Implementation of soft-computing models for prediction of flexural strength of pervious concrete hybridized with rice husk ash and calcium carbide waste-Reference-Cited by-同舟云学术

Implementation of soft-computing models for prediction of flexural strength of pervious concrete hybridized with rice husk ash and calcium carbide waste

Published:2021-06-02 Issue: Volume: Page:
ISSN:2363-6203
Container-title:Modeling Earth Systems and Environment
language:en
Short-container-title:Model. Earth Syst. Environ.

Author:

Malami Salim Idris,Musa A. A.,Haruna S. I.,Aliyu U. U.,Usman A. G.,Abdurrahman M. I.,Bashir Abba,Abba S. I.^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Computers in Earth Sciences,Statistics, Probability and Uncertainty,General Agricultural and Biological Sciences,General Environmental Science

Link

https://link.springer.com/content/pdf/10.1007/s40808-021-01195-4.pdf

Reference60 articles.

1. Abba SI, Abdulkadir RA, Gaya MS, Saleh MA, Esmaili P, Jibril MB (2019) Neuro-fuzzy ensemble techniques for the prediction of turbidity in water treatment plant. In: 2019 2nd international conference of the IEEE Nigeria computer Chapter, NigeriaComputConf 2019, 1–6. https://doi.org/10.1109/NigeriaComputConf45974.2019.8949629

2. Abdullahi G, IŞIK S, ABBA SI, MERİÇLİ F (2020) Artificial intelligence–based models for the qualitative and quantitative prediction of a phytochemical compound using HPLC method. Turkish J Chem 44(5):1339–1351. https://doi.org/10.3906/kim-2003-6

3. ACI 552R-10 (2010) Report on pervious concrete, American concrete institute. American Concrete Institute, Farmington Hills, Mishigan, 38

4. Alas M, Ismael S, Ali A, Ph D, Asce AM, Abdulhadi Y, Abba SI, Ph D (2020) Experimental evaluation and modeling of polymer nanocomposite modified asphalt binder using ANN and ANFIS. J Mater Civ Eng 32(10):1–11. https://doi.org/10.1061/(ASCE)MT.1943-5533.0003404

5. Albuthbahak OM, Alkhudery HH (2018) Artificial neural network model for flexural design of concrete hydraulic structures. Int J Civil Eng Technol 9(2):265–274

Cited by 32 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Machine learning approaches to predict the strength of graphene nanoplatelets concrete: Optimization and hyper tuning with graphical user interface;Materials Today Communications;2024-08

2. Response surface regression and machine learning models to predict the porosity and compressive strength of pervious concrete based on mix design parameters;Road Materials and Pavement Design;2024-07-09

3. Bond Strength Assessment of Normal Strength Concrete–Ultra-High-Performance Fiber Reinforced Concrete Using Repeated Drop-Weight Impact Test: Experimental and Machine Learning Technique;Materials;2024-06-20

4. A novel data-driven machine learning techniques to predict compressive strength of fly ash and recycled coarse aggregates based self-compacting concrete;Materials Today Communications;2024-06

5. Prediction model for compressive strength of rice husk ash blended sandcrete blocks using a machine learning models;Asian Journal of Civil Engineering;2024-05-24