• International conference on construction engineering and project management
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• 2024.07a
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• pp.285-292
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• 2024

Physiological devices and immersive technologies are crucial innovations being implemented for construction safety. Physiological devices provide insights into the wellbeing of workers while immersive technologies have a potential to simulate or enhance construction environments. These two technologies present numerous benefits for construction safety and have been extensively implemented in various dimensions. In addition to the individual benefits of these two technologies, combining them presents more opportunities for construction safety research and numerous studies have been conducted using this approach. However, despite promising results achieved by studies which have used this technological combination, no review has been conducted to summarize the findings of these studies. This review therefore summarizes studies that have combined immersive technologies with physiological monitoring for construction safety. A systematic approach is employed, and 24 articles are reviewed. This review highlights four safety aspects which have been explored using a combination of immersive technologies and physiological monitoring. These aspects are (1) Safety training and evaluation (2) Hazard identification (3) Attention assessment and (4) Cognitive strain assessment. In addition, there are three main directions for future research. (1) Future studies should explore other types of immersive technologies such as immersive audio (2) Physiological reactions to hazard exposure should be studied and (3) More multi-physiological approaches should be adopted.

• Journal of Korean Society of Disaster and Security
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• v.7 no.2
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• pp.9-16
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• 2014

Though the maintenance monitoring of structurally weak parts of the tunnel structure in public service must be judged in connection with the monitoring during the construction period for analysis of the behavior of the ground and surrounding structures following the tunnel excavation for the effective management, the monitoring during the construction period and the maintenance monitoring are implemented separately on the basis of the periods of construction and maintenance, so the connectivity and systematic management of the related data are mostly inadequate. The improvement direction is suggested in this thesis, by analyzing the problems of tunnel monitoring in the domestic design criteria. And, it is anticipated that from now on the use of hi-tech sensors and wireless communications technology will proceed vigorously in the maintenance, so considering these situations, the development and application of the maintenance monitoring system and the revision of the domestic design criteria and specification are needed in the future.

• Journal of Drive and Control
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• v.19 no.4
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• pp.29-35
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• 2022

In this paper, we developed a workload index for monitoring the durability test using operation information of an excavator. First, the acceleration and cylinder pressure were selected as load factors by analyzing operation data. Through load correlation analysis according to each load factor, Root Mean Square (RMS) and Work Load Range (WLR) were respectively derived as a load feature representing mechanical load. In addition, the workload index was used to quantify load features. For applying the workload index to monitoring, a real-time monitoring system consisting of sensors and embedded controller was installed on the excavator and the system was integrated with a remote monitoring environment using a wireless network. Results of load monitoring and analysis verified that the developed workload index was effective from the viewpoint of the relative comparison of the workload.

• Journal of the Korea Institute of Building Construction
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• v.21 no.5
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• pp.397-408
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• 2021

The construction industry has the highest occupational accidents/injuries and has experienced the most fatalities among entire industries. Korean government installed surveillance camera systems at construction sites to reduce occupational accident rates. Construction safety managers are monitoring potential hazards at the sites through surveillance system; however, the human capability of monitoring surveillance system with their own eyes has critical issues. A long-time monitoring surveillance system causes high physical fatigue and has limitations in grasping all accidents in real-time. Therefore, this study aims to build a deep learning-based safety monitoring system that can obtain information on the recognition, location, identification of workers and heavy equipment in the construction sites by applying multiple object tracking with instance segmentation. To evaluate the system's performance, we utilized the Microsoft common objects in context and the multiple object tracking challenge metrics. These results prove that it is optimal for efficiently automating monitoring surveillance system task at construction sites.

• International Journal of High-Rise Buildings
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• v.1 no.1
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• pp.37-51
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• 2012

