The Role of Additives in Estimating Service Life of Self-Compacting Concrete Mix Design Using Fib Modeling DOI
Alireza Masoumi, Reza Farokhzad, Seyed Hooman Ghasemi

et al.

SSRN Electronic Journal, Journal Year: 2022, Volume and Issue: unknown

Published: Jan. 1, 2022

Concrete strength is considered to be a significant criterion in the construction and operation of reinforced concrete structures. Reinforced structures exposed corrosive environmental effects are prone short lifetime due corrosion rebar presence chloride Regarding resolve or mitigating abovementioned shortcomings, current study conducted investigate impact Xanthan Gum (polysaccharide)on durability properties associated with an increased half-life self-compacting concrete. Durability mechanical have been evaluated at different concentrations supplementary material (in 0.2 0.25% by weight), microsilica 5, 7, 10% nanosilica 2, 2 4% weight). In addition rheological concrete, other factors including permeability coefficient, depth ions infiltration, electrical conductivity, pressure resistance taken into account. To do so, service 43 mixes estimated using FIB modeling. this study, soft computing methodologies (specific Artificial Neural Network (ANN)) utilized facilitate computation burden resulting from complexity model number variables. observed results confirmed high accuracy low error ANN modeling terms compatibility, nonlinearity, proper generalizability, capability anticipate resistance, as well prediction coefficient infiltration. Also, sensitivity analysis was performed super decision software determine ranking priority additives. Our final approve that additives can enhance life. Moreover, experiment sensitive additives, change additive’s weight ratio may lead alteration rank.

Language: Английский

Artificial intelligence algorithms for prediction and sensitivity analysis of mechanical properties of recycled aggregate concrete: A review DOI Creative Commons
Tien-Dung Nguyen, Rachid Cherif, Pierre-Yves Mahieux

et al.

Journal of Building Engineering, Journal Year: 2023, Volume and Issue: 66, P. 105929 - 105929

Published: Jan. 20, 2023

Using recycled aggregates generated from demolition waste for concrete production is a promissory option to reduce the environmental footprint of built environment. However, predicting hardened performance aggregate one main barriers its intensive deployment in construction sector. Since traditional empirical approaches are less reliable new formulations, artificial intelligence have been widely developed recent years towards this aim. In paper, we conducted an extensive literature review on (AI) methods that predict mechanical concretes and perform sensitivity analysis. The primary methodologies algorithms found thoroughly described, examined, discussed study concerning their applicability, accuracy, computational requirements. Furthermore, benefits drawbacks various highlighted. AI demonstrated success variety prediction applications with high accuracy. Although these robust predictive tools estimating concrete's mixture composition properties, highly dependent data structure hyperparameter selection. This could help engineers researchers make better decisions about using properties and/or optimise formulations concrete.

Language: Английский

Citations

57
The Role of Lightweight AI Models in Supporting a Sustainable Transition to Renewable Energy: A Systematic Review DOI Creative Commons
Tymoteusz Miller, Irmina Durlik, Ewelina Kostecka

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(5), P. 1192 - 1192

Published: Feb. 28, 2025

The transition from fossil fuels to renewable energy (RE) sources is an essential step in mitigating climate change and ensuring environmental sustainability. However, large-scale deployment of renewables accompanied by new challenges, including the growing demand for rare-earth elements, need recycling end-of-life equipment, rising footprint digital tools—particularly artificial intelligence (AI) models. This systematic review, following Preferred Reporting Items Systematic Reviews Meta-Analyses (PRISMA) guidelines, explores how lightweight, distilled AI models can alleviate computational burdens while supporting critical applications systems. We examined empirical conceptual studies published between 2010 2024 that address energy, circular economy paradigm, model distillation low-energy techniques. Our findings indicate adopting significantly reduce consumption data processing, enhance grid optimization, support sustainable resource management across lifecycle infrastructures. review concludes highlighting opportunities challenges policymakers, researchers, industry stakeholders aiming integrate principles into RE strategies, emphasizing urgent collaborative solutions incentivized policies encourage low-footprint innovation.

Language: Английский

Citations

1
Concrete chloride diffusion modelling using marine creatures-based metaheuristic artificial intelligence DOI
Emadaldin Mohammadi Golafshani, Alireza Kashani, Taehwan Kim

et al.

Journal of Cleaner Production, Journal Year: 2022, Volume and Issue: 374, P. 134021 - 134021

Published: Sept. 7, 2022

Language: Английский

Citations

25
Synergetic impact of volcanic ash and calcium carbide residue on the properties and microstructure of cementitious composites DOI
Jad Bawab, Hilal El-Hassan, Amr El-Dieb

et al.

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 439, P. 137390 - 137390

Published: July 10, 2024

Language: Английский

Citations

4
Predictive performance assessment of recycled coarse aggregate concrete using artificial intelligence: A review DOI Creative Commons

P. Anitha Kumari,

Sagar Paruthi, Ahmad Alyaseen

et al.

