Celal Çakıroğlu

1.1k total citations · 1 hit paper
40 papers, 711 citations indexed

About

Celal Çakıroğlu is a scholar working on Civil and Structural Engineering, Building and Construction and Mechanics of Materials. According to data from OpenAlex, Celal Çakıroğlu has authored 40 papers receiving a total of 711 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Civil and Structural Engineering, 15 papers in Building and Construction and 10 papers in Mechanics of Materials. Recurrent topics in Celal Çakıroğlu's work include Infrastructure Maintenance and Monitoring (13 papers), Innovative concrete reinforcement materials (11 papers) and Structural Behavior of Reinforced Concrete (8 papers). Celal Çakıroğlu is often cited by papers focused on Infrastructure Maintenance and Monitoring (13 papers), Innovative concrete reinforcement materials (11 papers) and Structural Behavior of Reinforced Concrete (8 papers). Celal Çakıroğlu collaborates with scholars based in Türkiye, Canada and South Korea. Celal Çakıroğlu's co-authors include Gebrai̇l Bekdaş, Kamrul Islam, Zong Woo Geem, Sanghun Kim, Moncef L. Nehdi, Laith Abualigah, Batin Latif Aylak, Gencay Sarıışık, Sercan Demir and Ümit Işıkdağ and has published in prestigious journals such as Construction and Building Materials, Expert Systems with Applications and Energy and Buildings.

In The Last Decade

Celal Çakıroğlu

36 papers receiving 661 citations

Hit Papers

Data-driven interpretable ensemble learning methods for t... 2023 2026 2024 2025 2023 40 80 120

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Celal Çakıroğlu Türkiye 15 462 303 92 68 62 40 711
Thuy‐Anh Nguyen Vietnam 18 770 1.7× 331 1.1× 96 1.0× 69 1.0× 33 0.5× 60 971
Murat Pala Türkiye 11 704 1.5× 244 0.8× 105 1.1× 77 1.1× 46 0.7× 14 834
Khaldoon A. Bani-Hani Jordan 16 576 1.2× 158 0.5× 104 1.1× 55 0.8× 40 0.6× 36 860
Danial Rezazadeh Eidgahee Iran 13 476 1.0× 248 0.8× 48 0.5× 47 0.7× 22 0.4× 20 589
Saeed Babanajad United States 12 596 1.3× 222 0.7× 104 1.1× 79 1.2× 86 1.4× 20 703
Masoud Ahmadi Iran 18 998 2.2× 623 2.1× 101 1.1× 82 1.2× 40 0.6× 37 1.3k
Hashem Jahangir Iran 17 764 1.7× 404 1.3× 70 0.8× 116 1.7× 20 0.3× 44 900
Arvindan Sivasuriyan India 12 292 0.6× 225 0.7× 58 0.6× 36 0.5× 64 1.0× 30 562
Majdi Flah Canada 5 964 2.1× 284 0.9× 160 1.7× 114 1.7× 33 0.5× 7 1.2k
Dandan Cao China 13 720 1.6× 270 0.9× 110 1.2× 59 0.9× 74 1.2× 34 853

Countries citing papers authored by Celal Çakıroğlu

Since Specialization
Citations

This map shows the geographic impact of Celal Çakıroğlu'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 Celal Çakıroğlu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Celal Çakıroğlu more than expected).

Fields of papers citing papers by Celal Çakıroğlu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Celal Çakıroğlu. 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 Celal Çakıroğlu. The network helps show where Celal Çakıroğlu may publish in the future.

