• Journal of the Korea Institute of Building Construction
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• v.12 no.3
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• pp.299-314
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• 2012

One of reasons for introducing Building Information Modeling (BIM) is to support clash detection tasks by means of a 3D product model. In the conventional construction project process, clashes have been found during construction phase. However, it can cause cost overrun and time delay. In order to investigate and correct clash detections at design phase, relevant business process and guide for this task should be provided. This study aims to identify hindrances in clash detection tasks at the design phase and analyze its current process using IDEF0 model. Despite the convenience of IDEF0 as a systems analysis tool, professional participants might have difficulties to understand their own tasks according to business process. For this reason, in this research, Business Process Model and Notation (BPMN) is introduced to provide ideal process and required decision making governance. The provide BPMN model will provide insights for a BIM-based collaborative environment to enhance the constructability through the construction project.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.12-16
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• 2011

Building Information Modeling (BIM) is being widely spread in AEC industry worldwide and also in South Korea. Although the creation of digital model is better to be started at design stage, it can also improve the productivity of construction by simulating the actual construction process and environment. This paper presents application of BIM-based simulations related with design changes to transfer floors in 58-storey reinforced concrete office building. Transfer floor is not only a structurally important part of the building but also a challenging part of the actual construction in terms of sequence and period due to the complexity of the work. Preconstruction of rebar, mechanical, and plumbing is performed to review the construction drawings and to perform clash detection. Each item of application is evaluated for its effectiveness on actual construction and for the development potential.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.634-639
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• 2013

The aim of the study is to develop the 3D visualization of Building Information Model and integrated 4D model for optimization of resources in the construction project. This study discuss the process of methodology and creation of 4D model of the project and simulate it to monitor the workflow at the site. Different stages of the construction process and activities are generated by using Revit and MS Project. MS project has been used for creation of the schedules and these are linked with the Revit for 3D modeling. The time used as the fourth dimension and 4D model created by using Navisworks Time liner software. Narges shopping center is presented as a case study to realize the actual uses and benefits of Building Information Model (BIM). Narges shopping mall is located in Tehran, Iran. As a part of Hekmat master plan, Narges shopping center is an 11 stores building with a total area of 30000 Sq.m. This shopping and entertainment center is comprised of 150 retails and two multi-use public halls with a capacity of 400 persons each and underground parking with total 400 parking space. The main purpose of architecture was to create an urban public center along with its revolving, spiral like form and an ever changing continuous façade by means of different colors, materials, which is in harmony with the other building of the master plan. The approximate cost of the project is $17 million and duration of the project schedule is 30 months. The developed Building Information Model enabled us to identify the potential collisions or clashes between various structural and architectural systems. 4D model has been used for limiting the interaction between subcontractors installing the different systems so rework could be avoided and productivity maximized. It is also observed that the utility of BIM for construction stimulation and clash detection is the best suitable method. Clash detection before the implementation of work is highly recommended to avoid rework.

• Journal of KIBIM
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• v.4 no.1
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• pp.1-7
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• 2014

The construction method using PC(Precast Concrete) has been widely used because the method can shorten the construction period and improve construction quality. In this paper, through the analysis of present design process for PC railway platform, design parameters on the geometry and properties were extracted and 3D information models for PC railway platform were constructed by the parametric modeling technique. Furthermore, the interface module was developed to link 3D models to the structural analysis/design sheet and database program using VBA(Visual Basic Application). This information model could be used in various areas including structural analysis and design, 2D drawing, quantity estimation and 4D simulation including clash detection.

• Journal of the Korea Institute of Building Construction
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• v.12 no.1
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• pp.98-107
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• 2012

Reinforcing bars (rebar) comprise an integral part of a concrete structure, and play a major role in the safety and durability of the building. However, the actual placement or installation of rebar is not planned and controlled by the detailer. Recently, 4D simulations, using 3D model and scheduling software, have been used to improve the efficiency of the construction phrase. However, 4D simulators have not been introduced at the detailed level of work, such as rebar placement. Therefore, this paper suggests a BIM-based simulator for rebar placement to determine the sequence with which rebar is placed into the form. The system using Autodesk Revit API automatically generates rebar placement plans for a building structure, and labels the placement sequence of each individual bar or set of bars with ascending numbers. The placement sequence is then visualized using Autodesk Revit Structure 2012. This paper provides a short description of a field assessment and limits.

