• Structural Engineering and Mechanics
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• v.68 no.5
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• pp.575-589
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• 2018

It is well known that the incidence angle of seismic excitation has an influence on the structural response of buildings, and this effect can be more significant in the case of near-fault signals. However, current seismic codes do not include detailed requirements regarding the direction of application of the seismic action and they have only recently introduced specific provisions about near-fault earthquakes. Thus, engineers have the task of evaluating all the relevant directions or the most critical conditions case by case, in order to avoid underestimating structural demand. To facilitate the identification of the most critical incidence angle, this paper presents a procedure which makes use of a two-degree of freedom model for representing a building. The proposed procedure makes it possible to avoid the extensive computational effort of multiple dynamic analyses with varying angles of incidence of ground motion excitation, which is required if a spatial multi-degree of freedom model is used for representing a building. The procedure is validated through the analysis of two case studies consisting of an eight- and a six-storey reinforced concrete frame building, selected as representative of existing structures located in Italy. A set of 124 near-fault ground motion records oriented along 8 incidence angles, varying from 0 to 180 degrees, with increments of 22.5 degrees, is used to excite the structures. Comparisons between the results obtained with detailed models of the two structures and the proposed procedure are used to show the accuracy of the latter in the prediction of the most critical angle of seismic incidence.

• Structural Engineering and Mechanics
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• v.69 no.1
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• pp.11-20
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• 2019

The biaxial failure mechanism of transversally bedding concrete layers was numerically simulated using a sophisticated two-dimensional discrete element method (DEM) implemented in the particle flow code (PFC2D). This numerical modelling code was first calibrated by uniaxial compression and Brazilian testing results to ensure the conformity of the simulated numerical model's response. Secondly, 21 rectangular models with dimension of $54mm{\times}108mm$ were built. Each model contains two transversely bedding layers. The first bedding layer has low mechanical properties, less than mechanical properties of intact material, and second bedding layer has high mechanical properties, more than mechanical properties of intact material. The angle of first bedding layer, with weak mechanical properties, related to loading direction was $0^{\circ}$, $15^{\circ}$, $30^{\circ}$, $45^{\circ}$, $60^{\circ}$, $75^{\circ}$ and $90^{\circ}$ while the angle of second layer, with high mechanical properties, related to loading direction was $90^{\circ}$, $105^{\circ}$, $120^{\circ}$, $135^{\circ}$, $150^{\circ}$, $160^{\circ}$ and $180^{\circ}$. Is to be note that the angle between bedding layer was $90^{\circ}$ in all bedding configurations. Also, three different pairs of the thickness were chosen in models, i.e., 5 mm/10 mm, 10 mm/10 mm and 20 mm/10 mm. The result shows that in all configurations, shear cracks develop between the weaker bedding layers. Shear cracks angel related to normal load change from $0^{\circ}$ to $90^{\circ}$ with increment of $15^{\circ}$. Numbers of shear cracks are constant by increasing the bedding thickness. It's to be noted that in some configuration, tensile cracks develop through the intact area of material model. There is not any failure in direction of bedding plane interface with higher strength.

• The Journal of the Korea Contents Association
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• v.19 no.5
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• pp.64-73
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• 2019

This study analyzed the efficiency by using the DEA model to evaluate the competitiveness of specialty construction firms. The target of the analysis is 300 firms with continuous data from 2010 to 2017 as listed and externally listed firms. Significant analysis results are as follows. First, the efficiency of specialty construction firms is lower than that of general construction industry. Second, efficiency by type was highest in facilities construction and dismantling work. This shows that the relevant industries such as reinforced concrete works and scaffolding and demolition work are relatively efficient in specialty construction industry. Third, the efficiency of specialty construction industry is affected by economic fluctuations. When the construction industry is in the expansion phase, the efficiency value is high and the efficiency value is low in the down phase. Finally, in the scale profitability analysis, specialty construction industry was most analyzed by DRS. This means that it is effective to scale down for the efficiency of the firm.

