• Korean Journal of Construction Engineering and Management
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• v.10 no.2
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• pp.102-110
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• 2009

The cost for maintenance of bridge structures such as repair or reinforcement is increasing. In addition, the efforts for inspection of bridge structures is becoming more important since the proper repair or reinforcement should be performed to save the maintenance cost and ensure the safety for public infrastructure. Therefore, it is studied on this paper to estimate the repair or reinforcement cycles using probabilistic approach for the steel-box girders of bridge superstructure. In addition, a computer simulation program is uniquely developed based on probabilistic approach to calculate the cycles derived from the function of age of bridge and performance rating curve which were previously studied. In order to ensure the reliability of results and appropriateness of the model, statistical analyses were performed. Also, the results were compared and proved to be similar with ones from previous statistical data related to the repair or reinforcement cycles. The results from this study is expected to be useful for the determination of proper time to repair or reinforce the bridge structure and raise the safetyness of bridge structure in advance.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.4
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• pp.95-105
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• 2021

Road surface damage detection is essential for a comfortable driving environment and the prevention of safety accidents. Road management institutes are using automated technology-based inspection equipment and systems. As one of these automation technologies, a sensor to detect road surface damage plays an important role. For this purpose, several studies on sensors using deep learning have been conducted in recent years. Road images and label images are needed to develop such deep learning algorithms. On the other hand, considerable time and labor will be needed to secure label images. In this paper, the adversarial learning method, one of the semi-supervised learning techniques, was proposed to solve this problem. For its implementation, a lightweight deep neural network model was trained using 5,327 road images and 1,327 label images. After experimenting with 400 road images, a model with a mean intersection over a union of 80.54% and an F1 score of 77.85% was developed. Through this, a technology that can improve recognition performance by adding only road images was developed to learning without label images and is expected to be used as a technology for road surface management in the future.

• Korean Journal of Construction Engineering and Management
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• v.22 no.1
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• pp.36-46
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• 2021

With an increasing trend toward high-rise modular construction, the simultaneous use of tower cranes at a modular construction site has recently been observed. Tower crane layout planning (TCLP) has a significant effect on cost, duration, safety and productivity of a project. In a modular construction project, particularly, poor decision about the layout of tower cranes is likely to have negative effects like additional employment of cranes and redesign, which will lead to additional costs and possible delays. It is, therefore, crucial to conduct thorough inspection of field conditions, lifting materials, tower crane capacity to make decisions on the layout of tower cranes. However, several challenges exist in planning for a multi-crane construction site in terms of safety and collaboration, which makes planning with experience and intuition complicated. This paper suggests a multi-objective optimization model for selection of the number of tower cranes, their models and locations, which minimizes cost and conflict. The proposed model contributes to the body of knowledge by showing the feasibility of using multi-objective optimization for TCLP decision-making process with consideration of trade-offs between cost and conflict.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.770-778
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• 2023

Purpose: In order to reduce forklift industrial accidents, it is necessary to classify them into institutional, technical, and educational factors and conduct research on whether each factor affects disaster reduction. Method: Descriptive statistical analysis, validity analysis, reliability analysis, and multiple regression analysis were conducted using SPSS 18 program based on an offline questionnaire based on a 5-point Likert scale. Result: As a result of multiple regression analysis, it was found that institutional, technical, and educational factors, which are independent variables for disaster reduction, explain about 62.5% of the variance in disaster prevention, which is the dependent variable. The regression model verification was found to be statistically significant with F=118.775 and significance probability p<0.01. Conclusion: First, there is a need to prevent disasters by including electric forklifts weighing less than 3 tons in the inspection system. Second, there is a need to make it mandatory to install front and rear cameras and forklift line beams to prevent forklift collision disasters. Third, there is a need to conduct special training related to forklifts every year, and drivers and nearby workers need to be included in the special training for forklifts.

• The Journal of Engineering Geology
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• v.24 no.1
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• pp.53-67
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• 2014

Numerical modeling is commonly used to reproduce the physical phenomena of dam-break and to compile resulting flood hazard maps. The accuracy of a dam-break model depends on the physical structure that describes the volume of storage, breach formation and progress, input variables, and model parameters. Model input and parameters are subjective in that they are prescribed; hence, caution is needed when interpreting the results. This study focuses on three parameters (breach degree ${\theta}$, shape factor P, and collapse rate k) used when the dam-break model is coupled with FLO-2D (a two-dimensional flood simulation model) to estimate flood coverage and depth etc. The results show that the simulation is sensitive to the shape factor P and the collapse rate k but not to the breach degree ${\theta}$. This study will contribute to reducing flood damage from dam-break disasters in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.220-227
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• 2021

PCT (Power Cable Tunnel) and UT (Utility Tunnel), which are non-transport underground infrastructures, are mostly RC (Reinforced Concrete) structures, and their durability decreases due to the deterioration caused by carbonation over time. In particular, since the rate of carbonation varies by use and region, a predictive model based on actual carbonation data is required for individual maintenance. In this study, a carbonation prediction model was developed for non-transport underground infrastructures, such as PCT and UT. A carbonation prediction model was developed using multiple regression analysis and deep neural network techniques based on the actual data obtained from a safety inspection. The structures, region, measurement location, construction method, measurement member, and concrete strength were selected as independent variables to determine the dependent variable carbonation rate coefficient in multiple regression analysis. The adjusted coefficient of determination (Ra2) of the multiple regression model was found to be 0.67. The coefficient of determination (R2) of the model for predicting the carbonation of non-transport underground infrastructures using a deep neural network was 0.82, which was superior to the comparative prediction model. These results are expected to help determine the optimal timing for repair on carbonation and preventive maintenance methodology for PCT and UT.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.254-265
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• 2021