Cleaner Materials, Journal Year: 2024, Volume and Issue: 13, P. 100263 - 100263

Published: July 30, 2024

Recycled coarse aggregate concrete enables the creation of environmentally friendly and cost-effective mixes. It helps address disposal problem demolition waste, meeting demand while improving product functionality reusability. The abundance obsolete buildings in cemeteries contributes to Construction Demolition waste. Concrete Aggregate (RCA) from demolished structures can be utilized as aggregates, albeit with concerns about its impact on compressive strength due absorption issues. This review aimed study develop different Artificial Intelligence (AI) model for prediction varying RCA content natural input parameters output parameter. range is 0 % 100 parameter 28 MPa 70.3 MPa. Experimental data literature articles used train validate development. Engineers researchers utilize these models predict by changing parameters. XGBoost Regression Model performed well R2 0.93594 followed Random Forest 0.92766, Gradient Boosting 0.90616 respectively. Ridge Regression, Lasso Linear Models were not predicting 0.57657, 0.57558, 0.57675 ANN also significant RCAC 0.8039. Future research could focus optimizing mechanical properties containing using AI models. Furthermore, extends analysis explore application various types concrete, highlighting versatility potential AI-driven approaches enhancing mix design.

Language: Английский

Citations

4
Prediction and validation of mechanical properties of self-compacting geopolymer concrete using combined machine learning methods a comparative and suitability assessment of the best analysis DOI Creative Commons
Kennedy C. Onyelowe, Ahmed M. Ebid, Paul O. Awoyera

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 21, 2025

Recent sustainable engineering trends show the re-use of wastes in production concrete materials. This was important two ways. First, there is a great environmental necessity to eliminate these industrial and their usage solid waste upcycling system ensure structural sustainability creates an avenue for this process. Second, it has become reduce laboratory equipment costs by establishing intelligent models through application supplementary cements optimized optimal performance For reasons, present research work applied learning abilities eight (8) ensemble-based one (1) symbolic regression machine methods predict strengths (compressive-Fc, flexural-Ff splitting tensile-Ft) SCGPC with "Orange Data Mining" software version 3.36. In paper, influence like ground granulated blast furnace slag (GGBS) fly ash (FA) alkali activators such as (NaOH Na2SiO3) on self-compacting geopolymer (SCGPC) terms strength been studied. executed using 132 mix entries at different curing regimes partitioned into 75% 25% training validation, respectively. At end process, indices were employed test accuracy comparatively best. Also, Taylor chart-based comparison conducted. The results that compressive (Fc) model, K-NN outclassed all ensemble techniques average R2 0.99, 0.96, error 0.04%. followed order superiority SVM closing its model 0.955 0.045%. Both ended equal SSE, MAE, MSE, RMSE. flexural (Ff) performed equally studied especially 0.99 other techniques. Finally, tensile (Ft) SCGPC, again 0.985 indices. RSM showed strong competition (standard = 1) 0.987, 0.973, 0.986 adequate precision 7) 129.7, 85.3, 123.5, Fc, Ff, Ft, addition, proposed closed-form equations which can be manually design performing materials most influential components, are GGBS, FA NaOH. NB came least performance. Overall, ML paper outperformed used previous literatures, except poorly NB.

Language: Английский

Citations

0
Artificial intelligence approaches in predicting the mechanical properties of natural fiber-reinforced concrete: A comprehensive review DOI
Mohammed Mohammed, Jawad K. Oleiwi,

Aeshah M. Mohammed

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 153, P. 110933 - 110933

Published: April 22, 2025

Language: Английский

Citations

0
Use of artificial intelligence to predict the performance of recycled aggregate concrete DOI
Tien-Dung Nguyen, Rachid Cherif, Pierre-Yves Mahieux

et al.

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 295 - 315

Published: Jan. 1, 2025

Language: Английский

Citations

0
Intelligent Prediction of Compressive Strength of Concrete Based on CNN-BiLSTM-MA DOI Creative Commons
Yuqiao Liu,

Hongling Yu,

Tao Guan

et al.

Case Studies in Construction Materials, Journal Year: 2025, Volume and Issue: unknown, P. e04486 - e04486

Published: March 1, 2025

Language: Английский

Citations

0
Prediction of the Compressive Strength of Waste-Based Concretes Using Artificial Neural Network DOI Open Access
Mouhamadou Amar, Mahfoud Benzerzour, R. Zentar

et al.

Materials, Journal Year: 2022, Volume and Issue: 15(20), P. 7045 - 7045

Published: Oct. 11, 2022

In the 21st century, numerous numerical calculation techniques have been discovered and used in several fields of science technology. The purpose this study was to use an artificial neural network (ANN) forecast compressive strength waste-based concretes. specimens studied include different kinds mineral additions: metakaolin, silica fume, fly ash, limestone filler, marble waste, recycled aggregates, ground granulated blast furnace slag. This method is based on experimental results available for 1303 mixtures gathered from 22 bibliographic sources ANN learning process. Based a multilayer feedforward model, data were arranged prepared train test model. model consists 18 inputs following type cement, water content, binder ratio, replacement quantity superplasticizer, etc. built applied with MATLAB software using module. According by proposed shows strong capacity predicting concrete particularly precise satisfactory accuracy (R² = 0.9888, MAPE 2.87%).

Language: Английский

Citations

13