• International conference on construction engineering and project management
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• 2024.07a
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• pp.1073-1080
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• 2024

The imperative for real-time automatic construction progress monitoring (ACPM) to avert project delays is widely acknowledged in construction project management. Current ACPM methodologies, however, face a challenge as they rely on collecting data from construction sites and processing it offline for progress analysis. This delayed approach poses a risk of late identification of critical construction issues, potentially leading to rework and subsequent project delays. This research introduces a real-time construction progress monitoring framework that integrates cutting-edge semantic Simultaneous Localization and Mapping (SLAM) techniques. The innovation lies in the framework's ability to promptly identify structural components during site inspections conducted through a robotic system. Incorporating deep learning models, specifically those employing semantic segmentation, enables the system to swiftly acquire and process real-time data, identifying specific structural components and their respective locations. Furthermore, by seamlessly integrating with Building Information Modeling (BIM), the system can effectively evaluate and compare the progress status of each structural component. This holistic approach offers an efficient and practical real-time progress monitoring solution for construction projects, ensuring timely issue identification and mitigating the risk of project delays.

• 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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• 2009.05a
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• pp.920-927
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• 2009

Many schedule delays and cost overruns in interior construction are caused by a lack of understanding in detailed and complicated interior works. To minimize these potential impacts in interior construction, a systematic approach for project managers to detect discrepancies at early stages and take corrective action through use of visualized data is required. This systematic implementation is still challenging: monitoring is time-consuming due to the significant amount of as-built data that needs to be collected and evaluated; and current interior construction progress reports have visual limitations in providing spatial context and in representing the complexities of interior components. To overcome these issues, this research focuses on visualization and computer vision techniques representing interior construction progress with photographs. The as-planned 3D models and as-built photographs are visualized in a 3D walk-through model. Within such an environment, the as-built interior construction elements are detected through computer vision techniques to automatically extract the progress data linked with Building Information Modeling (BIM). This allows a comparison between the as-planned model and as-built elements to be used for the representation of interior construction progress by superimposing over a 3D environment. This paper presents the process of representing and detecting interior construction components and the results for an ongoing construction project. This paper discusses implementation and future potential enhancement of these techniques in construction.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.317-320
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• 2015

Documenting work-in-progress on construction sites using images captured with smartphones, point-and-shoot cameras, and Unmanned Aerial Vehicles (UAVs) has gained significant popularity among practitioners. The spatial and temporal density of these large-scale site image collections and the availability of 4D Building Information Models (BIM) provide a unique opportunity to develop BIM-driven visual analytics that can quickly and easily detect and visualize construction progress deviations. Building on these emerging sources of information this paper presents a pipeline for model-driven visual analytics of construction progress. It particularly focuses on the following key steps: 1) capturing, transferring, and storing images; 2) BIM-driven analytics to identify performance deviations, and 3) visualizations that enable root-cause assessments on performance deviations. The information requirements, and the challenges and opportunities for improvements in data collection, plan preparations, progress deviation analysis particularly under limited visibility, and transforming identified deviations into performance metrics to enable root-cause assessments are discussed using several real world case studies.

• International conference on construction engineering and project management
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• 2017.10a
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• pp.252-259
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• 2017

Nowadays, the existing manual method for recording actual progress of the construction site has some drawbacks, such as great reliance on the experience of professional engineers, work-intensive, time consuming and error prone. A method integrating computer vision and BIM(Building Information Modeling) is presented for indoor automatic progress monitoring. The developed method can accurately calculate the engineering quantity of target component in the time-lapse images. Firstly, sample images of on-site target are collected for training the classifier. After the construction images are identified by edge detection and classifier, a voting algorithm based on mathematical geometry and vector operation will divide the target contour. Then, according to the camera calibration principle, the image pixel coordinates are conversed into the real world Coordinate and the real coordinates would be corrected with the help of the geometric information in BIM model. Finally, the actual engineering quantity is calculated.

• 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.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2015.05a
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• pp.130-131
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• 2015

Accurate construction progress measurement is an important issue for successful project delivery. This paper presents a method that keeps track of the progress measurement involved in construction operations and facilities visualization of the data using BIM and IOT. To verify the method, a residential house project was used for the case study. Test case verifies the usability and validity of the method implemented in the system.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.491-496
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• 2005

In this paper, the authors explained the overview of a method for detecting location of worker for development of a monitoring system for project progress management using RFID technology. Data is the RSSI (Receive Signal Strength Indication) from RFID tag attached to the worker installing a rolling shutter, and was obtained from antennas and RFID readers set around the construction site. Neural network was done using RSSI collected and the area where worker is performing a task, and an estimation model of the working area was prepared. The network had a range of the percentage of correctly classified from 62% to 92%. The authors suggested the method to make estimate by using integrated networks prepared in respect of RFID readers, and showed the percentage of correctly classified of 84.3%. According to the result, the authors confirmed the possibility of the monitoring system with RFID technology, and mentioned the factors necessary to develop for further practical use.

• 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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• 2007.03a
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• pp.50-60
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• 2007

The endeavor of this paper focuses on designing a monitoring system to provide a cost-effective solution on quality assurance for construction projects. The construction site monitoring system integrates a long-range wireless network, network cameras, and a web-based collaborative platform. The users of the system could obtain the most updated status of construction sites, such as behaviors of workers, project progress, and site events anywhere with Internet connectivity. It was carefully configured in order to maintain the reliability under the reactive conditions of the construction sites. This paper reports the architecture of the monitoring system and reviews the related technologies. The system has been implemented and tested on a construction site and promising results were obtained.