• Journal of KIBIM
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• v.8 no.4
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• pp.41-48
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• 2018

Recent technological improvements have made abundant changes in construction industry. In specific, some technical applications, such as Building Information Modeling (BIM) opens up many possibilities. Some studies have articulated the use of BIM and its advantages in construction, but most of them are theoretical, not practical. This study is to provide an insight to such obstacles in BIM research. By investigating a real project that could utilize BIM in planning and construction phases, the authors try to investigate a possible outline of advantages in BIM implementation. The study area was set to a railway construction site in South Korea. The site covers a multiple railway tracks, stations, telecommunication facilities, infrastructure facilities, railway structures, and so numerous. In the site, the authors have identified 12 errors in 7 projects that could be prevented if BIM was utilized before the construction.

• Computers and Concrete
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• v.24 no.6
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• pp.555-560
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• 2019

During the application of shotcrete, a part of the concrete bounces back after hitting to the surface, the reinforcement or previously sprayed concrete. This rebound material is definitely not added to the mixture and considered as waste. In this study, a deep neural network model was developed to predict the rebound material during shotcrete application. The factors affecting rebound and the datasets of these parameters were obtained from previous experiments. The Long Short-Term Memory (LSTM) architecture of the proposed deep neural network model was used in accordance with this data set. In the development of the proposed four-tier prediction model, the dataset was divided into 90% training and 10% test. The deep neural network was modeled with 11 dependents 1 independent data by determining the most appropriate hyper parameter values for prediction. Accuracy and error performance in success performance of LSTM model were evaluated over MSE and RMSE. A success of 93.2% was achieved at the end of training of the model and a success of 85.6% in the test. There was a difference of 7.6% between training and test. In the following stage, it is aimed to increase the success rate of the model by increasing the number of data in the data set with synthetic and experimental data. In addition, it is thought that prediction of the amount of rebound during dry-mix shotcrete application will provide economic gain as well as contributing to environmental protection.

• Wind and Structures
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• v.26 no.2
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• pp.75-87
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• 2018

A large number of low-rise buildings experienced serious roof covering failures under strong wind while few suffered structural damage. Clay and concrete tiles are two main kinds of roof covering. For the tile roof system, few researches were carried out based on Finite Element (FE) analysis due to the difficulty in the simulation of the interface between the tiles and the roof sheathing (the bonding materials, foam or mortar). In this paper, the FE analysis of a single clay or concrete tile with foam-set or mortar-set were built with the interface simulated by the equivalent nonlinear springs based on the mechanical uplift and displacement tests, and they were expanded into the whole roof. A detailed wind tunnel test was carried out at Tongji University to acquire the wind loads on these two kinds of roof tiles, and then the test data were fed into the FE analysis. For the purpose of validation and calibration, the results of FE analysis were compared with the full-scale performance ofthe tile roofs under simulated strong wind impact through one-of-a-kind Wall of Wind (WoW) apparatus at Florida International University. The results are consistent with the WoW test that the roof of concrete tiles with mortar-set provided the highest resistance, and the material defects or improper construction practices are the key factors to induce the roof tiles' failure. Meanwhile, the staggered setting of concrete tiles would help develop an interlocking mechanism between the tiles and increase their resistance.

• Korean Journal of Construction Engineering and Management
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• v.22 no.6
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• pp.3-14
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• 2021

In the Chinese construction industry, the utilization of Building Information Modeling (BIM) aims to increase the total output of the construction industry by solving the problem of inefficient interoperability in the construction industry. In 2011, the Chinese Ministry of Housing and Urban-Rural Development despite the technical advantages of BIM and the government policy, the BIM adoption rate in China is lower than 45%. Meanwhile, as the South Korean construction industry is a step ahead of its Chinese counterpart in introducing and utilizing BIM, it is expected that BIM is more actively utilized and accepted in South Korea than in China. According to a comparative study based on the hype-cycle theory, South Korea is at a more advanced stage of introducing BIM, than in China. This study aimed to suggest how to increase BIM utilization rates in China. To this end, this study comparatively analyzed factors affecting BIM acceptance between China and South Korea. For the comparative analysis of the BIM acceptance factors between China and South Korea, literature reviews on the technology acceptance model (TAM) and BIM acceptance model were carried out, and based on that, the BIM acceptance factors were classified. Other BIM acceptance factors were also added and considered, as they reflected Chinese national characteristics and construction industry. As for the derived BIM acceptance factors, construction project participants, especially actual BIM users in China and South Korea, were targeted for the survey. A t-test using SPSS 22.00 was carried out to identify significant differences in data. Finally, based on the t-test results, this study suggested ways of improving the BIM utilization rate in China. Based on the findings, this study is expected to contribute to activating BIM adoption in the Chinese construction industry and also to set a theoretical foundation for future studies on BIM utilization in the industry.

