A systematic review of machine learning techniques and applications in soil improvement using green materials

AH Saad, H Nahazanan, B Yusuf, SF Toha, A Alnuaim… - Sustainability, 2023 - mdpi.com
According to an extensive evaluation of published studies, there is a shortage of research on
systematic literature reviews related to machine learning prediction techniques and …

[HTML][HTML] Formulation of estimation models for the compressive strength of concrete mixed with nanosilica and carbon nanotubes

S Nazar, J Yang, MN Amin, K Khan, MF Javed… - Developments in the …, 2023 - Elsevier
New concepts for improving the performance of cementitious materials have recently
surfaced due to the advancement in nanotechnology. In this context, nano silica (NS) and …

Influence of sodium silicate to precursor ratio on mechanical properties and durability of the metakaolin/fly ash alkali-activated sustainable mortar using manufactured …

P Zhang, C Wang, F Wang, P Yuan - Reviews on Advanced Materials …, 2023 - degruyter.com
In recent years, manufactured sand produced from crushed rock has been used as fine
aggregate instead of natural sand in construction and industrial fields to minimize the impact …

[HTML][HTML] Machine learning based computational approach for crack width detection of self-healing concrete

F Althoey, MN Amin, K Khan, MM Usman… - Case Studies in …, 2022 - Elsevier
Concrete structures frequently experience the phenomena of crack development. The
researchers used certain healing agents to boost the frequently observed autogenous crack …

[HTML][HTML] Microstructure and chemical characterizations with soft computing models to evaluate the influence of calcium oxide and silicon dioxide in the fly ash and …

A Abdalla, A Salih - Resources, Conservation & Recycling Advances, 2022 - Elsevier
Environmental issues are raised from the global warming due to raised Carbon Dioxide (CO
2) emissions of factories worldwide. Cement manufacturing is highly energy-and emissions …

Hybrid MARS-, MEP-, and ANN-based prediction for modeling the compressive strength of cement mortar with various sand size and clay mineral metakaolin content

A Abdalla, AS Mohammed - Archives of Civil and Mechanical Engineering, 2022 - Springer
In this study, several mathematical, soft computing, and machine learning modeling tools are
used to develop a dependable model for forecasting the compressive strength of cement …

Mixed artificial intelligence models for compressive strength prediction and analysis of fly ash concrete

W Liang, W Yin, Y Zhong, Q Tao, K Li, Z Zhu… - … in Engineering Software, 2023 - Elsevier
The construction industry is facing challenges from the hazardous nature of Ordinary
Portland Cement (OPC) production as one of the main contributors to global warming and …

Machine learning-based predictive model for tensile and flexural strength of 3D-printed concrete

A Ali, RD Riaz, UJ Malik, SB Abbas, M Usman… - Materials, 2023 - mdpi.com
The additive manufacturing of concrete, also known as 3D-printed concrete, is produced
layer by layer using a 3D printer. The three-dimensional printing of concrete offers several …

Prospects of sustainable geotechnical applications of manufactured sand slurry as controlled low strength material

V Devaraj, V Mangottiri, S Balu - Construction and Building Materials, 2023 - Elsevier
Rapid changes in the construction industry for infrastructure development require extensive
use of building materials from a variety of sources. As natural resources deplete at a faster …

Application of deep neural network in the strength prediction of cemented paste backfill based on a global dataset

C Qi, J Zheng, X Yang, Q Chen, M Wu - Construction and Building Materials, 2023 - Elsevier
With the development of the mining industry, the increasing global content of tailings is
causing environmental pollution and the use of tailings as a cemented paste backfill (CPB) …