• Smart Structures and Systems
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• v.15 no.4
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• pp.1019-1039
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• 2015

Shanghai Tower is a composite structure building with a height of 632 m. In order to verify the structural properties and behaviors in construction and operation, a structural health monitoring project was conducted by Tongji University. The monitoring system includes sensor system, data acquisition system and a monitoring software system. Focusing on the health monitoring in construction, this paper introduced the monitoring parameters in construction, the data acquisition strategy and an integration structural health monitoring (SHM) software. The integration software - Structural Monitoring/ Analysis/ Evaluation System (SMAE) is designed based on integration and modular design idea, which includes on-line data acquisition, finite elements and dynamic property analysis functions. With the integration and modular design idea, this SHM system can realize the data exchange and results comparison from on-site monitoring and FEM effectively. The analysis of the monitoring data collected during the process of construction shows that the system works stably, realize data acquirement and analysis effectively, and also provides measured basis for understanding the structural state of the construction. Meanwhile, references are provided for the future automates construction monitoring and implementation of high-rise building structures.

• International Journal of Highway Engineering
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• v.14 no.6
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• pp.57-65
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• 2012

PURPOSES : Efficiency Improvement of a public road construction project management work using the development of a real-time remote site monitoring system METHODS : In this study, we developed the remote site monitoring system using a web camera for road construction projects in the RCMA(Regional Construction Management Administration). We can be monitored a construction progress and a weak point of the situation in real time using this. To achieve this, we tested about 10 road construction projects ordered by RCMA. Then, we verified a applicability for the site monitoring system in future. RESULTS : Take advantage of the remote site monitoring system consists of the Construction CALS System, one of the business systems used in the part of the MLTM(Ministry of Land, Transport and Maritime Affairs) institution-agencies. Was configured to be served through the "Construction CALS System" of "Construction Management System(Contractors)" and the "Construction CALS Portal System". Through this, Benefit analysis through a pilot application of the 10 road construction sites and developing considerations and "Guide for visual information processing equipment installation-operating in construction sites"are presented. CONCLUSIONS : Through the establishment of remote site monitoring system can improve the efficiency of construction management services. In addition, Various disasters and calamities, accidents and illegal construction will be prevented in advance is expected. This is expected to further improve the quality of the facilities.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.190-195
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• 2009

With the growth of information and communication technologies adoption in construction projects, it could be anticipated that more property owners and construction firms will attempt to use electronic gears and gadgets for site monitoring or surveillance purposes. As the construction workers may be the major group of project team members being monitored, from managerial perspectives and for ethical reasons, it is essential to investigate their degree of acceptance on site monitoring systems. Indeed studies on office workplace monitoring suggest that a monitoring system could shape or control the behaviors of employees. With adequate refinements, their research models could be applicable in the construction industry. This paper presents a model for analyzing the antecedences that affect workers' acceptance level on electronic monitoring, and investigating if there is any behavioral change.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.431-438
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• 2024

Monitoring unsafe activities on construction sites is challenging due to a variety of factors including the diversity of tasks and workers involved, the potential of human error and lack of real-time hazard detection. With technological advancements, several digital technologies have been proposed and applied to improve the monitoring process. Despite the potential of these technological advancements to reduce manual effort in traditional monitoring, the challenge lies in selecting and implementing the technology that best meets the specific needs of contractors. This paper aims to streamline the research of digital technologies in the construction domain by achieving three key objectives: (1) classify the types of unsafe activities that can be monitored automatically, (2) determine the specific data required for effective monitoring processes, and (3) identify the technologies that can facilitate such data collection process. We conduct a systematic literature review on cutting-edge technological studies to achieve the research aims. The findings of this research serve as a valuable resource for construction practitioners, offering insights into both the benefits and limitations of digital technologies in enhancing the monitoring process. Moreover, the study recommends preparatory elements that practitioners should undertake to integrate these technologies effectively into their monitoring frameworks. The study empowers practitioners by providing a deep understanding, enabling them to create a comprehensive safety management program aligned with the digital transformation process.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.12
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• pp.1056-1065
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• 2013

