AIMC Topic: Construction Materials

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

Waste management (New York, N.Y.) Jan 8, 2025
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 ...
Construction Industry Cost-Benefit Analysis Recycling Machine Learning Construction Materials Waste Management Finland Automation Industrial Waste
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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.) Dec 31, 2024
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...
Recycling Glass Carbon Construction Materials Neural Networks, Computer Machine Learning Algorithms
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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 Dec 16, 2024
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...
Construction Materials Cities Carbon Male Female Humans Middle Aged Public Health Adult Artificial Intelligence Surveys and Questionnaires
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Effective carbon footprint assessment strategy in fly ash geopolymer concrete based on adaptive boosting learning techniques.

Environmental research Dec 6, 2024
In light of the growing need to mitigate climate change impacts, this study presents an innovative methodology combining ensemble machine learning with experimental data to accurately predict the carbon dioxide footprint (CO-FP) of fly ash geopolymer...
Construction Materials Air Pollutants Machine Learning Environmental Monitoring Coal Ash Carbon Dioxide Carbon Footprint
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Precision assessment of the machine learning tools for the strength optimization of environmental-friendly lightweight foam concrete.

Journal of environmental management Nov 29, 2024
Foamed concrete (FC) is increasingly used in modern construction due to its lightweight nature, superior thermal insulation, and sustainable properties. However, accurately predicting its compressive strength remains a challenge due to the complex in...
Machine Learning Compressive Strength Construction Materials
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The research explores the predictive capacity of the shear strength of reinforced concrete walls with different cross-sectional shapes using the XGBoost model.

PloS one Nov 27, 2024
Structurally, the lateral load-bearing capacity mainly depends on reinforced concrete (RC) walls. Determination of flexural strength and shear strength is mandatory when designing reinforced concrete walls. Typically, these strengths are determined t...
Machine Learning Construction Materials Materials Testing Bayes Theorem Shear Strength Algorithms Models, Theoretical
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Modelling and evaluation of mechanical performance and environmental impacts of sustainable concretes using a multi-objective optimization based innovative interpretable artificial intelligence method.

Journal of environmental management Nov 16, 2024
This study focuses on modelling sustainable concretes' mechanical and environmental properties with interpretable artificial intelligence-based automated rule extraction, management of waste materials, and meeting future prospects. In this context, 2...
Compressive Strength Recycling Environment Models, Theoretical Construction Materials Artificial Intelligence
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A narrative review on the application of Delphi and fuzzy Delphi techniques in the cement industry.

Work (Reading, Mass.) Nov 13, 2024
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 ...
Humans Construction Industry Fuzzy Logic Delphi Technique Construction Materials Consensus
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Improved prediction accuracy for compressive strength of recycled aggregate concrete using optimization-based algorithms and cascade forward neural network.

Journal of environmental management Oct 30, 2024
This study proposed a data driven approach to predict the compressive strength (CS) of recycled aggregate concrete (RAC) for sustainable construction using an elite single genetic optimization algorithm-based cascade forward neural network (ESGA-CFNN...
Algorithms Construction Materials Recycling Compressive Strength Neural Networks, Computer
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Optimizing waste handling with interactive AI: Prompt-guided segmentation of construction and demolition waste using computer vision.

Waste management (New York, N.Y.) Sep 24, 2024
Optimized and automated methods for handling construction and demolition waste (CDW) are crucial for improving the resource recovery process in waste management. Automated waste recognition is a critical step in this process, and it relies on robust ...
Image Processing, Computer-Assisted Construction Materials Artificial Intelligence Waste Management Algorithms
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