• The Transactions of the Korean Institute of Power Electronics
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• v.13 no.4
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• pp.278-286
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• 2008

This paper investigates the fault generation type and the cause of output interruptionsin bulky with $30^{\sim}500[KVA]$ double conversion UPS, and proposes the fault detection method to improve the reliability of power supply used in the critical load in industry. Identifies its existing way of detecting a quality of inverter output it to bypass when exceeds its expectation. Under a UPS managing system, when an inner (Power device, Controller, CPU) fault occurs it disrupts the power supply and these occurrences has been verified by the results of experiments and application results. To overcome these problems, the proposed method constructs independently a fault-detection, a bypass-control device and a triple power supply apart from the conventional UPS operation. Also the detection point is changed to the preceding of a circuit breaker, a reference of fault detection is modified to avoid any clash and the breaking equipment is attached to intercept a spread of accident. As a result of applications of these developed systems to 242 UPS which was installed purposefully to the communication power supply, the service errors has not occurred in the UPS for two years since 2006.

• Journal of KIBIM
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• v.3 no.3
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• pp.29-38
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• 2013

Domestic construction industry is larger in size, and its shape is more complex. As a result, collaboration and information management tasks are difficult to manage in a way that conventional 2D-based exchange of information. BIM technology was introduced as a solution for this. But BIM only provides a very limited set of features such as Constructability review, clash detection in reality. and duplication of work and a lot of iterations occurs in BIM process. Thus, it takes quite a lot time to work. We studied how to improve the work efficiency by connecting API to business processes. Using the API in the integration process by automating repetitive tasks, reduce a significant amount of work time Using API (Automation, Optimization, Interoperability, Analysis) can solve the task problem that does not solved by the basic features, If we make good use of the combined API will be improved productivity.

• International Journal of Safety
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• v.9 no.2
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• pp.29-34
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• 2010

When doing maintenance works at the trackside of railway, the method which delivers information on approaching of train to maintenance workers through alarm devices such as the flag or indication light, etc., is being used by locating persons in charge of safety alarm in addition to the maintenance workers at fixed distances in the front and rear of the workplace. Workers maintaining at the trackside may collide with the train since they cannot recognize the approach of train although it approaches to the vicinity of maintenance workplace because of the sensory block phenomenon occurred due to their long hours of continued monotonous maintenance work. The clash or rear-end collision accidents between many maintenance trains called motor-cars can be occurred since there are cases where the signal systems for safe operation of motor-car such as track circuit etc. are blocked or not operated normally. We developed the new safety equipment for protection of trackside maintenance workers using radio frequency signals and bidirectional detection mechanism. The developed safety equipment must analyze the several operational mechanism for each different operation situations. In this paper the analysis results are represented.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.117-126
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• 2020

A maritime object detection system is an intelligent assistance system to maritime autonomous surface ship(MASS). It detects automatically floating debris, which has a clash risk with objects in the surrounding water and used to be checked by a captain with a naked eye, at a similar level of accuracy to the human check method. It is used to detect objects around a ship. In the past, they were detected with information gathered from radars or sonar devices. With the development of artificial intelligence technology, intelligent CCTV installed in a ship are used to detect various types of floating debris on the course of sailing. If the speed of processing video data slows down due to the various requirements and complexity of MASS, however, there is no guarantee for safety as well as smooth service support. Trying to solve this issue, this study conducted research on the minimization of computation volumes for video data and the increased speed of data processing to detect maritime objects. Unlike previous studies that used the Hough transform algorithm to find the horizon and secure the areas of interest for the concerned objects, the present study proposed a new method of optimizing a binarization algorithm and finding areas whose locations were similar to actual objects in order to improve the speed. A maritime object detection system was materialized based on deep learning CNN to demonstrate the usefulness of the proposed method and assess the performance of the algorithm. The proposed algorithm performed at a speed that was 4 times faster than the old method while keeping the detection accuracy of the old method.

• Smart Structures and Systems
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• v.29 no.4
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.