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Public health perspectives on green efficiency through smart cities, artificial intelligence for healthcare and low carbon building materials.

Frontiers in public health
INTRODUCTION: Smart cities, artificial intelligence (AI) in healthcare, and low-carbon building materials are pivotal to public health, environmental sustainability, and green efficiency. Despite their critical importance, understanding public percep...

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 ...

An explainable machine learning system for efficient use of waste glasses in durable concrete to maximise carbon credits towards net zero emissions.

Waste management (New York, N.Y.)
Recycling waste glass (WG) can be time-consuming, costly, and impractical. However, its incorporation into concrete significantly reduces environmental impact and carbon emissions. This paper introduces machine learning (ML) to civil engineering to o...

Image recognition technology for bituminous concrete reservoir panel cracks based on deep learning.

PloS one
Detecting cracks in asphalt concrete slabs is challenging due to environmental factors like lighting changes, surface reflections, and weather conditions, which affect image quality and crack detection accuracy. This study introduces a novel deep lea...

Identification and information acquisition of high-value construction solid waste combined millimeter-wave radar and convolutional neural networks.

Waste management (New York, N.Y.)
The accumulation of construction solid waste (CSW) leads to the waste of land resources and environmental pollution, becoming a significant social problem. Identifying the amount of high-value CSW is essential for assessing the value of accumulated C...

Developing a brain inspired multilobar neural networks architecture for rapidly and accurately estimating concrete compressive strength.

Scientific reports
Concrete compressive strength is a critical parameter in construction and structural engineering. Destructive experimental methods that offer a reliable approach to obtaining this property involve time-consuming procedures. Recent advancements in art...

Machine learning-based automated waste sorting in the construction industry: A comparative competitiveness case study.

Waste management (New York, N.Y.)
This article presents a comparative analysis of the circularity and cost-efficiency of two distinct construction material recycling processes: ML-based automated sorting (MLAS) and conventional sorting technologies. Empirical data was collected from ...

Classification and predictive leaching risk assessment of construction and demolition waste using multivariate statistical and machine learning analyses.

Waste management (New York, N.Y.)
Managing construction and demolition waste (CDW) poses serious concerns regarding landfilling and recycling because of the potential release of hazardous elements after leaching. Ceramic materials such as bricks, tiles, and porcelain account for more...

Concrete crack detection using ridgelet neural network optimized by advanced human evolutionary optimization.

Scientific reports
Concrete frameworks require strong structural integrity to ensure their durability and performance. However, they are disposed to develop cracks, which can compromise their overall quality. This research presents an innovative crack diagnosis algorit...

Explainable artificial intelligence-based compressive strength optimization and Life-Cycle Assessment of eco-friendly sugarcane bagasse ash concrete.

Environmental science and pollution research international
Investigations on the potential use of sustainable sugarcane bagasse ash (SCBA) as a supplementary cementitious material (SCM) in concrete production have been carried out. The paper employs model agnostic eXplainable Artificial Intelligence (XAI) to...