• Journal of Korean Society on Water Environment
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• v.35 no.5
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• pp.432-438
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• 2019

The purpose of this study is to predict Trophic Diatom Index (TDI) in tributaries of the Han River watershed using the random forest algorithm. The one year (2017) and supplied aquatic ecology health data were used. The data includes water quality(BOD, T-N, $NH_3-N$, T-P, $PO_4-P$, water temperature, DO, pH, conductivity, turbidity), hydraulic factors(water width, average water depth, average velocity of water), and TDI score. Seven factors including water temperature, BOD, T-N, $NH_3-N$, T-P, $PO_4-P$, and average water depth are selected by the Correlation Feature Selection. A TDI prediction model was generated by random forest using the seven factors. To evaluate this model, 2017 data set was used first. As a result of the evaluation, $R^2$, % Difference, NSE(Nash-Sutcliffe Efficiency), RMSE(Root Mean Square Error) and accuracy rate show that this model is compatible with predicting TDI. To be more concrete, $R^2$ is 0.93, % Difference is -0.37, NSE is 0.89, RMSE is 8.22 and accuracy rate is 70.4%. Also, additional evaluation using data set more than 17 times the measured point was performed. The results were similar when the 2017 data set were used. The Wilcoxon Signed Ranks Test shows there was no statistically significant difference between actual and predicted data for the 2017 data set. These results can specify the elements which probably affect aquatic ecology health. Also, these will provide direction relative to water quality management for a watershed that must be continuously preserved.

• Journal of Korean Critical Care Nursing
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• v.14 no.1
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• pp.1-13
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• 2021

Purpose : Based on Quint's theory and the relevant literature, this study constructed a structural equation model for explaining and predicting end-of-life care performance in clinical nurses. Methods : A self-administered questionnaire was used to collect data from 265 nurses between September 1 and September 30, 2016. The data were analyzed using SPSS ver. 21 and AMOS ver. 21. Results : The goodness of fit of the modified model was found to be relatively satisfactory (χ2=114.82, Nomed χ2(χ2/df)=2.44, SRMR=.06, GFI=.94, AGFI=.89, CFI=.95, TLI=.91, RMSEA=.07). End-of-life care performance was affected by the attitudes toward nursing care of the dying, working unit, and death anxiety. The attitudes toward such care had the highest effect on end-of-life care performance. Conclusion : The results suggest that end-of-life care performance is directly and indirectly affected by attitudes toward nursing care of the dying, participation in end-of-life care education, working unit, death perception, and death anxiety. To improve clinical nurses' end-of-life care performance, effective programs to promote death anxiety and attitudes toward nursing care of the dying need to be developed. In addition, hospital nursing organizations should attempt to produce concrete measures for death anxiety and terminal care attitudes in clinical nurses.

• Journal of the Korean Recycled Construction Resources Institute
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• v.10 no.3
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• pp.235-242
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• 2022

The percolation threshold of the CNT-reinforced cement paste is closely related to the optimal CNT amount to maximize the sensing ability of self-sensing concrete. However, the percolation threshold has various values depending on the cement, CNT, and water-to-cement ratio used. In this study, a percolation simulation model was proposed to predict the percolation threshold of the CNT-reinforced cement paste. The proposed model can simulate the percolation according to the amount of CNT using only the properties of CNT and cement, and for this, the concept of the number of aggregated CNT particles was used. The percolation simulation consists of forming a pre-hydrated cement paste model, random dispersion of CNTs, and percolation investigation. The simulation used CNT-reinforced cement paste with a water-cement ratio of 0.4 to 0.6, and the simulated percolation threshold point showed high accuracy with a simulation residual ratio of up to 7.5 % compared to the literature results.

