Optimisation of pervious concrete performance by varying aggregate shape, size, aggregate-to-cement ratio, and compaction effort by using the Taguchi method
SHB Wijekoon, N Sathiparan… - International Journal of …, 2024 - Taylor & Francis
The porosity and compressive strength of pervious concrete are critical determinants of its
suitability for various applications. Therefore, it is necessary to recognise the factors …
suitability for various applications. Therefore, it is necessary to recognise the factors …
A comparison between machine and deep learning models on high stationarity data
Advances in sensor, computing, and communication technologies are enabling big data
analytics by providing time series data. However, conventional models struggle to identify …
analytics by providing time series data. However, conventional models struggle to identify …
Machine learning meta-models for fast parameter identification of the lattice discrete particle model
When simulating the mechanical behavior of complex materials, the failure behavior is
strongly influenced by the internal structure. To account for such dependence, models at the …
strongly influenced by the internal structure. To account for such dependence, models at the …
Comparative analysis of gradient-boosting ensembles for estimation of compressive strength of quaternary blend concrete
Concrete compressive strength is usually determined 28 days after casting via crushing of
samples. However, the design strength may not be achieved after this time-consuming and …
samples. However, the design strength may not be achieved after this time-consuming and …
A machine learning approach to predicting pervious concrete properties: a review
This paper investigates the application of machine learning to predict the properties of
pervious concrete. Traditional methods like lab tests and formulas have limitations. Machine …
pervious concrete. Traditional methods like lab tests and formulas have limitations. Machine …
Coupled extreme gradient boosting algorithm with artificial intelligence models for predicting compressive strength of fiber reinforced polymer-confined concrete
Accurately predicting and identifying appropriate parameters are necessary for producing a
safe and reliable strength model of concrete elements confined with fiber-reinforced …
safe and reliable strength model of concrete elements confined with fiber-reinforced …
Strength and microstructural properties of phosphogypsum/ggbs-based geopolymer concrete
B Pratap - Iranian Journal of Science and Technology …, 2024 - Springer
Geopolymer concrete serves as an eco-friendly substitute for traditional Portland cement-
based concrete, notorious for its high carbon footprint due to substantial carbon dioxide …
based concrete, notorious for its high carbon footprint due to substantial carbon dioxide …
Predicting tensile strength of steel fiber-reinforced concrete based on a novel differential evolution-optimized extreme gradient boosting machine
ND Hoang - Neural Computing and Applications, 2024 - Springer
Splitting tensile strength (f spt) is a crucial parameter in designing concrete mixes. The
addition of steel fibers helps improve the mechanical properties of concrete, including its f …
addition of steel fibers helps improve the mechanical properties of concrete, including its f …
[HTML][HTML] Design of sustainable mortar incorporating construction and demolition waste through adaptive experiments accelerated by machine learning
Traditional approaches to designing sustainable construction materials are slow, resource-
intensive, and heavily reliant on trial and error. In this study, we present an integrated …
intensive, and heavily reliant on trial and error. In this study, we present an integrated …
Development and assessment of machine learning models for predicting fatigue response in AA2024
Accurate prediction of fatigue life is vital in the design of aerospace components subjected to
varying stress levels and loading frequencies. In the current research, machine learning …
varying stress levels and loading frequencies. In the current research, machine learning …