329 related articles for article (PubMed ID: 34641204)
1. Prediction of Geopolymer Concrete Compressive Strength Using Novel Machine Learning Algorithms.
Ahmad A; Ahmad W; Chaiyasarn K; Ostrowski KA; Aslam F; Zajdel P; Joyklad P
Polymers (Basel); 2021 Oct; 13(19):. PubMed ID: 34641204
[TBL] [Abstract][Full Text] [Related]
2. Comparative Study of Supervised Machine Learning Algorithms for Predicting the Compressive Strength of Concrete at High Temperature.
Ahmad A; Ostrowski KA; Maślak M; Farooq F; Mehmood I; Nafees A
Materials (Basel); 2021 Jul; 14(15):. PubMed ID: 34361416
[TBL] [Abstract][Full Text] [Related]
3. Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete.
Cao R; Fang Z; Jin M; Shang Y
Materials (Basel); 2022 Mar; 15(7):. PubMed ID: 35407733
[TBL] [Abstract][Full Text] [Related]
4. Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm.
Ahmad A; Farooq F; Niewiadomski P; Ostrowski K; Akbar A; Aslam F; Alyousef R
Materials (Basel); 2021 Feb; 14(4):. PubMed ID: 33567526
[TBL] [Abstract][Full Text] [Related]
5. Prediction of Mechanical Properties of Fly-Ash/Slag-Based Geopolymer Concrete Using Ensemble and Non-Ensemble Machine-Learning Techniques.
Amin MN; Khan K; Javed MF; Aslam F; Qadir MG; Faraz MI
Materials (Basel); 2022 May; 15(10):. PubMed ID: 35629515
[TBL] [Abstract][Full Text] [Related]
6. Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete.
Dao DV; Ly HB; Trinh SH; Le TT; Pham BT
Materials (Basel); 2019 Mar; 12(6):. PubMed ID: 30934566
[TBL] [Abstract][Full Text] [Related]
7. Bayesian Regularized Artificial Neural Network Model to Predict Strength Characteristics of Fly-Ash and Bottom-Ash Based Geopolymer Concrete.
Aneja S; Sharma A; Gupta R; Yoo DY
Materials (Basel); 2021 Apr; 14(7):. PubMed ID: 33915938
[TBL] [Abstract][Full Text] [Related]
8. Estimation of compressive strength of waste concrete utilizing fly ash/slag in concrete with interpretable approaches: optimization and graphical user interface (GUI).
Dodo Y; Arif K; Alyami M; Ali M; Najeh T; Gamil Y
Sci Rep; 2024 Feb; 14(1):4598. PubMed ID: 38409333
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Compressive Strength of Steel Fiber-Reinforced Concrete Employing Supervised Machine Learning Techniques.
Li Y; Zhang Q; Kamiński P; Deifalla AF; Sufian M; Dyczko A; Kahla NB; Atig M
Materials (Basel); 2022 Jun; 15(12):. PubMed ID: 35744270
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Evaluation of Artificial Intelligence Methods to Estimate the Compressive Strength of Geopolymers.
Zou Y; Zheng C; Alzahrani AM; Ahmad W; Ahmad A; Mohamed AM; Khallaf R; Elattar S
Gels; 2022 Apr; 8(5):. PubMed ID: 35621569
[TBL] [Abstract][Full Text] [Related]
13. Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil.
Abdullah GMS; Ahmad M; Babur M; Badshah MU; Al-Mansob RA; Gamil Y; Fawad M
Sci Rep; 2024 Jan; 14(1):2323. PubMed ID: 38282061
[TBL] [Abstract][Full Text] [Related]
14. Using Machine Learning Algorithms to Estimate the Compressive Property of High Strength Fiber Reinforced Concrete.
Dai L; Wu X; Zhou M; Ahmad W; Ali M; Sabri MMS; Salmi A; Ewais DYZ
Materials (Basel); 2022 Jun; 15(13):. PubMed ID: 35806575
[TBL] [Abstract][Full Text] [Related]
15. Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches.
Amin MN; Khan K; Ahmad W; Javed MF; Qureshi HJ; Saleem MU; Qadir MG; Faraz MI
Polymers (Basel); 2022 May; 14(10):. PubMed ID: 35632011
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. 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]
18. 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]
19. Prediction of Compressive Strength of Sustainable Foam Concrete Using Individual and Ensemble Machine Learning Approaches.
Ullah HS; Khushnood RA; Farooq F; Ahmad J; Vatin NI; Ewais DYZ
Materials (Basel); 2022 Apr; 15(9):. PubMed ID: 35591498
[TBL] [Abstract][Full Text] [Related]
20. Application of Advanced Machine Learning Approaches to Predict the Compressive Strength of Concrete Containing Supplementary Cementitious Materials.
Ahmad W; Ahmad A; Ostrowski KA; Aslam F; Joyklad P; Zajdel P
Materials (Basel); 2021 Oct; 14(19):. PubMed ID: 34640160
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
