Novel machine learning applications on fly ash based concrete: an overview

G Khambra, P Shukla - Materials Today: Proceedings, 2023 - Elsevier
A machine learning technique provides rapid access to various information models,
approaches, complex systems, and algorithms. In the present scenario the artificial neural …

Nano-silica-modified concrete: A bibliographic analysis and comprehensive review of material properties

K Khan, W Ahmad, MN Amin, S Nazar - Nanomaterials, 2022 - mdpi.com
Several review studies have been performed on nano-silica-modified concrete, but this
study adopted a new method based on scientometric analysis for the keywords' assessment …

Predictive modeling for sustainable high-performance concrete from industrial wastes: A comparison and optimization of models using ensemble learners

F Farooq, W Ahmed, A Akbar, F Aslam… - Journal of Cleaner …, 2021 - Elsevier
The cementitious matrix of high-performance concrete (HPC) is highly complex, and
ambiguity exists with its mix design. Compressive strength can vary with the composition …

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 …

Artificial neural network model to predict the compressive strength of eco-friendly geopolymer concrete incorporating silica fume and natural zeolite

AA Shahmansouri, M Yazdani, S Ghanbari… - Journal of Cleaner …, 2021 - Elsevier
The growing concern about global climate change and its adverse impacts on societies is
putting severe pressure on the construction industry as one of the largest producers of …

Predicting compressive strength of manufactured-sand concrete using conventional and metaheuristic-tuned artificial neural network

Y Zhao, H Hu, C Song, Z Wang - Measurement, 2022 - Elsevier
Compressive strength (CS) is the maximum resistance of concrete against axial
compressive loading in standard conditions. Estimation of this parameter is essential for the …

Fractionation of dyes/salts using loose nanofiltration membranes: Insight from machine learning prediction

N Baig, J Usman, SI Abba, M Benaafi… - Journal of Cleaner …, 2023 - Elsevier
Wastewater (WW) served as the crucial indicator for sustainable development, human
health, and the ecosystem. Nanofiltration (NF) membranes are efficient in contaminants, dye …

Estimation of rapid chloride permeability of SCC using hyperparameters optimized random forest models

DM Ge, LC Zhao, M Esmaeili-Falak - Journal of Sustainable …, 2023 - Taylor & Francis
Discovering concrete properties takes time, money, laboratory design, material preparation,
and testing with adequate equipment at the right ages. As a consequence, in the concrete …

Metaheuristic optimization of Levenberg–Marquardt-based artificial neural network using particle swarm optimization for prediction of foamed concrete compressive …

HB Ly, MH Nguyen, BT Pham - Neural Computing and Applications, 2021 - Springer
Foamed concrete (FC) shows advantageous applications in civil engineering, such as
reduction in dead loads, contribution to energy conservation, or decrease the construction …

Micro-characterisation of alkali activated paste with fly ash-GGBS-metakaolin binder system with ambient setting characteristics

M Kamath, S Prashant, M Kumar - Construction and Building Materials, 2021 - Elsevier
The study reports properties of a stable and high strength ternary binder matrix using Fly Ash
(FA), Ground Granulated Blast Furnace Slag (GGBS), and Metakaolin (MK). The bulk of the …