Purpose: Inspection and diagnosis on the performance and safety through domestic port facilities have been conducted for over 20 years. However, the long-term development strategies and directions for facility renewal and performance improvement using the diagnosis history and results are not working in realistically. In particular, in the case of port structures with a long service life, there are many problems in terms of safety and functionality due to increasing of the large-sized ships, of port use frequency, and the effects of natural disasters due to climate change. Method: In this study, the maintenance history data of the gravity type quay in element level were collected, defined as big data, and a predictive approximation model was derived to estimate the pattern of deterioration and aging of the facility of project level based on the data. In particular, we compared and proposed models suitable for the use of big data by examining the validity of the state-based deterioration pattern and deterioration approximation model generated through machine learning algorithms of GP and SGP techniques. Result: As a result of reviewing the suitability of the proposed technique, it was considered that the RMSE and R² in GP technique were 0.9854 and 0.0721, and the SGP technique was 0.7246 and 0.2518. Conclusion: This research through machine learning techniques is expected to play an important role in decision-making on investment in port facilities in the future if port facility data collection is continuously performed in the future.

• Journal of the Korean Society of Marine Environment & Safety
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• v.19 no.6
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• pp.649-657
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• 2013

In this study, for a proposal of job-based IMDG code expert training model, the training cases of other countries were analyzed comparatively and a questionnaire-survey was conducted to find the needs of workers. As results of comparative analysis, various curriculums were operated by jobs of workers in USA and by kinds of dangerous goods or vehicles in UK, but a common curriculum was provided for various jobs of workers in Korea. It was analyzed that current domestic curriculum is not efficient, and the respondents demand expansion of training including provision of information via web-site as results of questionnaire survey. Therefore, in conclusion, after the shore workers were classified into three groups(on-site worker, office worker, & carrying worker), the customized training program of each group was suggested. Furthermore, this study proposed the regional operation of training course to meet regional demand on education including establishment of on-line curriculums.

• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.99-110
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• 2008

It is commonly estimated that there is a much higher potential for accidents at a crossroads than along a single road due to its plethora of conflicting points. According to the 2006 figures by the National Police Agency, the number of traffic accidents at crossroads is greatly increasing compared to that along single roads. Among others, crossroads installed with traffic signals have more varied influential factors for traffic accidents and leave much more room for improvement than ones without traffic signals; thus, it is expected that a noticeable effect could be achieved in safety if proper counter-measures against the hazards at a crossroads were taken together with an estimate of causes for accidents This research managed to develop models for accident forecasts and accident intensity by applying data on accident history and site inspection of crossroads, targeting four selected downtown crossroads installed with traffic signals. The research was done by roughly dividing the process into four stages: first, analyze the accident model examined before; second, select variables affecting traffic accidents; third, develop a model for traffic accident forecasting by using a statistics-based methodology; and fourth, carry out the verification process of the models.

• Journal of the Korean Society of Safety
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• v.31 no.3
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• pp.8-15
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• 2016

The purpose of this study is to design a model with the structural stability so as not to lose the operational function due to structural plastic or fail of a sliding blast door by blast pressure to this aim, a numerical simulation was performed using full-size experiments and M&S (Modeling & Simulation) of the sliding blast door. The sliding blast door ($W3,000{\times}H2,500mm$) under the blast load is in the form of a sliding type 2-way metal grill, which was applied by a design blast pressure (reflected pressure $P_r$) of 17 bar. According to the experimental results of a real sliding blast door under blast load, the blast pressure reached the sliding blast door approximately 4.3 ms after the explosion and lasted about 4.0 ms thereafter. The maximum blast pressure($P_r$) was 347.7 psi (2,397.3 kPa), it is similar to the UFC 3-340-02 of Parameter(91 %). In addition, operation inspection that was conducted for the sliding blast door after real test showed a problem of losing the door opening function, which was because of the fail of the Reversal Bolt that was installed to prevent the shock due to rebound of the blast door from the blast pressure. According to the reproduction of the experiment through M&S by applying the blast pressure measurement value of the full-size experiments, the sliding blast door showed a similar result to the full-size experiment in that the reversal bolt part failed to lose the function. In addition, as the pressure is concentrated on the failed reversal bolt, the Principal Tensile Failure Stress was exceeded in only 1.25 ms after the explosion, and the reversal bolt completely failed after 5.4 ms. Based on the result of the failed reversal bolt through the full-size experiment and M&S, the shape and size of the bolts were changed to re-design the M&S and re-analyze the sliding blast door. According to the M&S re-analysis result when the reversal bolt was designed in a square of 25 mm ($625mm^2$), the maximum pressure that the reversal bolt receives showed 81% of the principal tensile failure stress of the material, in plastic stage before fail.