Toward Integrated Human-Machine Intelligence for Civil Engineering: An Interdisciplinary Perspective

Zhang, Cheng; Kim, Jin-Woo; Jeon, Jungho Choi and Dongryul; Xing, Jinding; Tang, Pingbo; Cai, Hubo

doi:10.1061/9780784483893.035

Cited by 5 publications

(5 citation statements)

References 10 publications

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“…• Barriers to collecting detailed behavioral data from humans and contextual environments [34]. 3) Potential benefits of using AI and machine learning for monitoring civil engineering systems and infrastructure:…”

Section: Maintenance and Monitoringmentioning

confidence: 99%

“…• Challenges with data availability, AI model definition, and ethical considerations [16]. • Need for collaborative partnerships and overcoming knowledge gaps [34]. • Potential impacts on job site injuries and industry business models [40].…”

Section: Maintenance and Monitoringmentioning

confidence: 99%

“…These technologies can improve the efficiency of civil engineering projects by providing solutions to challenges such as damage inspection, safety risk management, and information modeling [1]. However, there are also challenges in applying AI and machine learning to civil engineering, such as the need to define the interface between humans and AI, collect detailed behavioral data, and address ethical considerations [39,19,34].…”

Section: Introductionmentioning

confidence: 99%

“…Overall, the integration of AI and machine learning in civil engineering offers many possibilities for improving efficiency and addressing challenges. However, it is important to consider ethical considerations and continue research and development to fully realize the potential of these technologies in the field [19,34].…”

Section: Introductionmentioning

confidence: 99%

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Machine learning methods in civil engineering: a systematic review

Kamolov

2024

AMCS

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Section: Maintenance and Monitoringmentioning

confidence: 99%

Section: Maintenance and Monitoringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine learning methods in civil engineering: a systematic review

Kamolov

2024

AMCS

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“…Recently, adopting modern technological solutions such as deep learning algorithms, computer vision, and sensor networks has created unprecedented opportunities to automate the process of identifying welding hazards beyond the inherent limitations (e.g., labor intensiveness and human errors) of traditional observatory approaches such as adding one more person to observe welding activities [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. For example, Chen, W. et al [ 33 ] proposed a progressive probabilistic transformer-based welding flame detection method.…”

Section: Introductionmentioning

confidence: 99%

Welding Spark Detection on Construction Sites Using Contour Detection with Automatic Parameter Tuning and Deep-Learning-Based Filters

Jin,

Ahn,

Kim

et al. 2023

Sensors

Self Cite

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One of the primary causes of fires at construction sites is welding sparks. Fire detection systems utilizing computer vision technology offer a unique opportunity to monitor fires in construction sites. However, little effort has been made to date in regard to real-time tracking of small sparks that can lead to major fires at construction sites. In this study, a novel method is proposed to detect welding sparks in real-time contour detection with deep learning parameter tuning. An automatic parameter tuning algorithm employing a convolutional neural network was developed to identify the optimum hue saturation value. Additional filtering methods regarding the non-welding zone and a contour area-based filter were also newly developed to enhance the accuracy of welding spark prediction. The method was evaluated using 230 welding spark images and 104 videos. The results obtained from the welding images indicate that the suggested model for detecting welding sparks achieves a precision of 74.45% and a recall of 63.50% when noise images, such as flashing and reflection light, were removed from the dataset. Furthermore, our findings demonstrate that the proposed model is effective in capturing the number of welding sparks in the video dataset, with a 95.2% accuracy in detecting the moment when the number of welding sparks reaches its peak. These results highlight the potential of automated welding spark detection to enhance fire surveillance at construction sites.

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Question-Answering System Powered by Knowledge Graph and Generative Pretrained Transformer to Support Risk Identification in Tunnel Projects

Isah,

Kim

2025

J. Constr. Eng. Manage.

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Toward Integrated Human-Machine Intelligence for Civil Engineering: An Interdisciplinary Perspective

Cited by 5 publications

References 10 publications

Machine learning methods in civil engineering: a systematic review

Machine learning methods in civil engineering: a systematic review

Welding Spark Detection on Construction Sites Using Contour Detection with Automatic Parameter Tuning and Deep-Learning-Based Filters

Question-Answering System Powered by Knowledge Graph and Generative Pretrained Transformer to Support Risk Identification in Tunnel Projects

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