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Changwan Kim
Department of Architectural Engineering, Chung-Ang University, Seoul 06974, Republic of Korea

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Journal article
Published: 02 August 2021 in Automation in Construction
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As-built building information models (BIMs) based on the 3D point clouds of built environments need to be able to completely and automatically model building elements for various applications (e.g., structural analysis, facility maintenance, and environmental analysis). However, missing data during data acquisition can result in an inaccurate as-built BIM. This study thus proposes an automated as-built model generation method with complete geometry information extraction by exploiting the connectivity between the structural elements in a point cloud with missing data. We used a deep learning model to classify and segment the elements at the point level and employed a neighbor network to extract and model the exact geometry of elements. The experimental results demonstrate that the proposed method can automatically develop an as-built BIM from a point cloud with missing data by recognizing and modeling 99% of the individual elements from the structural elements. As a result, a complete BIM can be produced automatically by overcoming the limitations of missing data.

ACS Style

Hyunsoo Kim; Changwan Kim. 3D as-built modeling from incomplete point clouds using connectivity relations. Automation in Construction 2021, 130, 103855 .

AMA Style

Hyunsoo Kim, Changwan Kim. 3D as-built modeling from incomplete point clouds using connectivity relations. Automation in Construction. 2021; 130 ():103855.

Chicago/Turabian Style

Hyunsoo Kim; Changwan Kim. 2021. "3D as-built modeling from incomplete point clouds using connectivity relations." Automation in Construction 130, no. : 103855.

Journal article
Published: 21 March 2021 in Automation in Construction
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Safety is the most important issue in the operation of machinery on a construction site. Due to the poor visibility of the surrounding environment, the machinery operated at construction sites poses a serious threat to the safety of the construction workers, as well as to the operators. This study proposes an integrated construction worker detection and tracking scheme using complementary metal-oxide semiconductor (CMOS) image sensors for real-time monitoring of the workspace and the safe operation of construction machinery. Various procedures were developed to detect and track construction workers in image sequences obtained from the CMOS image sensors. The architecture of the proposed scheme consists of the latest and fourth version of you only look once (YOLO) and the Siamese network, which are based on convolutional neural networks. Field experiments were performed to test the performance, while earthmoving operations were executed at the construction sites. The integrated architecture had recall, precision, and accuracy rates and F1 and F2 scores of 98.47%, 97.50%, 96.04%, 97.98%, and 98.27%, respectively. In addition, the results of worker detection and tracking were updated at 22 frames per second (fps). It is expected that the proposed method can be applied to operator assistance systems in construction machinery to achieve active safety.

ACS Style

Hyojoo Son; Changwan Kim. Integrated worker detection and tracking for the safe operation of construction machinery. Automation in Construction 2021, 126, 103670 .

AMA Style

Hyojoo Son, Changwan Kim. Integrated worker detection and tracking for the safe operation of construction machinery. Automation in Construction. 2021; 126 ():103670.

Chicago/Turabian Style

Hyojoo Son; Changwan Kim. 2021. "Integrated worker detection and tracking for the safe operation of construction machinery." Automation in Construction 126, no. : 103670.

Journal article
Published: 16 November 2020 in Remote Sensing
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Conventional bridge maintenance requires significant time and effort because it involves manual inspection and two-dimensional drawings are used to record any damage. For this reason, a process that identifies the location of the damage in three-dimensional space and classifies the bridge components involved is required. In this study, three deep-learning models—PointNet, PointCNN, and Dynamic Graph Convolutional Neural Network (DGCNN)—were compared to classify the components of bridges. Point cloud data were acquired from three types of bridge (Rahmen, girder, and gravity bridges) to determine the optimal model for use across all three types. Three-fold cross-validation was employed, with overall accuracy and intersection over unions used as the performance measures. The mean interval over unit value of DGCNN is 86.85%, which is higher than 84.29% of Pointnet, 74.68% of PointCNN. The accurate classification of a bridge component based on its relationship with the surrounding components may assist in identifying whether the damage to a bridge affects a structurally important main component.

ACS Style

Hyeonsoo Kim; Changwan Kim. Deep-Learning-Based Classification of Point Clouds for Bridge Inspection. Remote Sensing 2020, 12, 3757 .

