Mohammadjavad Zeinali

529 total citations
16 papers, 418 citations indexed

About

Mohammadjavad Zeinali is a scholar working on Civil and Structural Engineering, Mechanical Engineering and Computational Mechanics. According to data from OpenAlex, Mohammadjavad Zeinali has authored 16 papers receiving a total of 418 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Civil and Structural Engineering, 8 papers in Mechanical Engineering and 4 papers in Computational Mechanics. Recurrent topics in Mohammadjavad Zeinali's work include Vibration Control and Rheological Fluids (6 papers), Advanced machining processes and optimization (4 papers) and Hydraulic and Pneumatic Systems (4 papers). Mohammadjavad Zeinali is often cited by papers focused on Vibration Control and Rheological Fluids (6 papers), Advanced machining processes and optimization (4 papers) and Hydraulic and Pneumatic Systems (4 papers). Mohammadjavad Zeinali collaborates with scholars based in Malaysia, Iran and Canada. Mohammadjavad Zeinali's co-authors include Victor Songméné, Chris K. Mechefske, Saiful Amri Mazlan, Hairi Zamzuri, Abdul Yasser Abd Fatah, Fitrian Imaduddin, Jules Kouam, J.N. Sahu, M. Pourtousi and P. Ganesan and has published in prestigious journals such as RSC Advances, Smart Materials and Structures and The International Journal of Advanced Manufacturing Technology.

In The Last Decade

Mohammadjavad Zeinali

16 papers receiving 409 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mohammadjavad Zeinali Malaysia 10 247 150 114 109 61 16 418
Pavlo Krot Poland 13 269 1.1× 66 0.4× 29 0.3× 46 0.4× 37 0.6× 41 366
Jieling Xiao China 8 272 1.1× 164 1.1× 41 0.4× 81 0.7× 23 0.4× 10 339
Loïc Andolfatto Switzerland 12 241 1.0× 99 0.7× 26 0.2× 37 0.3× 77 1.3× 25 395
Fábio Romano Lofrano Dotto Brazil 10 398 1.6× 47 0.3× 109 1.0× 158 1.4× 29 0.5× 28 474
Xianyin Duan China 14 441 1.8× 19 0.1× 203 1.8× 107 1.0× 161 2.6× 41 513
Simul Banerjee India 14 384 1.6× 53 0.4× 255 2.2× 263 2.4× 50 0.8× 31 497
Ali Ünüvar Türkiye 10 288 1.2× 36 0.2× 104 0.9× 110 1.0× 78 1.3× 20 342
Barry K. Fussell United States 12 632 2.6× 64 0.4× 278 2.4× 292 2.7× 317 5.2× 40 752

Countries citing papers authored by Mohammadjavad Zeinali

Since Specialization
Citations

This map shows the geographic impact of Mohammadjavad Zeinali's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mohammadjavad Zeinali with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohammadjavad Zeinali more than expected).

Fields of papers citing papers by Mohammadjavad Zeinali

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mohammadjavad Zeinali. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mohammadjavad Zeinali. The network helps show where Mohammadjavad Zeinali may publish in the future.

