AIMC Topic: Construction Materials

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A hybrid PSO-FFNN approach for optimized seismic design and accurate structural response prediction in steel moment-resisting frames.

PloS one Jun 2, 2025
The first steel is the most prevalent material used in building. Steel's intrinsic hardness and durability make it appropriate for different uses, but its greater adaptability makes it ideal for seismic design. The brittle fracture occurred in welded...
Algorithms Construction Materials Neural Networks, Computer Steel Earthquakes
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Consumption quota compilation based on BP artificial neural network algorithm in mechanical and electrical installation engineering of prefabricated buildings.

PloS one Jun 2, 2025
The traditional quota compilation method has a large workload and requires a lot of manpower and material resources, making it difficult to apply to the consumption quota compilation in mechanical and electrical installation engineering of prefabrica...
Algorithms Construction Materials Humans Neural Networks, Computer
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Zero-shot and few-shot multimodal plastic waste classification with vision-language models.

Waste management (New York, N.Y.) Apr 23, 2025
The construction sector is a large consumer of plastic, generating substantial volumes of plastic waste. Effective recycling of this waste requires accurate classification, as different plastic materials undergo distinct recycling processes to retain...
Recycling Construction Materials Waste Management Plastics
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Explainable artificial intelligence-based compressive strength optimization and Life-Cycle Assessment of eco-friendly sugarcane bagasse ash concrete.

Environmental science and pollution research international Mar 1, 2025
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...
Cellulose Construction Materials Artificial Intelligence Compressive Strength Saccharum
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Classification and predictive leaching risk assessment of construction and demolition waste using multivariate statistical and machine learning analyses.

Waste management (New York, N.Y.) Feb 19, 2025
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...
Construction Industry Industrial Waste Multivariate Analysis Waste Management Construction Materials Italy Machine Learning Water Pollutants, Chemical Risk Assessment
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Concrete crack detection using ridgelet neural network optimized by advanced human evolutionary optimization.

Scientific reports Feb 10, 2025
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...
Neural Networks, Computer Algorithms Humans Construction Materials
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Image recognition technology for bituminous concrete reservoir panel cracks based on deep learning.

PloS one Feb 4, 2025
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...
Construction Materials Hydrocarbons Deep Learning Image Processing, Computer-Assisted Algorithms
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Identification and information acquisition of high-value construction solid waste combined millimeter-wave radar and convolutional neural networks.

Waste management (New York, N.Y.) Jan 27, 2025
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...
Neural Networks, Computer Construction Materials Algorithms Recycling Solid Waste Convolutional Neural Networks Radar
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Developing a brain inspired multilobar neural networks architecture for rapidly and accurately estimating concrete compressive strength.

Scientific reports Jan 15, 2025
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...
Construction Materials Compressive Strength Brain Humans Neural Networks, Computer
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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 ...
Machine Learning Construction Materials Construction Industry Cost-Benefit Analysis Waste Management Industrial Waste Finland Recycling Automation
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