AMA Style

Hyeonsoo Kim, Changwan Kim. Deep-Learning-Based Classification of Point Clouds for Bridge Inspection. Remote Sensing. 2020; 12 (22):3757.

Chicago/Turabian Style

Hyeonsoo Kim; Changwan Kim. 2020. "Deep-Learning-Based Classification of Point Clouds for Bridge Inspection." Remote Sensing 12, no. 22: 3757.

Journal article
Published: 13 April 2020 in Sustainability
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Forecasting electricity demand at the regional or national level is a key procedural element of power-system planning. However, achieving such objectives in the residential sector, the primary driver of peak demand, is challenging given this sector’s pattern of constantly fluctuating and gradually increasing energy usage. Although deep learning algorithms have recently yielded promising results in various time series analyses, their potential applicability to forecasting monthly residential electricity demand has yet to be fully explored. As such, this study proposed a forecasting model with social and weather-related variables by introducing long short-term memory (LSTM), which has been known to be powerful among deep learning-based approaches for time series forecasting. The validation of the proposed model was performed using a set of data spanning 22 years in South Korea. The resulting forecasting performance was evaluated on the basis of six performance measures. Further, this model’s performance was subjected to a comparison with the performance of four benchmark models. The performance of the proposed model was exceptional according to all of the measures employed. This model can facilitate improved decision-making regarding power-system planning by accurately forecasting the electricity demands of the residential sector, thereby contributing to the efficient production and use of resources.

ACS Style

Hyojoo Son; Changwan Kim. A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity. Sustainability 2020, 12, 3103 .

AMA Style

Hyojoo Son, Changwan Kim. A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity. Sustainability. 2020; 12 (8):3103.

Chicago/Turabian Style

Hyojoo Son; Changwan Kim. 2020. "A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity." Sustainability 12, no. 8: 3103.

Journal article
Published: 11 October 2019 in Advanced Engineering Informatics
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With the growing need for automated condition monitoring and analysis in existing buildings, significant effort has been spent on the development of three-dimensional (3D) thermal models. However, little attention has been paid to ensuring the quality of these 3D thermal models, which can directly impact the accuracy of condition monitoring and analysis results. This study aims to propose a method to generate a high-quality 3D thermal model for mechanical, electrical, and plumbing (MEP) systems by bridging the quality discrepancy between high-resolution laser scan data and low-resolution thermal images using a deep convolutional neural network. The proposed method consists of two main parts: (1) improving the resolution of thermal images based on a deep convolutional network and (2) generating a high-quality 3D thermal model by mapping improved thermal images. The performance of the thermal image resolution improvement was validated using a dataset consisting of 312 thermal images. The results demonstrated that the quality of the improved thermal images based on a deep convolutional network was higher than conventional bicubic interpolation in terms of root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM). Qualitative analysis of a 3D thermal model utilizing the resolution-improved thermal images was also conducted. This was further qualitatively analyzed to have resulted in improved overall quality of the 3D thermal model. The ability to generate a high-quality 3D thermal model can help auditors to perform automated condition monitoring and analysis in buildings based on objective and accurate data.

ACS Style

Hyojoo Son; Changwan Kim; Hyunchul Choi. High-quality as-is 3D thermal modeling in MEP systems using a deep convolutional network. Advanced Engineering Informatics 2019, 42, 100999 .

AMA Style

Hyojoo Son, Changwan Kim, Hyunchul Choi. High-quality as-is 3D thermal modeling in MEP systems using a deep convolutional network. Advanced Engineering Informatics. 2019; 42 ():100999.

Chicago/Turabian Style

Hyojoo Son; Changwan Kim; Hyunchul Choi. 2019. "High-quality as-is 3D thermal modeling in MEP systems using a deep convolutional network." Advanced Engineering Informatics 42, no. : 100999.

