Bakhta Boukhatem

640 total citations
19 papers, 534 citations indexed

About

Bakhta Boukhatem is a scholar working on Civil and Structural Engineering, Ocean Engineering and Safety, Risk, Reliability and Quality. According to data from OpenAlex, Bakhta Boukhatem has authored 19 papers receiving a total of 534 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Civil and Structural Engineering, 2 papers in Ocean Engineering and 2 papers in Safety, Risk, Reliability and Quality. Recurrent topics in Bakhta Boukhatem's work include Concrete and Cement Materials Research (11 papers), Infrastructure Maintenance and Monitoring (7 papers) and Innovative concrete reinforcement materials (7 papers). Bakhta Boukhatem is often cited by papers focused on Concrete and Cement Materials Research (11 papers), Infrastructure Maintenance and Monitoring (7 papers) and Innovative concrete reinforcement materials (7 papers). Bakhta Boukhatem collaborates with scholars based in Algeria, Canada and Yemen. Bakhta Boukhatem's co-authors include Mohamed Ghrici, Arezki Tagnit‐Hamou, Khelifa Harichane, Saïd Kenai, A. Hamou, Mahmoud N. Hussien and Mourad Karray and has published in prestigious journals such as SHILAP Revista de lepidopterología, Construction and Building Materials and Neural Computing and Applications.

In The Last Decade

Bakhta Boukhatem

18 papers receiving 516 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Bakhta Boukhatem Algeria 13 480 177 39 36 22 19 534
Son Hoang Trinh Vietnam 8 478 1.0× 199 1.1× 26 0.7× 45 1.3× 13 0.6× 11 560
Reza Sarkhani Benemaran Iran 8 272 0.6× 82 0.5× 65 1.7× 31 0.9× 15 0.7× 9 307
Fazel Azarhomayun Iran 6 287 0.6× 136 0.8× 19 0.5× 38 1.1× 19 0.9× 8 412
Amir Tavana Amlashi Iran 10 384 0.8× 183 1.0× 22 0.6× 73 2.0× 12 0.5× 20 485
Washington Peres Núñez Brazil 14 490 1.0× 170 1.0× 19 0.5× 58 1.6× 19 0.9× 42 563
Hai‐Van Thi Vietnam 14 497 1.0× 233 1.3× 17 0.4× 51 1.4× 24 1.1× 17 588
Amir Hossein Rafiean Iran 7 665 1.4× 345 1.9× 25 0.6× 46 1.3× 28 1.3× 7 749
Mariusz Maślak Poland 7 508 1.1× 222 1.3× 26 0.7× 82 2.3× 32 1.5× 68 636
Behnam Ghorbani Australia 16 483 1.0× 198 1.1× 116 3.0× 63 1.8× 16 0.7× 23 539
Eduardo Botero Jaramillo Mexico 9 409 0.9× 214 1.2× 49 1.3× 26 0.7× 8 0.4× 30 505

Countries citing papers authored by Bakhta Boukhatem

Since Specialization
Citations

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

Fields of papers citing papers by Bakhta Boukhatem

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bakhta Boukhatem

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

All Works

19 of 19 papers shown
1.
Boukhatem, Bakhta, et al.. (2022). Artificial neural network-based prediction of properties of self-compacting concrete containing limestone powder. Asian Journal of Civil Engineering. 23(6). 817–839. 4 indexed citations
2.
Kenai, Saïd, et al.. (2021). Prediction of Compressive Strength of Self-Compacting Concrete (SCC) with Silica Fume Using Neural Networks Models. Civil Engineering Journal. 7(1). 118–139. 19 indexed citations
3.
Ghrici, Mohamed, et al.. (2021). Service life prediction of fly ash concrete using an artificial neural network. Frontiers of Structural and Civil Engineering. 15(3). 793–805. 19 indexed citations
4.
Boukhatem, Bakhta, et al.. (2021). Neural network model for predicting the carbonation depth of slag concrete. Asian Journal of Civil Engineering. 22(7). 1401–1414. 18 indexed citations
5.
Boukhatem, Bakhta, et al.. (2019). An intelligent hybrid system for predicting the tortuosity of the pore system of fly ash concrete. Construction and Building Materials. 205. 274–284. 23 indexed citations
6.
Ghrici, Mohamed, et al.. (2018). Compressive strength prediction of limestone filler concrete using artificial neural networks. 3(3). 289. 40 indexed citations
7.
Boukhatem, Bakhta, et al.. (2018). Neural networks and principle component analysis approaches to predict pile capacity in sand. SHILAP Revista de lepidopterología. 149. 2025–2025. 1 indexed citations
8.
Boukhatem, Bakhta, et al.. (2018). Neural networks and principle component analysis approaches to predict pile capacity in sand. SHILAP Revista de lepidopterología. 149. 2025–2025. 4 indexed citations
9.
10.
Boukhatem, Bakhta, et al.. (2017). A practical hybrid NNGA system for predicting the compressive strength of concrete containing natural pozzolan using an evolutionary structure. Construction and Building Materials. 149. 778–789. 45 indexed citations
11.
Boukhatem, Bakhta, et al.. (2017). Prediction of axial capacity of piles driven in non-cohesive soils based on neural networks approach. Journal of Civil Engineering and Management. 23(3). 393–408. 18 indexed citations
12.
Harichane, Khelifa, et al.. (2017). Prediction of geotechnical properties of clayey soils stabilised with lime using artificial neural networks (ANNs). International Journal of Geotechnical Engineering. 13(2). 191–203. 50 indexed citations
13.
Boukhatem, Bakhta, et al.. (2017). Exploring the major factors affecting fly-ash concrete carbonation using artificial neural network. Neural Computing and Applications. 31(S2). 969–988. 62 indexed citations
14.
Boukhatem, Bakhta, et al.. (2016). Prediction of properties of self-compacting concrete containing fly ash using artificial neural network. Neural Computing and Applications. 28(S1). 707–718. 136 indexed citations
15.
Boukhatem, Bakhta, et al.. (2015). Prediction Compressive Strength Of Self-Compacting Concrete Containing Fly Ash Using Fuzzy Logic Inference System. Zenodo (CERN European Organization for Nuclear Research). 8(12). 1336–1340. 1 indexed citations
16.
Boukhatem, Bakhta, et al.. (2013). Prediction of Efficiency Factor of Natural Pozzolan by the Use of an Artificial Neural Network. Journals & Books Hosting (International Knowledge Sharing Platform). 4(1). 40–45. 2 indexed citations
17.
Boukhatem, Bakhta, et al.. (2012). Predicting concrete properties using neural networks (NN) with principal component analysis (PCA) technique. Computers and Concrete, an International Journal. 10(6). 557–573. 35 indexed citations
18.
Boukhatem, Bakhta, et al.. (2011). APPLICATION OF NEW INFORMATION TECHNOLOGY ON CONCRETE: AN OVERVIEW / NAUJŲ INFORMACINIŲ TECHNOLOGIJŲ NAUDOJIMAS RUOŠIANT BETONĄ. APŽVALGA. Journal of Civil Engineering and Management. 17(2). 248–258. 28 indexed citations
19.
Boukhatem, Bakhta, et al.. (2011). Prediction of Efficiency Factor of Ground-Granulated Blast-Furnace Slag of Concrete Using Artificial Neural Network. ACI Materials Journal. 108(1). 25 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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