Visual Analytics for Operation-Level Construction Monitoring and Documentation: State-of-the-Art Technologies, Research Challenges, and Future Directions

Kim, Jin-Woo

doi:10.3389/fbuil.2020.575738

Cited by 19 publications

(4 citation statements)

References 151 publications

(166 reference statements)

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“…IoT devices, including sensors and drones, offer real-time data collection and monitoring, which are crucial for timely decision-making and issue resolution (Reja et al, 2022). AI and machine learning algorithms can analyze vast amounts of data to predict potential project delays and optimize scheduling processes (Kim, 2020). Automated monitoring systems are increasingly preferred over manual methods due to their efficiency and reliability.…”

Section: Introductionmentioning

confidence: 99%

Innovations in Project Monitoring Tools for Large-Scale Infrastructure Projects

Muritala Omeiza Umar,

Azubuike Chukwudi Okwandu,

Dorcas Oluwajuwonlo Akande

2024

Int. j. manag. entrep. res

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Innovations in project monitoring tools are paramount for the effective execution of large-scale infrastructure projects. This study aims to explore the transformative role of these tools in enhancing project performance, accuracy, and efficiency. The research employs a comprehensive review of existing literature, case studies, and the latest advancements in project monitoring practices. The study highlights that integrating digital tools such as Building Information Modeling (BIM), unmanned aerial vehicles (UAVs), and Geographic Information Systems (GIS) significantly improves project coordination and real-time decision-making. Automated progress monitoring technologies reduce reliance on manual data entry, enhancing the accuracy of project assessments. Additionally, sensor-based monitoring techniques enable predictive maintenance strategies, extending the lifespan of aging infrastructure. The findings underscore the importance of interoperability among digital tools and the need for robust monitoring models capable of adapting to the dynamic nature of infrastructure projects. Web-based applications and systems engineering principles further enhance the efficacy of project monitoring by providing real-time data and facilitating efficient communication among stakeholders. In conclusion, the integration of innovative project monitoring tools is pivotal for the successful execution and management of large-scale infrastructure projects. These technologies not only enhance project performance and reduce risks but also ensure sustainable and long-term infrastructure development. The study recommends continued research and investment in advanced monitoring systems to address existing challenges and unlock new opportunities for improving project outcomes. Embracing these innovations will be crucial for the construction industry to meet future demands and achieve higher standards of efficiency and sustainability. Keywords: Building Information Modeling (BIM), Unmanned Aerial Vehicles (UAVs), Geographic Information Systems (GIS), Automated Progress Monitoring, Predictive Maintenance, Project Performance Optimization.

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Section: Introductionmentioning

confidence: 99%

Innovations in Project Monitoring Tools for Large-Scale Infrastructure Projects

Muritala Omeiza Umar,

Azubuike Chukwudi Okwandu,

Dorcas Oluwajuwonlo Akande

2024

Int. j. manag. entrep. res

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“…In order to increase the efficiency of the construction management, the Internet of Things (IoT) has been developed in the past years to increase the automation of construction management. For instance, sensor-based methods have been used for construction object positioning, proximity calculation, and pose estimation [2,3]; computer-vision-based methods have been used for construction object detection, tracking, and activity recognition [4][5][6]; robotic methods have been used for automatic structure and site environment inspections [7]. Among the current IoT methods, computer-vision (CV)-based methods have been widely adopted with the advantages of a low cost and simple integration in construction automation fields [1].…”

Section: Introductionmentioning

confidence: 99%

“…By observing continuously recorded site surveillance videos, construction managers can identify safety issues, productivity of the workers, and equipment during the construction processes (e.g., workers' hardhats, equipment productivity, etc.) [4,5,8,9]. CV-based methods have been developed by researchers to facilitate construction management procedures by providing automatic productivity and safety control methods for construction managers.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Edge Computing in Computer Vision-Based Construction Resource Detection