Journal article
Published: 01 September 2019 in Journal of Computing in Civil Engineering
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The heavy equipment used at construction sites poses a significant threat to the safety of surrounding workers due to the inherently poor visibility of the equipment operator. To improve visibility, heavy-equipment manufacturers have adopted a system that monitors an all-around view of the area surrounding the equipment via cameras installed on every side (i.e., front, right, left, and rear) of the equipment body to display the surrounding environment on the operator’s monitor. Although these systems improve the visibility of the surrounding environment, detecting potential collisions with workers nearby is still restricted by the limited cognitive capacity of the operator, who is executing tasks that are themselves cognitively effortful. The aim of this study is to propose a real-time warning system using visual data acquired from cameras that are readily available in the heavy equipment to protect the workers from potentially dangerous situations involving equipment operations. For this purpose, possible collisions with workers in the workspace of heavy equipment are detected and monitored by estimating the workers’ positions in three dimensions (3D) with a monocular camera on each side of the equipment. Field tests were conducted to verify the accuracy and speed of the system, as well as its applicability to actual construction sites. The proposed system was implemented on two different sizes of excavators while the excavators performed excavating and moving tasks at various construction sites. The field test results indicate that the proposed system can provide information to the operator in real time about whether one or more workers may have come into contact with the equipment during manipulation and transportation of the equipment. It is expected that the proposed method can be utilized in around-view monitoring systems to assist the operator and achieve active safety.

ACS Style

Hyojoo Son; Hyeonwoo Seong; Hyunchul Choi; Changwan Kim. Real-Time Vision-Based Warning System for Prevention of Collisions between Workers and Heavy Equipment. Journal of Computing in Civil Engineering 2019, 33, 04019029 .

AMA Style

Hyojoo Son, Hyeonwoo Seong, Hyunchul Choi, Changwan Kim. Real-Time Vision-Based Warning System for Prevention of Collisions between Workers and Heavy Equipment. Journal of Computing in Civil Engineering. 2019; 33 (5):04019029.

Chicago/Turabian Style

Hyojoo Son; Hyeonwoo Seong; Hyunchul Choi; Changwan Kim. 2019. "Real-Time Vision-Based Warning System for Prevention of Collisions between Workers and Heavy Equipment." Journal of Computing in Civil Engineering 33, no. 5: 04019029.

Journal article
Published: 10 December 2018 in Automation in Construction
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Analyzing the location and behavior of construction workers using construction site images has been recognized as a means of providing useful information for safety management and productivity analysis. Although effective utilization of analyzed image data requires accurate and timely detection of workers in complex, continuously changing working environments, the previous methods that detect construction workers still require improvement because of the poor detection performance. This study proposes the use of very deep residual networks to accurately and rapidly detect construction workers under varying poses and against changing backgrounds in image sequences. The architecture of construction worker detection in this study is based on convolutional neural networks (CNNs). The proposed method is divided into two stages: extracting feature maps via very deep residual networks (ResNet-152) and bounding box regression and labeling from the original image via Faster regions with CNN features (R-CNN). The experiments were conducted at actual construction sites by acquiring 1.3-megapixel and 3.1-megapixel images from a movable digital camera to verify the proposed method for images from fixed and moving cameras. Faster R-CNN with ResNet-152 had accuracy, precision, and recall rates of 94.3%, 96.03%, and 98.13% for 3241 images, respectively. The proposed method processed 0.2 s per frame (i.e., 5 frames per second) on average. The results show that it is possible to accurately and rapidly detect multiple workers in construction site images by employing very deep residual networks without relying on limited assumptions about workers' postures, appearance, and background.

ACS Style

Hyojoo Son; Hyunchul Choi; Hyeonwoo Seong; Changwan Kim. Detection of construction workers under varying poses and changing background in image sequences via very deep residual networks. Automation in Construction 2018, 99, 27 -38.

AMA Style

Hyojoo Son, Hyunchul Choi, Hyeonwoo Seong, Changwan Kim. Detection of construction workers under varying poses and changing background in image sequences via very deep residual networks. Automation in Construction. 2018; 99 ():27-38.

Chicago/Turabian Style

Hyojoo Son; Hyunchul Choi; Hyeonwoo Seong; Changwan Kim. 2018. "Detection of construction workers under varying poses and changing background in image sequences via very deep residual networks." Automation in Construction 99, no. : 27-38.

