180 related articles for article (PubMed ID: 35849229)
1. Splitting tensile strength prediction of sustainable high-performance concrete using machine learning techniques.
Wu Y; Zhou Y
Environ Sci Pollut Res Int; 2022 Dec; 29(59):89198-89209. PubMed ID: 35849229
[TBL] [Abstract][Full Text] [Related]
2. Interpretable Predictive Modelling of Basalt Fiber Reinforced Concrete Splitting Tensile Strength Using Ensemble Machine Learning Methods and SHAP Approach.
Cakiroglu C; Aydın Y; Bekdaş G; Geem ZW
Materials (Basel); 2023 Jun; 16(13):. PubMed ID: 37444890
[TBL] [Abstract][Full Text] [Related]
3. Splitting tensile strength prediction of Metakaolin concrete using machine learning techniques.
Li Q; Ren G; Wang H; Xu Q; Zhao J; Wang H; Ding Y
Sci Rep; 2023 Nov; 13(1):20102. PubMed ID: 37973915
[TBL] [Abstract][Full Text] [Related]
4. Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand.
Javed MF; Khan M; Fawad M; Alabduljabbar H; Najeh T; Gamil Y
Sci Rep; 2024 Jun; 14(1):14617. PubMed ID: 38918460
[TBL] [Abstract][Full Text] [Related]
5. High-Performance Concrete Strength Prediction Based on Machine Learning.
Liu Y
Comput Intell Neurosci; 2022; 2022():5802217. PubMed ID: 35669631
[TBL] [Abstract][Full Text] [Related]
6. Use of Artificial Intelligence Methods for Predicting the Strength of Recycled Aggregate Concrete and the Influence of Raw Ingredients.
Pan X; Xiao Y; Suhail SA; Ahmad W; Murali G; Salmi A; Mohamed A
Materials (Basel); 2022 Jun; 15(12):. PubMed ID: 35744254
[TBL] [Abstract][Full Text] [Related]
7. On the Use of Machine Learning Models for Prediction of Compressive Strength of Concrete: Influence of Dimensionality Reduction on the Model Performance.
Wan Z; Xu Y; Šavija B
Materials (Basel); 2021 Feb; 14(4):. PubMed ID: 33546376
[TBL] [Abstract][Full Text] [Related]
8. Predicting compressive strength of RCFST columns under different loading scenarios using machine learning optimization.
Wu F; Tang F; Lu R; Cheng M
Sci Rep; 2023 Oct; 13(1):16571. PubMed ID: 37789042
[TBL] [Abstract][Full Text] [Related]
9. A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete.
de-Prado-Gil J; Palencia C; Jagadesh P; Martínez-García R
Materials (Basel); 2022 Jun; 15(12):. PubMed ID: 35744223
[TBL] [Abstract][Full Text] [Related]
10. Data-driven prediction on critical mechanical properties of engineered cementitious composites based on machine learning.
Qing S; Li C
Sci Rep; 2024 Jul; 14(1):15322. PubMed ID: 38961183
[TBL] [Abstract][Full Text] [Related]
11. Predictive Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Artificial Intelligence Approaches: MLPNN, ANFIS, and GEP.
Nafees A; Javed MF; Khan S; Nazir K; Farooq F; Aslam F; Musarat MA; Vatin NI
Materials (Basel); 2021 Dec; 14(24):. PubMed ID: 34947124
[TBL] [Abstract][Full Text] [Related]
12. Application of Machine Learning Techniques for Predicting Compressive, Splitting Tensile, and Flexural Strengths of Concrete with Metakaolin.
Shah HA; Yuan Q; Akmal U; Shah SA; Salmi A; Awad YA; Shah LA; Iftikhar Y; Javed MH; Khan MI
Materials (Basel); 2022 Aug; 15(15):. PubMed ID: 35955370
[TBL] [Abstract][Full Text] [Related]
13. Unboxing machine learning models for concrete strength prediction using XAI.
Elhishi S; Elashry AM; El-Metwally S
Sci Rep; 2023 Nov; 13(1):19892. PubMed ID: 37963976
[TBL] [Abstract][Full Text] [Related]
14. Split Tensile Strength Prediction of Recycled Aggregate-Based Sustainable Concrete Using Artificial Intelligence Methods.
Amin MN; Ahmad A; Khan K; Ahmad W; Nazar S; Faraz MI; Alabdullah AA
Materials (Basel); 2022 Jun; 15(12):. PubMed ID: 35744356
[TBL] [Abstract][Full Text] [Related]
15. Non-Tuned Machine Learning Approach for Predicting the Compressive Strength of High-Performance Concrete.
Al-Shamiri AK; Yuan TF; Kim AJH
Materials (Basel); 2020 Feb; 13(5):. PubMed ID: 32106394
[TBL] [Abstract][Full Text] [Related]
16. The Efficiency of Hybrid Intelligent Models in Predicting Fiber-Reinforced Polymer Concrete Interfacial-Bond Strength.
Barkhordari MS; Armaghani DJ; Sabri MMS; Ulrikh DV; Ahmad M
Materials (Basel); 2022 Apr; 15(9):. PubMed ID: 35591352
[TBL] [Abstract][Full Text] [Related]
17. Precise prediction of multiple anticancer drug efficacy using multi target regression and support vector regression analysis.
Brindha GR; Rishiikeshwer BS; Santhi B; Nakendraprasath K; Manikandan R; Gandomi AH
Comput Methods Programs Biomed; 2022 Sep; 224():107027. PubMed ID: 35914385
[TBL] [Abstract][Full Text] [Related]
18. Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning.
Huang X; Wasouf M; Sresakoolchai J; Kaewunruen S
Materials (Basel); 2021 Jul; 14(15):. PubMed ID: 34361262
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Predicting Compressive and Splitting Tensile Strengths of Silica Fume Concrete Using M5P Model Tree Algorithm.
Shah HA; Nehdi ML; Khan MI; Akmal U; Alabduljabbar H; Mohamed A; Sheraz M
Materials (Basel); 2022 Aug; 15(15):. PubMed ID: 35955371
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]