New generation of tall and complex buildings systems are now introduced that are reflective of the latest development in materials, design, sustainability, construction, and IT technologies. While the complexity in design is being overcome by the availability and advances in structural analysis tools and readily advanced software, the design of these buildings are still reliant on minimum code requirements that yet to be validated in full scale. The involvement of the author in the design and construction planning of Burj Khalifa since its inception until its completion prompted the author to conceptually develop an extensive survey and real-time structural health monitoring program to validate all the fundamental assumptions mad for the design and construction planning of the tower. The Burj Khalifa Project is the tallest structure ever built by man; the tower is 828 meters tall and comprises of 162 floors above grade and 3 basement levels. Early integration of aerodynamic shaping and wind engineering played a major role in the architectural massing and design of this multi-use tower, where mitigating and taming the dynamic wind effects was one of the most important design criteria established at the onset of the project design. Understanding the structural and foundation system behaviors of the tower are the key fundamental drivers for the development and execution of a state-of-the-art survey and structural health monitoring (SHM) programs. Therefore, the focus of this paper is to discuss the execution of the survey and real-time structural health monitoring programs to confirm the structural behavioral response of the tower during construction stage and during its service life; the monitoring programs included 1) monitoring the tower's foundation system, 2) monitoring the foundation settlement, 3) measuring the strains of the tower vertical elements, 4) measuring the wall and column vertical shortening due to elastic, shrinkage and creep effects, 5) measuring the lateral displacement of the tower under its own gravity loads (including asymmetrical effects) resulting from immediate elastic and long term creep effects, 6) measuring the building lateral movements and dynamic characteristic in real time during construction, 7) measuring the building displacements, accelerations, dynamic characteristics, and structural behavior in real time under building permanent conditions, 8) and monitoring the Pinnacle dynamic behavior and fatigue characteristics. This extensive SHM program has resulted in extensive insight into the structural response of the tower, allowed control the construction process, allowed for the evaluation of the structural response in effective and immediate manner and it allowed for immediate correlation between the measured and the predicted behavior. The survey and SHM programs developed for Burj Khalifa will with no doubt pioneer the use of new survey techniques and the execution of new SHM program concepts as part of the fundamental design of building structures. Moreover, this survey and SHM programs will be benchmarked as a model for the development of future generation of SHM programs for all critical and essential facilities, however, but with much improved devices and technologies, which are now being considered by the author for another tall and complex building development, that is presently under construction.

• Proceedings of the Korea Concrete Institute Conference
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• 1996.10a
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• pp.520-525
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• 1996

Recently, the prestressed concrete long-span bridegs are increasingly built at various locations in the world. The mechanical and structural behavior of prestressed concrete bridges is very complex because of nonlinear and time-dependent material behavior and sequential change of structural system due to stepwise construction. These factors may cause construction errors with respect to design value and monitoring system is needed to minimize or to protect construction errors. This study presents the basis development of monitoring system for precise construction of large scale prestressed concrete bridges.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.455-462
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• 2024

In the construction industry, known for its dynamic and hazardous environments, there exists a crucial demand for effective safety incident prevention. Traditional approaches to monitoring on-site safety, despite their importance, suffer from being laborious and heavily reliant on subjective, paper-based reports, which results in inefficiencies and fragmented data. Additionally, the incorporation of computer vision technologies for automated safety monitoring encounters a significant obstacle due to the lack of suitable training datasets. This challenge is due to the rare availability of safety accident images or videos and concerns over security and privacy violations. Consequently, this paper explores an innovative method to address the shortage of safety-related datasets in the construction sector by employing generative artificial intelligence (AI), specifically focusing on the Stable Diffusion model. Utilizing real-world construction accident scenarios, this method aims to generate photorealistic images to enrich training datasets for safety surveillance applications using computer vision. By systematically generating accident prompts, employing static prompts in empirical experiments, and compiling datasets with Stable Diffusion, this research bypasses the constraints of conventional data collection techniques in construction safety. The diversity and realism of the produced images hold considerable promise for tasks such as object detection and action recognition, thus improving safety measures. This study proposes future avenues for broadening scenario coverage, refining the prompt generation process, and merging artificial datasets with machine learning models for superior safety monitoring.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.12
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• pp.6550-6557
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• 2013