Journal article
Published: 01 November 2018 in Expert Systems with Applications
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In the bid process, predicting whether the contractor will suffer a financial crisis during the construction project is vital to project owners and other stakeholders for identifying problems and taking strategic action. In this context, the models for predicting financial crisis of contractor have been extensively studied. However, the previous studies have been focused on predicting a financial crisis for one-quarter or one-year ahead of prediction point, even though the duration of projects are relatively long in the construction industry, usually exceeding one year. Moreover, despite the possibility of knowing the signs of financial crisis of a contractor through predicting financial distress, no attempt has been made to predict financial distress that contractor can suffer before reaching a financial crisis including highly visible legal events, such as bankruptcy, default, and delisting. This means that there is significant gap between those models and practical application in terms of the prediction period and definition of the financial crisis. This study proposes voting-based ensemble models that predict financial distress of contractor for two- and three-year ahead of prediction point using a finance-based definition of financial distress. The prediction performance of proposed model was evaluated using financial statements of contractors in South Korea from 2007 to 2012. The proposed models showed area under the receiver operating characteristic curve (AUC) values of 0.940 and 0.910 for predicting financial distress for each of the prediction years. By predicting financial distress of the contractor from the early stages of a construction project to the end stage with high accuracy, this model can help project owners and broad stakeholders to avoid damage due to financial crisis during a project.

ACS Style

Hyunchul Choi; Hyojoo Son; Changwan Kim. Predicting financial distress of contractors in the construction industry using ensemble learning. Expert Systems with Applications 2018, 110, 1 -10.

AMA Style

Hyunchul Choi, Hyojoo Son, Changwan Kim. Predicting financial distress of contractors in the construction industry using ensemble learning. Expert Systems with Applications. 2018; 110 ():1-10.

Chicago/Turabian Style

Hyunchul Choi; Hyojoo Son; Changwan Kim. 2018. "Predicting financial distress of contractors in the construction industry using ensemble learning." Expert Systems with Applications 110, no. : 1-10.

Construction management
Published: 28 September 2018 in KSCE Journal of Civil Engineering
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Detecting the safety vests is an important foundation for various applications in safety management and productivity measurement. The fluorescent yellow-green color and fluorescent orange-red color of safety vests are generally considered as the most distinctive colors which represent workers in construction-site images. The objective of this study is to provide an evaluation of the safety vest detection using color information in construction-site images. The data sets of two colors of safety vests and the background were generated and used in this study. A comparative analysis of combinations of five color spaces (RGB, nRGB, HSV, Lab, and YCbCr) and six classifiers (ANN, C4.5, KNN, LR, NB, and SVM) was conducted. The performance of each combination was assessed in terms of the precision, recall, and F-measure. Moreover, an evaluation of the effects of color space conversion and the absence of luminance components on the detection performance was conducted. The comparison results showed that C4.5 classifier combined with YCbCr and SVM classifier combined with Lab, respectively, outperformed other combinations on each data set of safety vest colors. Furthermore, RGB color space transformation into non-RGB color spaces enhanced the classification performance. The evaluation also showed that the removal of luminance components did not help to improve the performance.

ACS Style

Hyeonwoo Seong; Hyojoo Son; Changwan Kim. A Comparative Study of Machine Learning Classification for Color-based Safety Vest Detection on Construction-Site Images. KSCE Journal of Civil Engineering 2018, 22, 4254 -4262.

AMA Style

Hyeonwoo Seong, Hyojoo Son, Changwan Kim. A Comparative Study of Machine Learning Classification for Color-based Safety Vest Detection on Construction-Site Images. KSCE Journal of Civil Engineering. 2018; 22 (11):4254-4262.

Chicago/Turabian Style

Hyeonwoo Seong; Hyojoo Son; Changwan Kim. 2018. "A Comparative Study of Machine Learning Classification for Color-based Safety Vest Detection on Construction-Site Images." KSCE Journal of Civil Engineering 22, no. 11: 4254-4262.

Journal article
Published: 01 October 2017 in Advanced Engineering Informatics
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ACS Style

Hyojoo Son; Changwan Kim. Semantic as-built 3D modeling of structural elements of buildings based on local concavity and convexity. Advanced Engineering Informatics 2017, 34, 114 -124.

AMA Style

Hyojoo Son, Changwan Kim. Semantic as-built 3D modeling of structural elements of buildings based on local concavity and convexity. Advanced Engineering Informatics. 2017; 34 ():114-124.

