186 related articles for article (PubMed ID: 36363023)
1. Prediction of Ecofriendly Concrete Compressive Strength Using Gradient Boosting Regression Tree Combined with GridSearchCV Hyperparameter-Optimization Techniques.
Alhakeem ZM; Jebur YM; Henedy SN; Imran H; Bernardo LFA; Hussein HM
Materials (Basel); 2022 Oct; 15(21):. PubMed ID: 36363023
[TBL] [Abstract][Full Text] [Related]
2. Development of Prediction Model to Predict the Compressive Strength of Eco-Friendly Concrete Using Multivariate Polynomial Regression Combined with Stepwise Method.
Imran H; Al-Abdaly NM; Shamsa MH; Shatnawi A; Ibrahim M; Ostrowski KA
Materials (Basel); 2022 Jan; 15(1):. PubMed ID: 35009463
[TBL] [Abstract][Full Text] [Related]
3. Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms.
Rathakrishnan V; Bt Beddu S; Ahmed AN
Sci Rep; 2022 Jun; 12(1):9539. PubMed ID: 35680937
[TBL] [Abstract][Full Text] [Related]
4. Modeling the compressive strength of eco-friendly self-compacting concrete incorporating ground granulated blast furnace slag using soft computing techniques.
Faraj RH; Mohammed AA; Omer KM
Environ Sci Pollut Res Int; 2022 Oct; 29(47):71338-71357. PubMed ID: 35596861
[TBL] [Abstract][Full Text] [Related]
5. Comparison of Prediction Models Based on Machine Learning for the Compressive Strength Estimation of Recycled Aggregate Concrete.
Khan K; Ahmad W; Amin MN; Aslam F; Ahmad A; Al-Faiad MA
Materials (Basel); 2022 May; 15(10):. PubMed ID: 35629456
[TBL] [Abstract][Full Text] [Related]
6. Systematic multiscale models to predict the compressive strength of fly ash-based geopolymer concrete at various mixture proportions and curing regimes.
Ahmed HU; Mohammed AS; Mohammed AA; Faraj RH
PLoS One; 2021; 16(6):e0253006. PubMed ID: 34125869
[TBL] [Abstract][Full Text] [Related]
7. Compressive Strength Estimation of Fly Ash/Slag Based Green Concrete by Deploying Artificial Intelligence Models.
Khan K; Salami BA; Iqbal M; Amin MN; Ahmed F; Jalal FE
Materials (Basel); 2022 May; 15(10):. PubMed ID: 35629748
[TBL] [Abstract][Full Text] [Related]
8. Machine Learning Prediction Models to Evaluate the Strength of Recycled Aggregate Concrete.
Yuan X; Tian Y; Ahmad W; Ahmad A; Usanova KI; Mohamed AM; Khallaf R
Materials (Basel); 2022 Apr; 15(8):. PubMed ID: 35454516
[TBL] [Abstract][Full Text] [Related]
9. Forecasting the strength of preplaced aggregate concrete using interpretable machine learning approaches.
Javed MF; Fawad M; Lodhi R; Najeh T; Gamil Y
Sci Rep; 2024 Apr; 14(1):8381. PubMed ID: 38600161
[TBL] [Abstract][Full Text] [Related]
10. Evaluation of Strength Development in Concrete with Ground Granulated Blast Furnace Slag Using Apparent Activation Energy.
Yang HM; Kwon SJ; Myung NV; Singh JK; Lee HS; Mandal S
Materials (Basel); 2020 Jan; 13(2):. PubMed ID: 31963399
[TBL] [Abstract][Full Text] [Related]
11. Developing a boosted decision tree regression prediction model as a sustainable tool for compressive strength of environmentally friendly concrete.
Latif SD
Environ Sci Pollut Res Int; 2021 Dec; 28(46):65935-65944. PubMed ID: 34327638
[TBL] [Abstract][Full Text] [Related]
12. Application of Ensemble Machine Learning Methods to Estimate the Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete.
Anjum M; Khan K; Ahmad W; Ahmad A; Amin MN; Nafees A
Polymers (Basel); 2022 Sep; 14(18):. PubMed ID: 36146051
[TBL] [Abstract][Full Text] [Related]
13. Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment.
Latif SD
Environ Sci Pollut Res Int; 2021 Jun; 28(23):30294-30302. PubMed ID: 33590396
[TBL] [Abstract][Full Text] [Related]
14. Hybrid nonlinear regression model versus MARS, MEP, and ANN to evaluate the effect of the size and content of waste tire rubber on the compressive strength of concrete.
Ismael Jaf DK; Abdalla A; Mohammed AS; Abdulrahman PI; Rawaz Kurda ; Mohammed AA
Heliyon; 2024 Feb; 10(4):e25997. PubMed ID: 38384542
[TBL] [Abstract][Full Text] [Related]
15. Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions.
Amin MN; Ahmad W; Khan K; Ahmad A; Nazar S; Alabdullah AA
Materials (Basel); 2022 Jul; 15(15):. PubMed ID: 35955144
[TBL] [Abstract][Full Text] [Related]
16. Evaluation and estimation of compressive strength of concrete masonry prism using gradient boosting algorithm.
Ho LS; Tran VQ
PLoS One; 2024; 19(3):e0297364. PubMed ID: 38442109
[TBL] [Abstract][Full Text] [Related]
17. Developing interpretable machine learning-Shapley additive explanations model for unconfined compressive strength of cohesive soils stabilized with geopolymer.
Ngo AQ; Nguyen LQ; Tran VQ
PLoS One; 2023; 18(6):e0286950. PubMed ID: 37289821
[TBL] [Abstract][Full Text] [Related]
18. Soft computing models to predict the compressive strength of GGBS/FA- geopolymer concrete.
Ahmed HU; Mohammed AA; Mohammed A
PLoS One; 2022; 17(5):e0265846. PubMed ID: 35613110
[TBL] [Abstract][Full Text] [Related]
19. Mixture Optimization of Recycled Aggregate Concrete Using Hybrid Machine Learning Model.
Nunez I; Marani A; Nehdi ML
Materials (Basel); 2020 Sep; 13(19):. PubMed ID: 33003383
[TBL] [Abstract][Full Text] [Related]
20. Mechanical Framework for Geopolymer Gels Construction: An Optimized LSTM Technique to Predict Compressive Strength of Fly Ash-Based Geopolymer Gels Concrete.
Shi X; Chen S; Wang Q; Lu Y; Ren S; Huang J
Gels; 2024 Feb; 10(2):. PubMed ID: 38391478
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
