is adjacent to ranches and grasslands, has little artificial interference, as more plants are distributed, and fewer naturalized plants are found than in Suwolbong and Dangsanbong. Accordingly, each oreum plays an important role in the flora of Jeju-do, thus a management plan tailored to the characteristics of the volcano is necessary.

• Structural Engineering and Mechanics
• /
• v.90 no.2
• /
• pp.189-208
• /
• 2024

Artificial intelligence is one of the efficient methods that can be developed to simulate nonlinear behavior and predict the response of building structures. In this regard, an adaptive method based on optimization algorithms is used to train the TSK model of the fuzzy inference system to estimate the seismic behavior of building structures based on analytical data. The optimization algorithm is implemented to determine the parameters of the TSK model based on the minimization of prediction error for the training data set. The adaptive training is designed on the feedback of the results of previous time steps, in which three training cases of 2, 5, and 10 previous time steps were used. The training data is collected from the results of nonlinear time history analysis under 100 ground motion records with different seismic properties. Also, 10 records were used to test the inference system. The performance of the proposed inference system is evaluated on two 3 and 20-story models of nonlinear steel moment frame. The results show that the inference system of the TSK model by combining the optimization method is an efficient computational method for predicting the response of nonlinear structures. Meanwhile, the multi-vers optimization (MVO) algorithm is more accurate in determining the optimal parameters of the TSK model. Also, the accuracy of the results increases significantly with increasing the number of previous steps.

• Steel and Composite Structures
• /
• v.50 no.4
• /
• pp.443-458
• /
• 2024

The construction industry, one of the biggest producers of greenhouse emissions, is under a lot of pressure as a result of growing worries about how climate change may affect local communities. Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues connected to the manufacture of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete, which might be used in lieu of traditional concrete to reduce CO₂ emissions in the building industry. In the present work, the compressive strength (f_c) of GPC is calculated using random forests regression (RFR) methodology where natural zeolite (NZ) and silica fume (SF) replace ground granulated blast-furnace slag (GGBFS). From the literature, a thorough set of experimental experiments on GPC samples were compiled, totaling 254 data rows. The considered RFR integrated with artificial hummingbird optimization (AHA), black widow optimization algorithm (BWOA), and chimp optimization algorithm (ChOA), abbreviated as ARFR, BRFR, and CRFR. The outcomes obtained for RFR models demonstrated satisfactory performance across all evaluation metrics in the prediction procedure. For R² metric, the CRFR model gained 0.9988 and 0.9981 in the train and test data set higher than those for BRFR (0.9982 and 0.9969), followed by ARFR (0.9971 and 0.9956). Some other error and distribution metrics depicted a roughly 50% improvement for CRFR respect to ARFR.

• Journal of Korean Association for Spatial Structures
• /
• v.24 no.2
• /
• pp.83-90
• /
• 2024

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.

• Korean Journal of Agricultural Science
• /
• v.50 no.3
• /
• pp.511-526
• /
• 2023

In this study, three types of structures-stepped gabion retaining walls, vertical gabion retaining walls, and parapets-were installed on the dam floor crest to prevent the overflow of deteriorative homogeneous reservoirs. The acceleration response, displacement behavior, and pore water pressure ratio behavior were compared and evaluated using shaking-table model tests. The experimental conditions were set to 0.154 g in consideration of the domestic standard and the seismic acceleration range according to the magnitude of the earthquake, and the input waveform was applied with Pohang, Gongen, and artificial earthquake waves. The acceleration response according to the design ground acceleration increased as the height of the embankment increased, and the observed value were larger in the range of 1.1 to 2.1 times the input acceleration for all structures. The horizontal and vertical displacements exhibited maximum values on the upstream slope, and the embankment was evaluated as stable and included within the allowable range for all waveforms. The settlement ratio considering the similarity law exhibited the least change in the case of the parapet structure. The amplification ratio was 1.1 to 1.5 times in all structures, with the largest observed in the dam crest. The maximum excess pore water pressure ratio was in the range of 0.010 - 0.021, and the liquefaction evaluation standard was within 1.0, which was considered very stable.