A Novel Implementation of an AI-Based Smart Construction Safety Inspection Protocol in the UAE

Shanti, Mohammad Z.; Cho, Chung-Suk; Byon, Young-Ji; Yeun, Chan Yeob; Kim, Tae Yeon; Kim, Song-Kyoo; Altunaiji, Ahmed

doi:10.1109/access.2021.3135662

Cited by 21 publications

(8 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One notable application is predictive analytics, which leverages historical data and machine learning algorithms to forecast potential project delays, material shortages, and cost overruns [1,9,10]. Another vital area is autonomous construction equipment, such as self-driving bulldozers and excavators, which streamline excavation and grading processes while reducing labour costs [11][12][13]. Computer vision systems enable real-time monitoring of construction sites, enhancing safety by detecting potential hazards and ensuring compliance with safety protocols [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence (AI)-Based Technology Adoption in the Construction Industry: A Cross National Perspective Using the Technology Acceptance Model

Na,

Heo,

Choi

et al. 2023

Buildings

View full text Add to dashboard Cite

The research has chosen the workers in construction-related companies in South Korea and the United Kingdom (UK) as research subjects in order to analyse factors that influence their usage intention of Artificial Intelligence (AI) based technologies. The perceived usefulness had a positive impact (+) on technological satisfaction and usage intention in terms of the commonalities shown by the construction industry workers in both countries, South Korea and the UK, in adopting AI-based technologies. Moreover, the most remarkable differences were personal competence and social influence when choosing AI-based technologies. It was analysed that in the case of South Korea, personal competence had a positive impact (+) on perceived ease of use, whereas the UK had a positive impact (+) on perceived usefulness and perceived ease of use. This study holds particular significance in the domain of cross-cultural research within the construction industry. It conducts an analysis of the factors influencing the adoption of AI-driven technologies or products, with a specific focus on the cultural differences between two nations: South Korea and the UK, which represent Eastern and Western cultural paradigms, respectively.

show abstract

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence (AI)-Based Technology Adoption in the Construction Industry: A Cross National Perspective Using the Technology Acceptance Model

Na,

Heo,

Choi

et al. 2023

Buildings

View full text Add to dashboard Cite

show abstract

“…YOLOv3 overcomes the limitations of YOLOv2 and YOLOv1 by detecting features at three different scales, especially in the detection of smaller objects [25]. [26], where the yellow box in the MRI shows the location of the tumor. Figure 6.…”

Section: Yolov3mentioning

confidence: 99%

“…Figure 6. Architecture of the YOLOv3 model[26], where the yellow box in the MRI shows the location of the tumor. Figure6.…”

mentioning

confidence: 99%

A Deep Learning Approach for Brain Tumor Firmness Detection Based on Five Different YOLO Versions: YOLOv3–YOLOv7

Alhussainan,

Ben Youssef,

Ben Ismail

2024

Computation

View full text Add to dashboard Cite

Brain tumor diagnosis traditionally relies on the manual examination of magnetic resonance images (MRIs), a process that is prone to human error and is also time consuming. Recent advancements leverage machine learning models to categorize tumors, such as distinguishing between “malignant” and “benign” classes. This study focuses on the supervised machine learning task of classifying “firm” and “soft” meningiomas, critical for determining optimal brain tumor treatment. The research aims to enhance meningioma firmness detection using state-of-the-art deep learning architectures. The study employs a YOLO architecture adapted for meningioma classification (Firm vs. Soft). This YOLO-based model serves as a machine learning component within a proposed CAD system. To improve model generalization and combat overfitting, transfer learning and data augmentation techniques are explored. Intra-model analysis is conducted for each of the five YOLO versions, optimizing parameters such as the optimizer, batch size, and learning rate based on sensitivity and training time. YOLOv3, YOLOv4, and YOLOv7 demonstrate exceptional sensitivity, reaching 100%. Comparative analysis against state-of-the-art models highlights their superiority. YOLOv7, utilizing the SGD optimizer, a batch size of 64, and a learning rate of 0.01, achieves outstanding overall performance with metrics including mean average precision (99.96%), precision (98.50%), specificity (97.95%), balanced accuracy (98.97%), and F1-score (99.24%). This research showcases the effectiveness of YOLO architectures in meningioma firmness detection, with YOLOv7 emerging as the optimal model. The study’s findings underscore the significance of model selection and parameter optimization for achieving high sensitivity and robust overall performance in brain tumor classification.

show abstract

“…Reja et al [3] used deep learning to track construction progress by analyzing images and videos to enhance project management activities and decision-making. Shanti et al [4] demonstrated the use of deep learning for recognizing safety violations at construction sites. Park et al [5] applied deep learning to detect and quantify cracks on surfaces of concrete structures.…”

Section: Introductionmentioning

confidence: 99%

A Scaffolding Assembly Deficiency Detection System with Deep Learning and Augmented Reality

Dzeng,

Cheng,

Cheng

2024

Buildings

View full text Add to dashboard Cite

Scaffoldings play a critical role as temporary structures in supporting construction processes. Accidents at construction sites frequently stem from issues related to scaffoldings, including insufficient support caused by deviations from the construction design, insecure rod connections, or absence of cross-bracing, which result in uneven loading and potential collapse, leading to casualties. This research introduces a novel approach employing deep learning (i.e., YOLO v5) and augmented reality (AR), termed the scaffolding assembly deficiency detection system (SADDS), designed to aid field inspectors in discerning deficiencies within scaffolding assemblies. Inspectors have the flexibility to utilize SADDS through various devices, such as video cameras, mobile phones, or AR goggles, for the automated identification of deficiencies in scaffolding assemblies. The conducted test yielded satisfactory results, with a mean average precision of 0.89 and individual precision values of 0.96, 0.82, 0.90, and 0.89 for qualified frames and frames with the missing cross-tie rod, missing lower-tie rod, and missing footboard deficiencies, respectively. Subsequent field tests conducted at two construction sites demonstrated improved system performance compared to the training test. Furthermore, the advantages and disadvantages of employing mobile phones and AR goggles were discussed, elucidating certain limitations of the SADDS system, such as self-occlusion and efficiency issues.

show abstract

A Novel Implementation of an AI-Based Smart Construction Safety Inspection Protocol in the UAE

Cited by 21 publications

References 15 publications

Artificial Intelligence (AI)-Based Technology Adoption in the Construction Industry: A Cross National Perspective Using the Technology Acceptance Model

Artificial Intelligence (AI)-Based Technology Adoption in the Construction Industry: A Cross National Perspective Using the Technology Acceptance Model

A Deep Learning Approach for Brain Tumor Firmness Detection Based on Five Different YOLO Versions: YOLOv3–YOLOv7

A Scaffolding Assembly Deficiency Detection System with Deep Learning and Augmented Reality

Contact Info

Product

Resources

About