AIMC Topic: Construction Industry

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A narrative review on the application of Delphi and fuzzy Delphi techniques in the cement industry.

Work (Reading, Mass.)
BackgroundThe Delphi technique and its fuzzy variant are widely employed across various sectors, including industrial applications, for eliciting expert consensus and identifying priorities. Despite its prevalence, there is limited literature on its ...

Multi-label material and human risk factors recognition model for construction site safety management.

Journal of safety research
INTRODUCTION: Construction sites are prone to numerous safety risk factors, but safety managers have difficulty managing these risk factors for practical reasons. Moreover, manually identifying multiple risk factors visually is challenging. Therefore...

Identification and evaluation of deep foundation pit construction risks based on Grey-DEMATEL-Fuzzy comprehensive evaluation method.

PloS one
In recent years, foundation pit construction has been rapidly developing in the direction of deep and large-scale, leading to the frequent occurrence of construction accidents. The pit construction process is characterised by a complex environment, h...

Enhancing construction safety: predicting worker sleep deprivation using machine learning algorithms.

Scientific reports
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
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
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
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
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
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)
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...