AI-based risk assessment for construction site disaster preparedness through deep learning-based digital twinning

Kamari, Mirsalar; Ham, Youngjib

doi:10.1016/j.autcon.2021.104091

Cited by 46 publications

(23 citation statements)

References 42 publications

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“…Generally, the early work using AI in construction risk management has been promising, highlighting the technology's ability to identify, analyze, and mitigate risks more effectively than traditional methods (Abioye et al. , 2021; Kamari and Ham, 2022).…”

Section: Literature Backgroundmentioning

confidence: 99%

Can ChatGPT exceed humans in construction project risk management?

Nyqvist,

Peltokorpi,

Seppänen

2024

ECAM

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PurposeThe objective of this research is to investigate the capabilities of the ChatGPT GPT-4 model, a form of artificial intelligence (AI), in comparison to human experts in the context of construction project risk management.Design/methodology/approachEmploying a mixed-methods approach, the study draws a qualitative and quantitative comparison between 16 human risk management experts from Finnish construction companies and the ChatGPT AI model utilizing anonymous peer reviews. It focuses primarily on the areas of risk identification, analysis, and control.FindingsChatGPT has demonstrated a superior ability to generate comprehensive risk management plans, with its quantitative scores significantly surpassing the human average. Nonetheless, the AI model's strategies are found to lack practicality and specificity, areas where human expertise excels.Originality/valueThis study marks a significant advancement in construction project risk management research by conducting a pioneering blind-review study that assesses the capabilities of the advanced AI model, GPT-4, against those of human experts. Emphasizing the evolution from earlier GPT models, this research not only underscores the innovative application of ChatGPT-4 but also the critical role of anonymized peer evaluations in enhancing the objectivity of findings. It illuminates the synergistic potential of AI and human expertise, advocating for a collaborative model where AI serves as an augmentative tool, thereby optimizing human performance in identifying and managing risks.

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Section: Literature Backgroundmentioning

confidence: 99%

Can ChatGPT exceed humans in construction project risk management?

Nyqvist,

Peltokorpi,

Seppänen

2024

ECAM

View full text Add to dashboard Cite

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“…Finally, the actual progress was integrated into a BIM model using self-adaptive grid-based mapping. Kamari and Ham [28] assessed windblown debris hazards at construction sites by utilizing UAVs to create digital models. They captured aerial images of the study area to reconstruct 3D digital models, then conducted image segmentation to identify windblown debris and projected their findings on the digital model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Wei et al [27] point out that there is a lack of research on the development of real-time progress monitoring systems with other digital construction technologies, such as Building Information Modeling for foundation construction in different environments and weather conditions. Kamari and Ham [28] mention that there is a lack of studies using daily construction images, building information modeling, and morphological image processing techniques to monitor and evaluate the progress and degree of clutter on construction sites. According to Zhang et al [29], existing image-to-BIM registration methods require high-precision GPS data, which is only available for UAV RTK, or rely on matching specific textural and geometric features, which are generally absent on building facades.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Physical Progress of Work Using UAV and BIM in Construction Projects

Palomino Ojeda,

Quiñones Huatangari,

Cayatopa Calderon

et al. 2024

Civ Eng J

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The delay in the physical progress of construction creates additional costs, missed deadlines, and quality issues. The research aimed to estimate the physical progress of the project by using unmanned aerial vehicles (UAVs) and building information modeling (BIM). The methodology comprised capturing 848 high-resolution images of the Civil Engineering Laboratory construction site at the National University of Jaen, Cajamarca, Peru, using the Phantom 4 RTK drone. The photographs were processed using Agisoft 2.0.1 software, resulting in a point cloud. This was then imported into ReCap Pro 2023 software, which was used to assess the quality of the points. The Revit 2023 software was subsequently utilized to establish the phase parameters, linking the BIM model with the point cloud, filtering the model, and eventually exporting it to the Power BI 2023 software. The work's estimated progress utilizing the proposed methodology was 42.82%, which was not statistically significant compared to the Public Works Information System (INFOBRAS) of 43.14%. This allows for the automation of customary processes, the identification of crucial issues, and prompt decision-making. The study's originality lies in the suggestion of integrating aerial imagery with drones and BIM modeling for the real-time and precise estimation of work progression. This method provides a precise and effective substitute for traditional techniques for gauging the tangible advancement of projects. Doi: 10.28991/CEJ-2024-010-02-02 Full Text: PDF

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“…DT combined with artificial intelligence is significant for improving construction quality and ensuring management safety (Kamari & Ham, 2022). Zhidchenko et al (2018) simulated machine dynamics based on DT to predict the fatigue life of construction machinery engines.…”

Section: Literature Reviewmentioning

confidence: 99%

Earthwork digital twin for teleoperation of an automated bulldozer in edge dumping

You

Zhou

Ding

et al. 2023

Journal of Field Robotics

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The unstructured construction site with a complex and changeable environment brings challenges to human–machine interaction. In this study, the earthwork digital twin (DT) is proposed to realize accurate and real‐time perception, which can support the teleoperation of an automated bulldozer. The overall framework includes anterior‐time DT, real‐time DT, and posterior‐time DT, which integrates three‐dimensional modeling, edge detection, and blade trajectory algorithms. Multidimensional heterogeneous data in bulldozer teleoperation can be displayed in real‐time to ensure construction safety. Big data during teleoperation of the construction process can be collected, stored, and analyzed. The DT proposed in this study was successfully applied in a major construction project in China, and testing results show its universality, robustness, and advanced performance. The key technologies proposed in this study can be applied to solve the common problems in the construction industry, which is promising for future intelligent construction.

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AI-based risk assessment for construction site disaster preparedness through deep learning-based digital twinning

Cited by 46 publications

References 42 publications

Can ChatGPT exceed humans in construction project risk management?

Can ChatGPT exceed humans in construction project risk management?

Estimation of the Physical Progress of Work Using UAV and BIM in Construction Projects

Earthwork digital twin for teleoperation of an automated bulldozer in edge dumping

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