AIMC Topic: Compressive Strength

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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...

Precision assessment of the machine learning tools for the strength optimization of environmental-friendly lightweight foam concrete.

Journal of environmental management
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...

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
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...

Improved prediction accuracy for compressive strength of recycled aggregate concrete using optimization-based algorithms and cascade forward neural network.

Journal of environmental management
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...

Machine learning reveals correlations between brain age and mechanics.

Acta biomaterialia
Our brain undergoes significant micro- and macroscopic changes throughout its life cycle. It is therefore crucial to understand the effect of aging on the mechanical properties of the brain in order to develop accurate personalized simulations and di...

Deep learning-based prediction of compressive strength of eco-friendly geopolymer concrete.

Environmental science and pollution research international
The greenhouse gases cause global warming on Earth. The cement production industry is one of the largest sectors producing greenhouse gases. The geopolymer is produced with synthesized by the reaction of an alkaline solution and the waste materials s...

Artificial neural network, machine learning modelling of compressive strength of recycled coarse aggregate based self-compacting concrete.

PloS one
This research study aims to understand the application of Artificial Neural Networks (ANNs) to forecast the Self-Compacting Recycled Coarse Aggregate Concrete (SCRCAC) compressive strength. From different literature, 602 available data sets from SCRC...

Evaluation and estimation of compressive strength of concrete masonry prism using gradient boosting algorithm.

PloS one
The compressive strength (CS) of the hollow concrete masonry prism is known as an important parameter for designing masonry structures. In general, the CS is determined using laboratory tests, however, laboratory tests are time-consuming and high-cos...

Advanced machine learning algorithms to evaluate the effects of the raw ingredients on flowability and compressive strength of ultra-high-performance concrete.

PloS one
The estimation of concrete characteristics through artificial intelligence techniques is come out to be an effective way in the construction sector in terms of time and cost conservation. The manufacturing of Ultra-High-Performance Concrete (UHPC) is...

Intelligent prediction of rockburst in tunnels based on back propagation neural network integrated beetle antennae search algorithm.

Environmental science and pollution research international
Rockburst is one of the major engineering geological disasters of underground engineering. Accurate rockburst intensity level prediction is vital for disaster control during underground tunnel construction. In this work, a hybrid model integrating th...