AIMC Topic: Construction Materials

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Machine learning-based prediction of the axial load capacity of UHPC strengthened reinforced concrete columns: A comparative analysis.

PloS one
This study develops and evaluates machine learning (ML) models to predict the axial load capacity (Pu) of reinforced concrete (RC) columns strengthened with ultra-high-performance concrete (UHPC) jackets. A comprehensive experimental database contain...

Automated cementing quality detection using a domain-specific, multi-scale convolutional neural network.

PloS one
Cementing quality is a key factor in ensuring the long-term safe production of oil and gas wells and preventing defects. Traditional cementing quality evaluation mainly relies on logging interpreters manually analyzing acoustic logging data, such as ...

Advancing sustainable concrete with bacterial self-healing technology and Kuhn-Tucker condition.

Scientific reports
This research investigates the self-healing potential of Bacillus subtilis in concrete due to its high capacity for calcium carbonate precipitation. Mathematical modelling and machine learning methods, i.e., Random Forest Method (RFM) and Kuhn-Tucker...

Analysis and prediction of the axial compression properties of desert sand concrete with steel tube restraint based on an improved BP neural network model.

PloS one
Accurate analysis and prediction of axial compression are important for ensuring the construction quality and safety of desert sand recycled aggregate concrete confined by steel tubes. In this study, the axial compressive strength and elastic modulus...

Hybrid machine learning approach for prediction and design optimization of marshall stability in graphene oxide-modified asphalt concrete.

Environmental research
Marshall Stability (MS) is a key, yet costly and time-consuming, metric for designing asphalt concrete (AC) in general, and Graphene Oxide (GO)-modified AC in particular. To address this, this study introduces a novel hybrid machine learning framewor...

A review of recent trends in sustainable biopolymer-integrated concrete and its impact on mechanical performance and structural reliability.

International journal of biological macromolecules
The integration of sustainable biopolymers into concrete has emerged as a promising approach to enhance mechanical properties, environmental performance, and long-term structural reliability. Conventional concrete, while globally prevalent, faces sig...

Estimation of compressive strength of ultra-high performance lightweight concrete (UHPLC) using neural network.

PloS one
High strength and lightweight are key trends in concrete development. Achieving a balance between these properties to produce high structural efficiency (strength-to-weight ratio) concrete is challenging due to the complex relationship between compre...

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

PloS one
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...

Consumption quota compilation based on BP artificial neural network algorithm in mechanical and electrical installation engineering of prefabricated buildings.

PloS one
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...

Zero-shot and few-shot multimodal plastic waste classification with vision-language models.

Waste management (New York, N.Y.)
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...