Predictive models for concrete properties using machine learning and deep learning approaches: A review
Concrete is one of the most widely used materials in various civil engineering applications.
Its global production rate is increasing to meet demand. Mechanical properties of concrete …
Its global production rate is increasing to meet demand. Mechanical properties of concrete …
[HTML][HTML] A review on mixture design methods for geopolymer concrete
Geopolymer concretes (GPCs) can be produced by chemical activation of industrial by-
products and processed natural minerals that contain aluminosilicate. There have been a …
products and processed natural minerals that contain aluminosilicate. There have been a …
Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach
DC Feng, ZT Liu, XD Wang, Y Chen, JQ Chang… - … and Building Materials, 2020 - Elsevier
In this paper, an intelligent approach based on the machine learning technique is proposed
for predicting the compressive strength of concrete. This approach employs the adaptive …
for predicting the compressive strength of concrete. This approach employs the adaptive …
Prediction of concrete materials compressive strength using surrogate models
Using soft computing methods could be of great interest in predicting the compressive
strength of Ultra-High-Performance Fibre Reinforced Concrete (UHPFRC). Therefore, this …
strength of Ultra-High-Performance Fibre Reinforced Concrete (UHPFRC). Therefore, this …
Soft computing models to predict the compressive strength of GGBS/FA-geopolymer concrete
A variety of ashes used as the binder in geopolymer concrete such as fly ash (FA), ground
granulated blast furnace slag (GGBS), rice husk ash (RHA), metakaolin (MK), palm oil fuel …
granulated blast furnace slag (GGBS), rice husk ash (RHA), metakaolin (MK), palm oil fuel …
Prediction of concrete strengths enabled by missing data imputation and interpretable machine learning
Abstract Machine learning (ML)-based prediction of non-linear composition-strength
relationship in concretes requires a large, complete, and consistent dataset. However, the …
relationship in concretes requires a large, complete, and consistent dataset. However, the …
Effitioned soft computing models to evaluate the impact of silicon dioxide (SiO2) to calcium oxide (CaO) ratio in fly ash on the compressive strength of concrete
DKI Jaf, AS Abdulrahman, PI Abdulrahman… - Journal of Building …, 2023 - Elsevier
Environmental issues are raised from global warming due to raised Carbon Dioxide (CO 2)
emissions of factories worldwide. Cement production provides about 8–10% of the total CO …
emissions of factories worldwide. Cement production provides about 8–10% of the total CO …
Machine learning and interactive GUI for concrete compressive strength prediction
Concrete compressive strength (CS) is a crucial performance parameter in concrete
structure design. Reliable strength prediction reduces costs and time in design and prevents …
structure design. Reliable strength prediction reduces costs and time in design and prevents …
Optimum mix design of geopolymer pastes and concretes cured in ambient condition based on compressive strength, setting time and workability
MNS Hadi, H Zhang, S Parkinson - Journal of Building engineering, 2019 - Elsevier
In this study, the effects of ground granulated blast furnace slag (GGBFS) content, alkaline
solution to binder (Al/Bi) mass ratio, sodium silicate solution to sodium hydroxide solution …
solution to binder (Al/Bi) mass ratio, sodium silicate solution to sodium hydroxide solution …
A comparative study on the compressive strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis …
Abstract In this study, Multiple Regression Analysis (MRA) and Artificial Neural Network
(ANN) models are constructed to predict the compressive strength of High Performance …
(ANN) models are constructed to predict the compressive strength of High Performance …