• Geomechanics and Engineering
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• v.27 no.6
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• pp.551-560
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• 2021

Due to the low strength and high compressibility characteristics, the loess deposits tunnels are prone to large deformations and collapse. An accurate stability evaluation for loess deposits is of considerable significance in deformation control and safety work during tunnel construction. 37 groups of representative data based on real loess deposits cases were adopted to establish the stability evaluation model for the tunnel project in Yan'an, China. Physical and mechanical indices, including water content, cohesion, internal friction angle, elastic modulus, and poisson ratio are selected as index system on the stability level of loess. The data set is randomly divided into 80% as the training set and 20% as the test set. Firstly, principal component analysis (PCA) is used to convert the five index system to three linearly independent principal components X1, X2 and X3. Then, the principal components were used as input vectors for probabilistic neural network (PNN) to map the nonlinear relationship between the index system and stability level of loess. Furthermore, Leave-One-Out cross validation was applied for the training set to find the suitable smoothing factor. At last, the established model with the target smoothing factor 0.04 was applied for the test set, and a 100% prediction accuracy rate was obtained. This intelligent classification method for loess deposits can be easily conducted, which has wide potential applications in evaluating loess deposits.

• Journal of Korean Society of Steel Construction
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• v.14 no.2
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• pp.357-364
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• 2002

The study performed a series of flexural tests on Closed Cold-Formed Steel Sections for stud, joist, and roof truss. Results were compared with analytical values. Each 2.4-m long and 0.9-m wide specimen consisted of two steel beams set at 0.46 m interval. The steel beams were attached to the specimens using either plaster board or ply wood. Another specimens did not use any attachment material. Positive and negative bending tests were conducted to investigate the composite behavior, including the effects of plaster board or ply wood on the buckling behavior of steel beam. Full-scale roof truss tests were also performed to study the buckling behavior and failure mode of the truss members.

• Journal of Korean Society of Steel Construction
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• v.17 no.3 s.76
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• pp.271-283
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• 2005

This paper concerns the prediction of shear capacity, as governed by steel failure and concrete breakout failure, of set anchors installed in plain concrete. For this purpose, the methods to evaluate the shear capacity of the set anchors in concrete are summarized and the experimental data are compared with capacities by the two present methods : the method of ACI349-90 and the Concrete Capacity Design (CCD) method. (1) The constant-0.684 in the steel strength equation of set anchor was determined from shear test data at the 5 percent fractile probability. Consequently, it was concluded that the constant-0.6 and 0.5 in the steel strength equation for steel failure of ACI318-02 and EOTA were safe. The nominal shear strength of set anchor was proposed as following. $V_s=0.684 A_{se}f_{ut}$. (2) The CCD method was considered reasonable in estimating the concrete breakout strength of set anchors. In terms of the CCD method, the nominal concrete breakout strength of set anchor in shear was provided as follows; $V_b=0.609(\frac{\iota}{d_o})^{0.2}\sqrt{d_0}\sqrt{f_c}(c_1)^{1.5}$(N). (3) The CCD method was considered reasonable in estimating the concrete breakout strength for spacing of set anchors. The proposed equation was considered safe in estimating the concrete breakout strength for spacing of set anchors.

• Steel and Composite Structures
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• v.50 no.4
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• pp.443-458
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• 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 Environmental Science International
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• v.20 no.10
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• pp.1347-1355
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• 2011

In Jeju island, runoff has frequently happened when the rainfall depth is over a threshold value. To simulated this characteristic rainfall-runoff model structure has to be modified. In this study, the TRSM (Threshold Runoff Simulation Method) was developed to overcome the limitations of SWAT in applying to the hydrologic characteristics of Jeju island. When the precipitation and soil water are less than threshold value, we revised the SWAT routine not to make surface/lateral or groundwater discharge. For Hancheon watershed, the threshold value was set as 80% of soil water through the analysis of rainfall-runoff relationship. Through the simulation of test watershed, it was proven that TRSM performed much better in simulating pulse type stream flow for the Hancheon watershed.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.373-376
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• 2002

This paper represents a survey of a variety thresholding techniques including both global and local thresholding. Several thresholding methods are examined in detail to evaluate their performance based on a given set of test images. We also attempt to evaluate the performance of several thresholding methods for construction field documents image recognition system using a broken line structures, broken symbols and text, blurring of lines, symbols and text, noise in homogeneous areas measure as a criterion functions.