Chicago/Turabian Style

Hyojoo Son; Changwan Kim. 2017. "Semantic as-built 3D modeling of structural elements of buildings based on local concavity and convexity." Advanced Engineering Informatics 34, no. : 114-124.

Journal article
Published: 01 August 2017 in Resources, Conservation and Recycling
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ACS Style

Hyojoo Son; Changwan Kim. Short-term forecasting of electricity demand for the residential sector using weather and social variables. Resources, Conservation and Recycling 2017, 123, 200 -207.

AMA Style

Hyojoo Son, Changwan Kim. Short-term forecasting of electricity demand for the residential sector using weather and social variables. Resources, Conservation and Recycling. 2017; 123 ():200-207.

Chicago/Turabian Style

Hyojoo Son; Changwan Kim. 2017. "Short-term forecasting of electricity demand for the residential sector using weather and social variables." Resources, Conservation and Recycling 123, no. : 200-207.

Journal article
Published: 01 August 2017 in Journal of Construction Engineering and Management
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Fall accidents constitute a crucial type of accident in the construction industry. This study investigates fall accidents that occurred in the United States between 1997 and 2012. Using the 20,997 construction industry accidents recorded in the Occupational Safety and Health Administration (OSHA) database, this study examines the frequency and trend of fall accidents. Additionally, by using data from 9,141 fall accidents, this study investigates various dimensions of fall accidents, such as fall height, fall location, and fall protection, and types of industry and projects where fall accidents occurred. The analyses and subsequent findings are discussed as follows: First, the percentage of fall accidents from four major accident types (fall, struck by, caught in or between, and electrocution) has been increased substantially. Second, in terms of project type, residential housing projects experienced a higher portion of fall accidents. Third, more than 80% of fall accidents occurred from a height of less than 9.1 m (30 ft), and only 11% of fall accident victims were properly equipped with fall protection. These findings serve to alert safety agencies of the need to diagnose the current state of fall accidents and to revise the policies and regulations to reduce these figures.

ACS Style

M.Asce Youngcheol Kang; Sohaib Siddiqui; Sung Joon Suk; M.Asce Seokho Chi; A.M.Asce Changwan Kim. Trends of Fall Accidents in the U.S. Construction Industry. Journal of Construction Engineering and Management 2017, 143, 04017043 .

AMA Style

M.Asce Youngcheol Kang, Sohaib Siddiqui, Sung Joon Suk, M.Asce Seokho Chi, A.M.Asce Changwan Kim. Trends of Fall Accidents in the U.S. Construction Industry. Journal of Construction Engineering and Management. 2017; 143 (8):04017043.

Chicago/Turabian Style

M.Asce Youngcheol Kang; Sohaib Siddiqui; Sung Joon Suk; M.Asce Seokho Chi; A.M.Asce Changwan Kim. 2017. "Trends of Fall Accidents in the U.S. Construction Industry." Journal of Construction Engineering and Management 143, no. 8: 04017043.

Conference paper
Published: 01 July 2017 in Proceedings of the 34th International Symposium on Automation and Robotics in Construction (ISARC)
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ACS Style

Hyojoo Son; Hyunwoo Sung; Hyunchul Choi; Sungwook Lee; Changwan Kim. Detection of Nearby Obstacles with Monocular Vision for Earthmoving Operations. Proceedings of the 34th International Symposium on Automation and Robotics in Construction (ISARC) 2017, 500 -505.

AMA Style

Hyojoo Son, Hyunwoo Sung, Hyunchul Choi, Sungwook Lee, Changwan Kim. Detection of Nearby Obstacles with Monocular Vision for Earthmoving Operations. Proceedings of the 34th International Symposium on Automation and Robotics in Construction (ISARC). 2017; ():500-505.

Chicago/Turabian Style

Hyojoo Son; Hyunwoo Sung; Hyunchul Choi; Sungwook Lee; Changwan Kim. 2017. "Detection of Nearby Obstacles with Monocular Vision for Earthmoving Operations." Proceedings of the 34th International Symposium on Automation and Robotics in Construction (ISARC) , no. : 500-505.