The public road construction projects awarded by the regional construction and management office, which is an affiliate of the Ministry of Land, Infrastructure and Transport, are managed by construction supervision officers. These officials frequently visit a large number of construction sites to conduct inspections and supervision tasks. Therefore, the site management efficiency is essential in terms of the time and money spent in travelling to the sites. The introduction of a site monitoring management system is considered necessary to minimize the number of site visits and enable remote monitoring of the construction progress to enhance the business efficiency of the construction supervision officers. In this study, a remote site monitoring system was constructed using web cameras for public road construction works. The trial applications were implemented by selecting ten constructions sites. The effectiveness of the system was analyzed to assess its applicability. In an assessment of the applicability of the verification results, remote site monitoring showed cost savings of approximately 35% compared to the existing site management. The guidelines for applying the site monitoring management system were provided, the introduction plan was investigated, and the improvement method was presented. The results showed that the system is likely to minimize the unnecessary site visits, remove the risk factors at vulnerable areas in the sites beforehand, and prevent a range of disasters and accidents. In addition, the quality of the infrastructures is likely to improve through the prevention of accidents and the elimination of substandard and faulty construction work.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.30-31
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• 2009

Early detection of schedule delay in field construction activities is vital to project management. It provides the opportunity to initiate remedial actions and increases the chance of controlling such overruns or minimizing their impacts. This entails project managers to design, implement, and maintain a systematic approach for progress monitoring to promptly identify, process and communicate discrepancies between actual and as-planned performances as early as possible. Despite importance, systematic implementation of progress monitoring is challenging: (1) Current progress monitoring is time-consuming as it needs extensive as-planned and as-built data collection; (2) The excessive amount of work required to be performed may cause human-errors and reduce the quality of manually collected data and since only an approximate visual inspection is usually performed, makes the collected data subjective; (3) Existing methods of progress monitoring are also non-systematic and may also create a time-lag between the time progress is reported and the time progress is actually accomplished; (4) Progress reports are visually complex, and do not reflect spatial aspects of construction; and (5) Current reporting methods increase the time required to describe and explain progress in coordination meetings and in turn could delay the decision making process. In summary, with current methods, it may be not be easy to understand the progress situation clearly and quickly. To overcome such inefficiencies, this research focuses on exploring application of unsorted daily progress photograph logs - available on any construction site - as well as IFC-based 4D models for progress monitoring. Our approach is based on computing, from the images themselves, the photographer's locations and orientations, along with a sparse 3D geometric representation of the as-built scene using daily progress photographs and superimposition of the reconstructed scene over the as-planned 4D model. Within such an environment, progress photographs are registered in the virtual as-planned environment, allowing a large unstructured collection of daily construction images to be interactively explored. In addition, sparse reconstructed scenes superimposed over 4D models allow site images to be geo-registered with the as-planned components and consequently, a location-based image processing technique to be implemented and progress data to be extracted automatically. The result of progress comparison study between as-planned and as-built performances can subsequently be visualized in the D4AR - 4D Augmented Reality - environment using a traffic light metaphor. In such an environment, project participants would be able to: 1) use the 4D as-planned model as a baseline for progress monitoring, compare it to daily construction photographs and study workspace logistics; 2) interactively and remotely explore registered construction photographs in a 3D environment; 3) analyze registered images and quantify as-built progress; 4) measure discrepancies between as-planned and as-built performances; and 5) visually represent progress discrepancies through superimposition of 4D as-planned models over progress photographs, make control decisions and effectively communicate those with project participants. We present our preliminary results on two ongoing construction projects and discuss implementation, perceived benefits and future potential enhancement of this new technology in construction, in all fronts of automatic data collection, processing and communication.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.471-478
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• 2024

Effective physical fatigue monitoring is crucial for ensuring the health, safety, and productivity of construction workers, given the physically demanding nature of their work and the challenging environment in which they operate. In recent years, wearable sensors have shown growing potential for physical fatigue monitoring among construction workers. However, such fatigue assessment methods exhibit a significant gap as they often overlook the impact of inter-individual variability, such as differences in height, weight, and body mass index, on physiological signals that indicate physical fatigue. Therefore, this study aimed to investigate the role of personal factors in altering physiological responses, thereby improving the reliability and accuracy of fatigue monitoring using wearable physiological sensors. To explore the impact of these inter-individual factors, we experimentally analyzed the relationship between personal characteristics, physiological signals, and physical fatigue. Our findings reveal that although the inter-individual factors may not be directly correlated with fatigue levels, they significantly affect fatigue through their influence on physiological signals. Incorporation of these factors into a random forest predictive model significantly enhanced its predictive performance. Furthermore, integrating personal features with other variables to create new features in the physical fatigue prediction model notably improves its accuracy, highlighting the potential for developing personalized fatigue detection systems.