AIMC Topic: Construction Industry
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Enhancing construction safety: predicting worker sleep deprivation using machine learning algorithms.
Scientific reports
Jul 8, 2024
Sleep deprivation is a critical issue that affects workers in numerous industries, including construction. It adversely affects workers and can lead to significant concerns regarding their health, safety, and overall job performance. Several studies ...
Daily planning conversations and AI: Keys for improving construction culture, engagement, planning, and safety.
American journal of industrial medicine
May 29, 2024
The construction industry is known for its inherent risks, contributing to ~170,000 workplace injuries and illnesses annually in the United States. Engaging in prejob safety discussions presents a crucial chance to safeguard workers by proactively re...
A new multi-criteria decision making method for the selection of construction contractors using interval valued fuzzy set.
BMC research notes
Apr 22, 2024
OBJECTIVE: This article introduces a novel approach called Digital Weighted Multi Criteria Decision Making (DWMCDM) that employs interval valued fuzzy sets to select the best contractor for building projects. The contractor is chosen based on the pre...
Research on helmet wearing detection method based on deep learning.
Scientific reports
Mar 25, 2024
The vigorous development of the construction industry has also brought unprecedented safety risks. The wearing of safety helmets at the construction site can effectively reduce casualties. As a result, this paper suggests employing a deep learning-ba...
Enhancing safety of construction workers in Korea: an integrated text mining and machine learning framework for predicting accident types.
International journal of injury control and safety promotion
Jan 2, 2024
Construction workers face a high risk of various occupational accidents, many of which can result in fatalities. This study aims to develop a prediction model for nine prevalent types of construction accidents, utilizing construction tasks, activitie...
Deep learning-based models for environmental management: Recognizing construction, renovation, and demolition waste in-the-wild.
Journal of environmental management
Jan 1, 2024
The construction industry generates a substantial volume of solid waste, often destinated for landfills, causing significant environmental pollution. Waste recycling is decisive in managing waste yet challenging due to labor-intensive sorting process...
A Bibliometric Analysis of Neuroscience Tools Use in Construction Health and Safety Management.
Sensors (Basel, Switzerland)
Nov 30, 2023
Despite longstanding traditional construction health and safety management (CHSM) methods, the construction industry continues to face persistent challenges in this field. Neuroscience tools offer potential advantages in addressing these safety and h...
Real-time monitoring unsafe behaviors of portable multi-position ladder worker using deep learning based on vision data.
Journal of safety research
Sep 14, 2023
INTRODUCTION: Fatal fall from height accidents, especially on construction sites, persist, underscoring the importance of monitoring and managing worker behaviors to enhance safety. Deep learning showed the possibility of substituting the manual work...
Unmanned Aerial Systems and Deep Learning for Safety and Health Activity Monitoring on Construction Sites.
Sensors (Basel, Switzerland)
Jul 26, 2023
Construction is a highly hazardous industry typified by several complex features in dynamic work environments that have the possibility of causing harm or ill health to construction workers. The constant monitoring of workers' unsafe behaviors and wo...
Real-time construction demolition waste detection using state-of-the-art deep learning methods; single-stage vs two-stage detectors.
Waste management (New York, N.Y.)
Jun 1, 2023
Central to the development of a successful waste sorting robot lies an accurate and fast object detection system. This study assesses the performance of the most representative deep-learning models for the real-time localisation and classification of...
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