Noise generated from Construction site has been raised most civil complaints to 64.4 % among the environmental pollution in 2011. Therefore, local government recommends and prescribes that construction sites over a certain scale install sound level meter for noise monitoring. For example, Seoul has implemented a 24 hour noise monitoring system, with real time communication, to the large construction sites more than 10,000 $m^2$ from the end of August 2012. But it is difficult to use noise measurement data for the construction noise assessment, since the installation standards and technical specifications for construction noise monitoring system are not presented. In this paper, we proposed noise monitoring system improvement plan including technical specifications and installation standards using the investigating results of the problems of current noise monitoring system and the foreign cases.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.775-782
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• 2024

This systematic review comprehensively analyzes the application of computer vision in construction productivity measurement and emphasizes the importance of worker accountability in construction sites. It identifies a significant gap in the connection level between input (resources) and output data (products or progress) of productivity monitoring, a factor not adequately addressed in prior research. The review highlights three fundamental groups: input, output, and connection groups. Object detection, tracking, pose, and activity recognition, as the input stage, are essential for identifying characteristics and worker movements. The output phase will mostly focus on progress monitoring, and understanding the interaction of workers with other entities will be discussed in the connection groups. This study offers four research future research directions for the worker accountability monitoring process, such as human-object interaction (HOI), generative AI, location-based management systems (LBMS), and robotic technologies. The successful accountability monitoring will secure the accuracy of productivity measurement and elevate the competitiveness of the construction industry.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.423-430
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• 2024

The task of vision safety monitoring in construction environments presents a formidable challenge, owing to the dynamic and heterogeneous nature of these settings. Despite the advancements in artificial intelligence, the nuanced analysis of small or tiny personal protective equipment (PPE) remains a complex endeavor. In response to this challenge, this paper introduces an innovative safety monitoring system, specifically designed to enhance the safety monitoring of working both at ground level and at elevated heights. This novel system integrates a suite of sophisticated technologies: instance segmentation, shape classification, object tracking, a visualization report, and a real-time notification module. Collectively, these components coalesce to deliver a safety monitoring solution, ensuring a higher standard of protection for construction workers. The experimental results…..

• International conference on construction engineering and project management
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• 2013.01a
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• pp.188-195
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• 2013

Construction operations generate a significant amount of air pollutant emissions, including carbon emissions. The environmental performance of construction operations is closely relevant to the operational efficiency of each resource employed, which indicates how efficiently each resource (e.g., construction equipment) is utilized. In this context, monitoring the operational efficiency of construction equipment provides key information in managing and improving the environmental performance and productivity of construction operations. In this paper, we report our efforts to measure the operational efficiency of construction equipment, using low-cost accelerometers. An experimental study and real-world case studies are conducted to demonstrate the feasibility of the proposed approach. The results have shown the potential of this approach as an economically feasible means of monitoring the environmental performance of construction operations.

• Journal of the Society of Disaster Information
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• v.15 no.1
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• pp.28-38
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• 2019

Purpose: The purpose of this study is to contribute to improvement of measurement management level of construction structure and technology development of monitoring sensor by presenting the detailed causes and improvement plans of construction monitoring sensor's decline in durability. Method: The causes and improvement plans of the durability degradation of the construction monitoring sensor were divided into the construction field and the electric, electronic field. The detailed status was reviewed. Results: In the field of construction, approval and inspection, inspection and testing, verification and calibration, and minimization of loss and damage ratio were reviewed. In the field of electric and electronics, sensor package and sealing, disconnection of stress concentration area, damage caused by lightning and corrosion were reviewed. Conclusion: It is expected that the durability of monitoring sensors applied to the construction site will become longer than the present status based on the study that analyzed causes and improvement plans of construction monitoring sensor's decline in durability in the field of construction and electric, electronic devices.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.415-422
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• 2024

Recent advances in construction automation have led to increased use of deep learning-based computer vision technology for construction monitoring. However, monitoring systems based on supervised learning struggle with recognizing complex risk factors in construction environments, highlighting the need for adaptable solutions. Large multimodal models, pretrained on extensive image-text datasets, present a promising solution with their capability to recognize diverse objects and extract semantic information. This paper proposes a methodology that generates training data for multimodal models, including safety-centric descriptions using GPT-4V, and fine-tunes the LLaVA model using the LoRA method. Experimental results from seven construction site hazard scenarios show that the fine-tuned model accurately assesses safety status in images. These findings underscore the proposed approach's effectiveness in enhancing construction site safety monitoring and illustrate the potential of large multimodal models to tackle domain-specific challenges.