• Smart Structures and Systems
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• v.30 no.6
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• pp.601-612
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• 2022

The prediction of structural mechanical behaviors is vital important to early perceive the abnormal conditions and avoid the occurrence of disasters. Especially for underground engineering, complex geological conditions make the structure more prone to disasters. Aiming at solving the problems existing in previous studies, such as incomplete consideration factors and can only predict the continuous performance, the deep attention fused temporal convolution network (DATCN) is proposed in this paper to predict the spatial mechanical behaviors of structure, which integrates both the temporal effect and spatial effect and realize the cross-time prediction. The temporal convolution network (TCN) and self-attention mechanism are employed to learn the temporal correlation of each monitoring point and the spatial correlation among different points, respectively. Then, the predicted result obtained from DATCN is compared with that obtained from some classical baselines, including SVR, LR, MLP, and RNNs. Also, the parameters involved in DATCN are discussed to optimize the prediction ability. The prediction result demonstrates that the proposed DATCN model outperforms the state-of-the-art baselines. The prediction accuracy of DATCN model after 24 hours reaches 90 percent. Also, the performance in last 14 hours plays a domain role to predict the short-term behaviors of the structure. As a study case, the proposed model is applied in an underwater shield tunnel to predict the stress variation of concrete segments in space.

• Journal of the Korean Geosynthetics Society
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• v.21 no.2
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• pp.11-19
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• 2022

In this paper, we propose a GoogleNet transfer learning and CNN-LSTM combination method to improve the time-series prediction performance for crack detection using crack data captured inside the sewer pipes. LSTM can solve the long-term dependency problem of CNN, so spatial and temporal characteristics can be considered at the same time. The predictive performance of the proposed method is excellent in all test variables as a result of comparing the RMSE(Root Mean Square Error) for time series sections using the crack data inside the sewer pipe. In addition, as a result of examining the prediction performance at the time of data generation, the proposed method was verified that it is effective in predicting crack detection by comparing with the existing CNN-only model. If the proposed method and experimental results obtained through this study are utilized, it can be applied in various fields such as the environment and humanities where time series data occurs frequently as well as crack data of concrete structures.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.2
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• pp.165-173
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• 2023

The data management and the evaluation of defects in the bridge are generally conducted based on inspection and diagnosis data, including the exterior damage map and defect quantity table prepared by periodic inspection. Since most of these data are written in 2D-based documents and are difficult to digitize in a standardized manner, it is challenging to utilize them beyond the defined functionality. This study proposed methods to efficiently build a BIM (Building Information Modeling)-based bridge damage model from raw data of inspection report and to manage and utilize the damage information linking to bridge model through the spread sheet data generated by COBie (Construction Operations Building Information Exchange). In addition, a method to conduct the condition assessment of defects in bridge was proposed based on an automatic evaluation process using digitized bridge member and damage information. The proposed methods were tested using superstructure of PSC-I girder concrete bridge, and the efficiency and effectiveness of the methods were verified.

• Computers and Concrete
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• v.31 no.5
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• pp.405-417
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• 2023

The submerged floating tunnel (SFT) is tethered by mooring lines anchored to the seabed, therefore, the structural integrity of the anchor should be sensitively managed. Despite their importance, reaction forces cannot be simply measured by attaching sensors or load cells because of the structural and environmental characteristics of the submerged structure. Therefore, we propose an effective method for estimating the reaction forces at the seabed anchor of a submerged floating tunnel using a structural pattern model. First, a structural pattern model is established to use the correlation between tunnel motion and anchor reactions via a deep learning algorithm. Once the pattern model is established, it is directly used to estimate the reaction forces by inputting the tunnel motion data, which can be directly measured inside the tunnel. Because the sequential characteristics of responses in the time domain should be considered, the long short-term memory (LSTM) algorithm is mainly used to recognize structural behavioral patterns. Using hydrodynamics-based simulations, big data on the structural behavior of the SFT under various waves were generated, and the prepared datasets were used to validate the proposed method. The simulation-based validation results clearly show that the proposed method can precisely estimate time-series reactions using only acceleration data. In addition to real-time structural health monitoring, the proposed method can be useful for forensics when an unexpected accident or failure is related to the seabed anchors of the SFT.