Conference paper
Published: 01 July 2017 in Proceedings of the 34th International Symposium on Automation and Robotics in Construction (ISARC)
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I.A.A.R.C. - International Association for Automation and Robotics in Construction Civil Engineering

ACS Style

Hyeonwoo Seong; Hyunchul Choi; HyukMan Cho; Hyukman Cho And Sungwook Lee; Hyojoo Son; Changwan Kim. Vision-Based Safety Vest Detection in a Construction Scene. Proceedings of the 34th International Symposium on Automation and Robotics in Construction (ISARC) 2017, 288 -293.

AMA Style

Hyeonwoo Seong, Hyunchul Choi, HyukMan Cho, Hyukman Cho And Sungwook Lee, Hyojoo Son, Changwan Kim. Vision-Based Safety Vest Detection in a Construction Scene. Proceedings of the 34th International Symposium on Automation and Robotics in Construction (ISARC). 2017; ():288-293.

Chicago/Turabian Style

Hyeonwoo Seong; Hyunchul Choi; HyukMan Cho; Hyukman Cho And Sungwook Lee; Hyojoo Son; Changwan Kim. 2017. "Vision-Based Safety Vest Detection in a Construction Scene." Proceedings of the 34th International Symposium on Automation and Robotics in Construction (ISARC) , no. : 288-293.

Conference paper
Published: 01 July 2017 in Proceedings of the 34th International Symposium on Automation and Robotics in Construction (ISARC)
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ACS Style

Hyunchul Choi; Hyeonwoo Seong; HyukMan Cho; Sungwook Lee; Hyojoo Son; Changwan Kim. Comparison of Single Classifier Models for Predicting Long-term Business Failure of Construction Companies Using Finance-based Definition of the Failure. Proceedings of the 34th International Symposium on Automation and Robotics in Construction (ISARC) 2017, 282 -287.

AMA Style

Hyunchul Choi, Hyeonwoo Seong, HyukMan Cho, Sungwook Lee, Hyojoo Son, Changwan Kim. Comparison of Single Classifier Models for Predicting Long-term Business Failure of Construction Companies Using Finance-based Definition of the Failure. Proceedings of the 34th International Symposium on Automation and Robotics in Construction (ISARC). 2017; ():282-287.

Chicago/Turabian Style

Hyunchul Choi; Hyeonwoo Seong; HyukMan Cho; Sungwook Lee; Hyojoo Son; Changwan Kim. 2017. "Comparison of Single Classifier Models for Predicting Long-term Business Failure of Construction Companies Using Finance-based Definition of the Failure." Proceedings of the 34th International Symposium on Automation and Robotics in Construction (ISARC) , no. : 282-287.

Journal article
Published: 01 July 2017 in Journal of Management in Engineering
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A schedule update is defined as a process for measuring the status of a project, predicting the date of its completion, and providing project managers with critical schedule information. The updated schedule information helps project managers evaluate their projects and take appropriate actions that will enable them to finish projects on time. In current practice, most schedule-update processes are performed manually, require considerable time and effort, and rely on subjective experience. This study proposes data flow and develops a system that updates schedules automatically through the use of project-management software and provides critical schedule information to project managers. The proposed system is based on the use of three-dimensional (3D) point-cloud data from construction sites and a four-dimensional (4D) model that includes an as-planned schedule. To verify the performance of the system developed in this study, a case study based on an actual construction site was conducted. The results show that the proposed system contributes to the automation of all steps involved in schedule updating. With automatic schedule updates, the practice can be made more efficient, and the process of schedule updating can be performed in a reliable and objective manner.

ACS Style

Hyojoo Son; Changwan Kim; Yong Kwon Cho. Automated Schedule Updates Using As-Built Data and a 4D Building Information Model. Journal of Management in Engineering 2017, 33, 04017012 .

AMA Style

Hyojoo Son, Changwan Kim, Yong Kwon Cho. Automated Schedule Updates Using As-Built Data and a 4D Building Information Model. Journal of Management in Engineering. 2017; 33 (4):04017012.

Chicago/Turabian Style

Hyojoo Son; Changwan Kim; Yong Kwon Cho. 2017. "Automated Schedule Updates Using As-Built Data and a 4D Building Information Model." Journal of Management in Engineering 33, no. 4: 04017012.

