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For: Zeng J, Roussis PC, Mohammed AS, Maraveas C, Fatemi SA, Armaghani DJ, Asteris PG. Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels. Applied Sciences 2021;11:3705. [DOI: 10.3390/app11083705] [Cited by in Crossref: 13] [Cited by in F6Publishing: 7] [Article Influence: 13.0] [Reference Citation Analysis]
Number Citing Articles
1 Farouk AIB, Jinsong Z. Prediction of Interface Bond Strength Between Ultra-High-Performance Concrete (UHPC) and Normal Strength Concrete (NSC) Using a Machine Learning Approach. Arab J Sci Eng. [DOI: 10.1007/s13369-021-06433-6] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 5.0] [Reference Citation Analysis]
2 Kaffash Charandabi N, Gholami A, Abdollahzadeh Bina A. Road accident risk prediction using generalized regression neural network optimized with self-organizing map. Neural Comput & Applic. [DOI: 10.1007/s00521-021-06549-8] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Emad W, Salih A, Kurda R, Hassan A. Multivariable models to forecast the mechanical properties of polymerized cement paste. Journal of Materials Research and Technology 2021;14:2677-99. [DOI: 10.1016/j.jmrt.2021.07.137] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
4 Emad W, Salih A, Kurda R. Experimental study using ASTM and BS standards and model evaluations to predict the compressive strength of the cement grouted sands modified with polymer. Case Studies in Construction Materials 2021;15:e00600. [DOI: 10.1016/j.cscm.2021.e00600] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
5 Niu Y, Cheng H, Wu S, Sun J, Wang J. Rheological properties of cemented paste backfill and the construction of a prediction model. Case Studies in Construction Materials 2022;16:e01140. [DOI: 10.1016/j.cscm.2022.e01140] [Reference Citation Analysis]
6 Xue J, Shao J, Burlion N. Estimation of constituent properties of concrete materials with an artificial neural network based method. Cement and Concrete Research 2021;150:106614. [DOI: 10.1016/j.cemconres.2021.106614] [Reference Citation Analysis]
7 Akbarzadeh M, Shaffiee Haghshenas S, Jalali SME, Zare S, Mikaeil R. Developing the Rule of Thumb for Evaluating Penetration Rate of TBM, Using Binary Classification. Geotech Geol Eng. [DOI: 10.1007/s10706-022-02178-7] [Reference Citation Analysis]