Machine learning for structural engineering: A state-of-the-art review

HT Thai - Structures, 2022 - Elsevier
Abstract Machine learning (ML) has become the most successful branch of artificial
intelligence (AI). It provides a unique opportunity to make structural engineering more …

Predictive models for concrete properties using machine learning and deep learning approaches: A review

MM Moein, A Saradar, K Rahmati… - Journal of Building …, 2023 - Elsevier
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 …

Machine learning techniques and multi-scale models to evaluate the impact of silicon dioxide (SiO2) and calcium oxide (CaO) in fly ash on the compressive strength of …

DKI Jaf, PI Abdulrahman, AS Mohammed… - … and Building Materials, 2023 - Elsevier
Fly ash is a by-product almost found in coal power plants; it is available worldwide.
According to the hazardous impacts of cement on the environment, fly ash is known to be a …

[HTML][HTML] A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations …

IU Ekanayake, DPP Meddage, U Rathnayake - Case Studies in …, 2022 - Elsevier
Abstract Machine learning (ML) techniques are often employed for the accurate prediction of
the compressive strength of concrete. Despite higher accuracy, previous ML models failed to …

[HTML][HTML] Compressive strength of geopolymer concrete modified with nano-silica: Experimental and modeling investigations

HU Ahmed, AS Mohammed, RH Faraj… - Case Studies in …, 2022 - Elsevier
Since nanotechnology can enhance the performance of materials, significant effort has been
expended in recent years to incorporate nanoparticles (NPs) into geopolymer concrete …

RETRACTED: Fresh and mechanical performances of recycled plastic aggregate geopolymer concrete modified with Nano-silica: Experimental and computational …

HU Ahmed, AS Mohammed, AA Mohammed - 2023 - Elsevier
Following receipt of a reader complaint, it was established that this paper [https://doi.
org/10.1016/j. conbuildmat. 2023.132266] and another submitted to the Journal of Building …

Machine learning prediction of mechanical properties of concrete: Critical review

WB Chaabene, M Flah, ML Nehdi - Construction and Building Materials, 2020 - Elsevier
Accurate prediction of the mechanical properties of concrete has been a concern since
these properties are often required by design codes. The emergence of new concrete …

Support vector regression (SVR) and grey wolf optimization (GWO) to predict the compressive strength of GGBFS-based geopolymer concrete

HU Ahmed, RR Mostafa, A Mohammed, P Sihag… - Neural Computing and …, 2023 - Springer
Geopolymer concrete is an eco-efficient and environmentally friendly construction material.
Various ashes were used as the binder in geopolymer concrete, such as fly ash, ground …

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 …

Compressive strength of geopolymer concrete composites: a systematic comprehensive review, analysis and modeling

HU Ahmed, AS Mohammed, SMA Qaidi… - European Journal of …, 2023 - Taylor & Francis
The desire to make the concrete industry more environmentally friendly has existed for a
long time. Geopolymer concrete, which uses industrial or agricultural by-product ashes as …