Journal article
Published: 01 March 2017 in Journal of Computing in Civil Engineering
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Recognizing construction assets (e.g., materials, equipment, labor) from point cloud data of construction environments provides essential information for engineering and management applications including progress monitoring, safety management, supply-chain management, and quality control. This study introduces a novel principal axes descriptor (PAD) for construction-equipment classification from point cloud data. Scattered as-is point clouds are first processed with downsampling, segmentation, and clustering steps to obtain individual instances of construction equipment. A geometric descriptor consisting of dimensional variation, occupancy distribution, shape profile, and plane counting features is then calculated to encode three-dimensional (3D) characteristics of each equipment category. Using the derived features, machine learning methods such as k-nearest neighbors and support vector machine are employed to determine class membership among major construction-equipment categories such as backhoe loader, bulldozer, dump truck, excavator, and front loader. Construction-equipment classification with the proposed PAD was validated using computer-aided design (CAD)–generated point clouds as training data and laser-scanned point clouds from an equipment yard as testing data. The recognition performance was further evaluated using point clouds from a construction site as well as a pose variation data set. PAD was shown to achieve a higher recall rate and lower computation time compared to competing 3D descriptors. The results indicate that the proposed descriptor is a viable solution for construction-equipment classification from point cloud data.

ACS Style

Jingdao Chen; Yihai Fang; Yong K. Cho; Changwan Kim. Principal Axes Descriptor for Automated Construction-Equipment Classification from Point Clouds. Journal of Computing in Civil Engineering 2017, 31, 04016058 .

AMA Style

Jingdao Chen, Yihai Fang, Yong K. Cho, Changwan Kim. Principal Axes Descriptor for Automated Construction-Equipment Classification from Point Clouds. Journal of Computing in Civil Engineering. 2017; 31 (2):04016058.

Chicago/Turabian Style

Jingdao Chen; Yihai Fang; Yong K. Cho; Changwan Kim. 2017. "Principal Axes Descriptor for Automated Construction-Equipment Classification from Point Clouds." Journal of Computing in Civil Engineering 31, no. 2: 04016058.

Transportation engineering
Published: 09 January 2017 in KSCE Journal of Civil Engineering
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This article compares Taiwan and Korea high speed rail systems to identify service factors that affect their long-term development. Data were collected by questionnaires and interviews administered over several years to study passenger travel behavior and perceptions of service quality. Analysis results indicate that improving operational effectiveness requires enhanced service quality and that Taiwan High Speed Rail (THSR) passengers are very concerned about facility of infrastructure services. In contrast, the main concern of Korea Train eXpress (KTX) passengers is frontline staff interaction. Notably, the data show that THSR passenger satisfaction increased steadily to levels superior to those expressed by KTX passengers. Another finding is that, in terms of resource allocation, both Taiwan and Korea should improve the handling of passenger complaints, provide improved scheduling information, and strive to improve arrival and departure punctuality. The contribution of this study is the development of a systematic method of assessing the long-term performance of high-speed rail transport services, by which management units can adjust operating strategies to continuously improve services. The analysis results can facilitate the THSR and KTX to formulating planning and operational strategies that can achieve the goal of sustainable operations.

ACS Style

Jui-Sheng Chou; Changwan Kim; Pei-Yu Tsai; Chun-Pin Yeh; Hyojoo Son. Longitudinal assessment of high-speed rail service delivery, satisfaction and operations: A study of Taiwan and Korea systems. KSCE Journal of Civil Engineering 2017, 21, 2413 -2428.

AMA Style

Jui-Sheng Chou, Changwan Kim, Pei-Yu Tsai, Chun-Pin Yeh, Hyojoo Son. Longitudinal assessment of high-speed rail service delivery, satisfaction and operations: A study of Taiwan and Korea systems. KSCE Journal of Civil Engineering. 2017; 21 (6):2413-2428.

Chicago/Turabian Style

Jui-Sheng Chou; Changwan Kim; Pei-Yu Tsai; Chun-Pin Yeh; Hyojoo Son. 2017. "Longitudinal assessment of high-speed rail service delivery, satisfaction and operations: A study of Taiwan and Korea systems." KSCE Journal of Civil Engineering 21, no. 6: 2413-2428.