Co-authorship network of co-authors of Mohammadjavad Zeinali

This figure shows the co-authorship network connecting the top 25 collaborators of Mohammadjavad Zeinali. A scholar is included among the top collaborators of Mohammadjavad Zeinali based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mohammadjavad Zeinali. Mohammadjavad Zeinali is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
1.
Zeinali, Mohammadjavad, Gholamhossein Rahimi, & Shahram Hosseini. (2025). Nondestructive method for predicting buckling loads of sandwich plates with functionally graded porous layers using particle swarm optimization and bagging technique (machine learning). Mechanics Based Design of Structures and Machines. 53(10). 6891–6923. 2 indexed citations
2.
Zeinali, Mohammadjavad, Gholamhossein Rahimi, & Shahram Hosseini. (2024). Buckling load optimization of sandwich plates with trapezoidal corrugated core and elliptical cutout using vibration correlation techniques and artificial neural network; experimental and numerical analysis. Thin-Walled Structures. 200. 111957–111957. 9 indexed citations
3.
Zeinali, Mohammadjavad, Gholamhossein Rahimi, & Shahram Hosseini. (2023). Optimizing buckling load of sandwich plates with cutouts using artificial neural networks and genetic algorithms. Mechanics Based Design of Structures and Machines. 52(9). 6173–6190. 11 indexed citations
4.
Zeinali, Mohammadjavad, et al.. (2021). Tool wear prediction in high-speed turning of a steel alloy using long short-term memory modelling. Measurement. 177. 109329–109329. 93 indexed citations
5.
Zeinali, Mohammadjavad, et al.. (2021). Neuro-fuzzy based predictive model for cutting force in CNC turning process of Al–Si–Cu cast alloy using modifier elements. SN Applied Sciences. 3(1). 8 indexed citations
6.
Zeinali, Mohammadjavad, et al.. (2020). Prediction of cutting tool wear during a turning process using artificial intelligence techniques. The International Journal of Advanced Manufacturing Technology. 111(1-2). 505–515. 36 indexed citations
7.
Songméné, Victor, et al.. (2019). Neuro-fuzzy predictive model for surface roughness and cutting force of machined Al–20 Mg2Si–2Cu metal matrix composite using additives. Neural Computing and Applications. 32(12). 8115–8126. 29 indexed citations
8.
Zeinali, Mohammadjavad & Mohsen Pourreza‐Bilondi. (2018). Estimation of Optimal Parameters of the Nonlinear Muskingum Model Using Continuous Ant Colony Algorithm. 8(3). 94–106. 1 indexed citations
9.
Zeinali, Mohammadjavad, et al.. (2016). Influence of piston and magnetic coils on the field-dependent damping performance of a mixed-mode magnetorheological damper. Smart Materials and Structures. 25(5). 55010–55010. 21 indexed citations
10.
Pourtousi, M., Mohammadjavad Zeinali, P. Ganesan, & J.N. Sahu. (2015). Prediction of multiphase flow pattern inside a 3D bubble column reactor using a combination of CFD and ANFIS. RSC Advances. 5(104). 85652–85672. 50 indexed citations
11.
Fatah, Abdul Yasser Abd, Saiful Amri Mazlan, Tsuyoshi Koga, et al.. (2015). A review of design and modeling of magnetorheological valve. International Journal of Modern Physics B. 29(4). 1530004–1530004. 62 indexed citations
12.
Zeinali, Mohammadjavad, Saiful Amri Mazlan, Abdul Yasser Abd Fatah, & Hairi Zamzuri. (2014). A GA-Weighted Adaptive Neuro-Fuzzy Model to Predict the Behaviour of Magnetorheological Damper. Applied Mechanics and Materials. 663. 203–207. 5 indexed citations
13.
Zeinali, Mohammadjavad, et al.. (2014). Experiments and modeling of a new magnetorheological cell under combination of flow and shear-flow modes. Journal of Non-Newtonian Fluid Mechanics. 215. 70–79. 11 indexed citations
14.
Zeinali, Mohammadjavad, Saiful Amri Mazlan, Abdul Yasser Abd Fatah, & Hairi Zamzuri. (2013). A phenomenological dynamic model of a magnetorheological damper using a neuro-fuzzy system. Smart Materials and Structures. 22(12). 125013–125013. 36 indexed citations
15.
Sidik, Nor Azwadi Che, et al.. (2013). Adaptive-Network-Based Fuzzy Inference System Analysis to Predict the Temperature and Flow Fields in a Lid-Driven Cavity. Numerical Heat Transfer Part A Applications. 63(12). 906–920. 38 indexed citations
16.
Zeinali, Mohammadjavad & Intan Zaurah Mat Darus. (2012). Fuzzy PID controller simulation for a quarter-car semi-active suspension system using Magnetorheological damper. 36. 104–108. 6 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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