Chen

Lian

et al. 2022

Buildings

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The Internet of Things (IoT), including sensors, computer vision (CV), robotics, and visual reality technologies, is widely used in the construction industry to facilitate construction management in productivity and safety control. The application of such technologies in real construction projects requires high-quality computing resources, the network for data transferring, a near real-time response, geographical closeness to the smart environments, etc. Most existing research has focused on the first step of method development and has neglected the further deployment step. For example, when using CV-based methods for construction site monitoring, internet-connected cameras must transmit large quantities of high-quality data to the central office, which may be located thousands of miles away. Not only the quality may suffer due to latency, but the wideband cost can be astronomical. Edge computing devices and systems help solve this problem by providing a local source to process the data. The goal of this study is to embed the CV-based method into devices and thus to develop a practical edge computing system for vision-based construction resource detection, which can provide automatic construction with high-quality and more applicable service. Specifically, this study first developed a CV-based hardhat color detection model to manage workers in different tasks. Then, the model was embedded into a Raspberry Pi microcomputer mainboard for video data processing, and the performance was compared with the local computer to validate the feasibility of the proposed method.

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“…UAV remote monitoring, as an alternative technology to fixed-carrier video monitoring for onsite proximity monitoring [25], can provide construction managers with a clear understanding of construction site conditions and progress [36]. This method also improves construction productivity by properly planning and deploying construction equipment based on monitoring and recording at the operational level [37]. This saves a significant amount of time and reduces costs [2,4,6,38].…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Work Cycles of Earthmoving Excavators in Earthmoving Projects Using UAV Remote Sensing

Wang

Liu

et al. 2021

Remote Sensing

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Monitoring the work cycles of earthmoving excavators is an important aspect of construction productivity assessment. Currently, the most advanced method for the recognition of work cycles is the “Stretching-Bending” Sequential Pattern (SBSP), which is based on fixed-carrier video monitoring (FC-SBSP). However, the application of this method presupposes the availability of preconstructed installation carriers to act as a surveillance camera as well as installed and commissioned surveillance systems that work in tandem with them. Obviously, this method is difficult to apply to projects with no conditions for a monitoring camera installation or which have a short construction time. This highlights the potential application of Unmanned Aerial Vehicle (UAV) remote sensing, which is flexible and mobile. Unfortunately, few studies have been conducted on the application of UAV remote sensing for the work cycle monitoring of earthmoving excavators. This research is necessary because the use of UAV remote sensing for monitoring the work cycles of earthmoving excavators can improve construction productivity and save time and costs, especially in post-disaster reconstruction projects involving harsh construction environments, and emergency projects with short construction periods. In addition, the challenges posed by UAV shaking may have to be taken into account when using the SBSP for UAV remote sensing. To this end, this study used application experiments in which stabilization processing of UAV video data was performed for UAV shaking. The application experimental results show that the work cycle performance of UAV remote-sensing-based SBSP (UAV-SBSP) for UAV video data was 2.45% and 5.36% lower in terms of precision and recall, respectively, without stabilization processing than after stabilization processing. Comparative experiments were also designed to investigate the applicability of the SBSP oriented toward UAV remote sensing. Comparative experimental results show that the same level of performance was obtained for the recognition of work cycles with the UAV-SBSP as compared with the FC-SBSP, demonstrating the good applicability of this method. Therefore, the results of this study show that UAV remote sensing enables effective monitoring of earthmoving excavator work cycles in construction sites where monitoring cameras are not available for installation, and it can be used as an alternative technology to fixed-carrier video monitoring for onsite proximity monitoring.

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Visual Analytics for Operation-Level Construction Monitoring and Documentation: State-of-the-Art Technologies, Research Challenges, and Future Directions

Cited by 19 publications

References 151 publications

Innovations in Project Monitoring Tools for Large-Scale Infrastructure Projects

Innovations in Project Monitoring Tools for Large-Scale Infrastructure Projects

Investigation of Edge Computing in Computer Vision-Based Construction Resource Detection

Monitoring the Work Cycles of Earthmoving Excavators in Earthmoving Projects Using UAV Remote Sensing

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