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191 ta ish

Ish: Predictive modeling for compressive strength of blended cement concrete using hybrid machine learning models

  1. Grey Wolf Optimizer

    Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis

    MaqolaOchiq kirish201426 iqtibos
    ABI

  2. Particle swarm optimization

    James Kennedy, R.C. Eberhart

    Maqola200217 iqtibos
    ABI

  3. Beware of q2!

    Alexander Golbraikh, Alexander Tropsha

    Maqola200213 iqtibos
    ABI

  4. On Some Aspects of Variable Selection for Partial Least Squares Regression Models

    Partha Pratim Roy, Kunal Roy

    Maqola20078 iqtibos
    ABI

  5. A tutorial on support vector regression

    Alex Smola, Bernhard Schölkopf

    Maqola20048 iqtibos
    ABI

  6. Eco-efficient cements: Potential economically viable solutions for a low-CO2 cement-based materials industry

    Karen Scrivener, Vanderley Moacyr John, E.M. Gartner

    Maqola20188 iqtibos
    ABI

  7. A Tutorial on Support Vector Machines for Pattern Recognition

    Christopher J. C. Burges

    Maqola19984 iqtibos
    ABI

  8. Supplementary cementitious materials

    Barbara Lothenbach, Karen Scrivener, R.D. Hooton

    Maqola20114 iqtibos
    ABI

  9. Assessment of artificial neural network and genetic programming as predictive tools

    Amir H. Gandomi, David A. Roke

    Maqola20153 iqtibos
    ABI

  10. Performance comparison of cement replacing materials in concrete: Limestone fillers and supplementary cementing materials – A review

    Daman K. Panesar, Runxiao Zhang

    Sharh maqola20203 iqtibos
    ABI

  11. Compressive strength prediction of fly ash-based geopolymer concrete via advanced machine learning techniques

    Ayaz Ahmad, Waqas Ahmad, Fahid Aslam +1

    Maqola20213 iqtibos
    ABI

  12. Long-term mechanical and durability properties of recycled aggregate concrete prepared with the incorporation of fly ash

    Shi-Cong Kou, Chi Sun Poon

    Maqola20123 iqtibos
    ABI

  13. Sarlavhasiz

    Boshqa3 iqtibos
    ABI

  14. Supplementary cementitious materials: New sources, characterization, and performance insights

    Maria Juenger, Ruben Snellings, Susan A. Bernal

    Maqola20193 iqtibos
    ABI

  15. Physical, chemical, and geotechnical properties of coal fly ash: A global review

    Arpita Bhatt, Sharon Priyadarshini, Aiswarya Acharath Mohanakrishnan +3

    Sharh maqola20193 iqtibos
    ABI

  16. Novel Approach to Strength Modeling of Concrete under Triaxial Compression

    Amir H. Gandomi, Saeed Babanajad, Amir H. Alavi +1

    Maqola20122 iqtibos
    ABI

  17. Predictive modeling for sustainable high-performance concrete from industrial wastes: A comparison and optimization of models using ensemble learners

    Furqan Farooq, Wisal Ahmed, Arslan Akbar +2

    Maqola20212 iqtibos
    ABI

  18. Utilization of fly ash with silica fume and properties of Portland cement–fly ash–silica fume concrete

    Thanongsak Nochaiya, Watcharapong Wongkeo, Arnon Chaipanich

    Maqola20092 iqtibos
    ABI

  19. Swarm Intelligence and Bio-Inspired Computation

    Xin‐She Yang, Mehmet Karamanoglu

    Bob20132 iqtibos
    ABI

  20. Predictive modeling of swell-strength of expansive soils using artificial intelligence approaches: ANN, ANFIS and GEP

    Fazal E. Jalal, Yongfu Xu, Mudassir Iqbal +2

    Maqola20212 iqtibos
    ABI

  21. Application of Advanced Machine Learning Approaches to Predict the Compressive Strength of Concrete Containing Supplementary Cementitious Materials

    Waqas Ahmad, Ayaz Ahmad, Krzysztof Adam Ostrowski +3

    Maqola20212 iqtibos
    ABI

  22. Sarlavhasiz

    Boshqa2 iqtibos
    ABI

  23. Physical properties and mechanical behaviour of concrete made with recycled aggregates and fly ash

    Carmine Lima, António Caggiano, Ciro Faella +3

    Maqola20132 iqtibos
    ABI

  24. Sarlavhasiz

    Boshqa2 iqtibos
    ABI

  25. Sarlavhasiz

    Boshqa2 iqtibos
    ABI