Co-authorship network of co-authors of Celal Çakıroğlu

This figure shows the co-authorship network connecting the top 25 collaborators of Celal Çakıroğlu. A scholar is included among the top collaborators of Celal Çakıroğlu 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 Celal Çakıroğlu. Celal Çakıroğlu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Çakıroğlu, Celal, et al.. (2025). Explainable machine learning predictive model for mechanical strength of recycled ceramic tile-based concrete. Materials Today Communications. 44. 112139–112139. 3 indexed citations
2.
Çakıroğlu, Celal, et al.. (2025). Fatigue Predictive Modeling of Composite Materials for Wind Turbine Blades Using Explainable Gradient Boosting Models. Coatings. 15(3). 325–325. 4 indexed citations
3.
Çakıroğlu, Celal, et al.. (2025). Tensile Strength Predictive Modeling of Natural-Fiber-Reinforced Recycled Aggregate Concrete Using Explainable Gradient Boosting Models. Journal of Composites Science. 9(3). 119–119. 2 indexed citations
5.
Çakıroğlu, Celal, et al.. (2024). Explainable ensemble learning graphical user interface for predicting rebar bond strength and failure mode in recycled coarse aggregate concrete. Developments in the Built Environment. 20. 100547–100547. 4 indexed citations
6.
Çakıroğlu, Celal, et al.. (2024). Cooling load prediction of a double-story terrace house using ensemble learning techniques and genetic programming with SHAP approach. Energy and Buildings. 313. 114254–114254. 19 indexed citations
7.
Çakıroğlu, Celal, et al.. (2024). Explainable Ensemble Learning and Multilayer Perceptron Modeling for Compressive Strength Prediction of Ultra-High-Performance Concrete. Biomimetics. 9(9). 544–544. 10 indexed citations
8.
Çakıroğlu, Celal, Kamrul Islam, Gebrai̇l Bekdaş, & Moncef L. Nehdi. (2023). Data-driven ensemble learning approach for optimal design of cantilever soldier pile retaining walls. Structures. 51. 1268–1280. 18 indexed citations
9.
Çakıroğlu, Celal, et al.. (2023). Explainable ensemble learning data-driven modeling of mechanical properties of fiber-reinforced rubberized recycled aggregate concrete. Journal of Building Engineering. 76. 107279–107279. 69 indexed citations
10.
Çakıroğlu, Celal, et al.. (2023). Data-driven interpretable ensemble learning methods for the prediction of wind turbine power incorporating SHAP analysis. Expert Systems with Applications. 237. 121464–121464. 132 indexed citations breakdown →
11.
Çakıroğlu, Celal, Gebrai̇l Bekdaş, Ümit Işıkdağ, et al.. (2023). Neural Network Predictive Models for Alkali-Activated Concrete Carbon Emission Using Metaheuristic Optimization Algorithms. Sustainability. 16(1). 142–142. 9 indexed citations
14.
Liu, Tongxu, Celal Çakıroğlu, Kamrul Islam, Zhen Wang, & Moncef L. Nehdi. (2023). Explainable machine learning model for predicting punching shear strength of FRC flat slabs. Engineering Structures. 301. 117276–117276. 34 indexed citations
15.
Bekdaş, Gebrai̇l, Celal Çakıroğlu, Sanghun Kim, & Zong Woo Geem. (2023). Optimal Dimensions of Post-Tensioned Concrete Cylindrical Walls Using Harmony Search and Ensemble Learning with SHAP. Sustainability. 15(10). 7890–7890. 6 indexed citations
16.
Çakıroğlu, Celal & Gebrai̇l Bekdaş. (2023). Predictive Modeling of Recycled Aggregate Concrete Beam Shear Strength Using Explainable Ensemble Learning Methods. Sustainability. 15(6). 4957–4957. 13 indexed citations
17.
Batool, Farnaz, Kamrul Islam, Celal Çakıroğlu, & Anjuman Shahriar. (2021). Effectiveness of wood waste sawdust to produce medium- to low-strength concrete materials. Journal of Building Engineering. 44. 103237–103237. 51 indexed citations
18.
Çakıroğlu, Celal, Kamrul Islam, Gebrai̇l Bekdaş, & A. H. M. Muntasir Billah. (2021). CO2 Emission and Cost Optimization of Concrete-Filled Steel Tubular (CFST) Columns Using Metaheuristic Algorithms. Sustainability. 13(14). 8092–8092. 16 indexed citations
19.
Çakıroğlu, Celal, et al.. (2014). Evaluation of Pressurized Cold Bend Pipe Body Tensile Fractures Under Bending Loads. 1 indexed citations
20.
Çakıroğlu, Celal, et al.. (2012). Numerical Analysis of High Pressure Cold Bend Pipe to Investigate the Behaviour of Tension Side Fracture. 273–278. 3 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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