Journal article
Published: 04 October 2016 in IEEE Transactions on Electron Devices
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We propose a time-sequential ultraviolet (UV) exposure process that can improve the surface anchoring energy of photopolyimide (PI) embedded with reactive mesogen (RM) in high-speed liquid crystal (LC) display devices. To increase the anchoring energy of the PI layer, a separated UV exposure process of polymerization for the embedded RM material and PI layer is required [1]-[8]. In this paper, we propose a novel single-frequency UV exposure method that can perform the separated polymerization of the UV alignment layer and the embedded RM material by optimizing the intensity of the exposure UV light. Using the proposed UV exposure method, we polymerize the RM material during the first 4 s, and then accomplish the polymerization of the UV alignment layer sequentially. To demonstrate the electro-optical performance, we measure the surface anchoring energy and the optical response time of in-plane switching LC cell. The measured results show that the surface anchoring energy and the optical response time are improved by more than 2.5 times and 28.2%, respectively, compared with the conventional UV exposure.

ACS Style

Byung-June Mun; Ki-Woong Park; Ji-Ho Baek; Byeong Koo Kim; Hyun Chul Choi; Changwan Kim; Bongsoon Kang; Seung Hee Lee; Gi-Dong Lee. Time-Sequential Ultraviolet Exposure to Alignment Layers Embedded With Reactive Mesogen for High-Speed In-Plane Switching Liquid Crystal Cell. IEEE Transactions on Electron Devices 2016, 63, 4326 -4330.

AMA Style

Byung-June Mun, Ki-Woong Park, Ji-Ho Baek, Byeong Koo Kim, Hyun Chul Choi, Changwan Kim, Bongsoon Kang, Seung Hee Lee, Gi-Dong Lee. Time-Sequential Ultraviolet Exposure to Alignment Layers Embedded With Reactive Mesogen for High-Speed In-Plane Switching Liquid Crystal Cell. IEEE Transactions on Electron Devices. 2016; 63 (11):4326-4330.

Chicago/Turabian Style

Byung-June Mun; Ki-Woong Park; Ji-Ho Baek; Byeong Koo Kim; Hyun Chul Choi; Changwan Kim; Bongsoon Kang; Seung Hee Lee; Gi-Dong Lee. 2016. "Time-Sequential Ultraviolet Exposure to Alignment Layers Embedded With Reactive Mesogen for High-Speed In-Plane Switching Liquid Crystal Cell." IEEE Transactions on Electron Devices 63, no. 11: 4326-4330.

Journal article
Published: 01 August 2016 in Automation in Construction
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The demand for three-dimensional (3D) modeling of as-built or as-is pipelines that occupy large areas in operating plants has been growing in recent times. In practice, although measurements can be efficiently performed by the use of laser-scanning technology, generating a pipeline model from laser-scan data remains challenging, possibly due to the data characteristics. This paper proposes a method for generating 3D models of entire pipelines, including straight pipes, elbows, reducers, and tee pipes, from laser-scan data. The proposed method comprises three main tasks: (1) identifying the existence and location of entire pipelines from laser-scan data, (2) segmenting each pipeline surface into its constituent forms (straight pipe, elbow, reducer, and tee), and (3) reconstructing the geometry of the individual pipelines and generating a 3D model of it. Field experiments were performed at two different operating industrial plants to validate the method. Evaluation of the quantitative results reveals that the proposed method can indeed be used for the automation of 3D modeling of the pipelines in industrial plants—and to be capable of producing such models with high accuracy.

ACS Style

Hyojoo Son; Changwan Kim. Automatic segmentation and 3D modeling of pipelines into constituent parts from laser-scan data of the built environment. Automation in Construction 2016, 68, 203 -211.

AMA Style

Hyojoo Son, Changwan Kim. Automatic segmentation and 3D modeling of pipelines into constituent parts from laser-scan data of the built environment. Automation in Construction. 2016; 68 ():203-211.

Chicago/Turabian Style

Hyojoo Son; Changwan Kim. 2016. "Automatic segmentation and 3D modeling of pipelines into constituent parts from laser-scan data of the built environment." Automation in Construction 68, no. : 203-211.