[HTML][HTML] Synthesis, properties, applications, 3D printing and machine learning of graphene quantum dots in polymer nanocomposites

V Dananjaya, S Marimuthu, R Yang, AN Grace… - Progress in Materials …, 2024 - Elsevier
This comprehensive review discusses the recent progress in synthesis, properties,
applications, 3D printing and machine learning of graphene quantum dots (GQDs) in …

[HTML][HTML] A research agenda for augmented and virtual reality in architecture, engineering and construction

JMD Delgado, L Oyedele, P Demian… - Advanced Engineering …, 2020 - Elsevier
This paper presents a study on the usage landscape of augmented reality (AR) and virtual
reality (VR) in the architecture, engineering and construction sectors, and proposes a …

Advances in computational intelligence of polymer composite materials: machine learning assisted modeling, analysis and design

A Sharma, T Mukhopadhyay, SM Rangappa… - … Methods in Engineering, 2022 - Springer
The superior multi-functional properties of polymer composites have made them an ideal
choice for aerospace, automobile, marine, civil, and many other technologically demanding …

Determining the critical risk factors for predicting the severity of ship collision accidents using a data-driven approach

H Lan, X Ma, W Qiao, W Deng - Reliability Engineering & System Safety, 2023 - Elsevier
Ship collision accidents often result in serious casualties and property losses. Predicting the
severity of ship collisions is beneficial to improve maritime transport safety. Therefore, this …

Grey wolf optimizer-based machine learning algorithm to predict electric vehicle charging duration time

I Ullah, K Liu, T Yamamoto, M Shafiullah… - Transportation …, 2023 - Taylor & Francis
Precise charging time prediction can effectively mitigate the inconvenience to drivers
induced by inevitable charging behavior throughout trips. Although the effectiveness of the …

[HTML][HTML] Enhancing construction safety: Machine learning-based classification of injury types

M Alkaissy, M Arashpour, EM Golafshani, MR Hosseini… - Safety science, 2023 - Elsevier
The construction industry is a hazardous industry with significant injuries and fatalities. Few
studies have used data-driven analysis to investigate injuries due to construction accidents …

Application of machine learning techniques for predicting the consequences of construction accidents in China

R Zhu, X Hu, J Hou, X Li - Process Safety and Environmental Protection, 2021 - Elsevier
Construction accidents can easily cause massive casualties and property losses. This
research uses machine learning technique to analyze 16 critical factors and assess the …

Integrating feature engineering, genetic algorithm and tree-based machine learning methods to predict the post-accident disability status of construction workers

K Koc, Ö Ekmekcioğlu, AP Gurgun - Automation in Construction, 2021 - Elsevier
The construction industry is among the riskiest industries around the world. Hence, the
preliminary studies exploring the consequences of occupational accidents have received …

RFCNN: Traffic accident severity prediction based on decision level fusion of machine and deep learning model

M Manzoor, M Umer, S Sadiq, A Ishaq, S Ullah… - IEEE …, 2021 - ieeexplore.ieee.org
Traffic accidents on highways are a leading cause of death despite the development of traffic
safety measures. The burden of casualties and damage caused by road accidents is very …

Scenario-based automated data preprocessing to predict severity of construction accidents

K Koc, AP Gurgun - Automation in Construction, 2022 - Elsevier
Occupational accidents are common in the construction industry, therefore develo**
prediction models to detect high severe accidents would be useful. However, existing …