Author | Title | Year | Journal/Proceedings | DOI/URL | |
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Farzad Rezazadeh P, Amin Abrishambaf, Axel Dürrbaum, Gregor Zimmermann, Andreas Kroll | Investigating Reproducibility of Ultra-High Performance Concrete with Consistent Mechanical Properties: A Modeling Pipeline for Sparse Data in Complex Manufacturing [BibTeX] |
2024 | 34. Workshop Computational Intelligence, accepted | ||
BibTeX: @inproceedings{Rezazadeh2024GMA, author = {Farzad Rezazadeh P and Amin Abrishambaf and Axel Dürrbaum and Gregor Zimmermann and Andreas Kroll}, booktitle = {34. Workshop Computational Intelligence}, language = {english}, mrtnote = {nopeer,presenter:Rezazadeh,EEpBeton}, note = {accepted}, owner = {fr}, title = {Investigating Reproducibility of Ultra-High Performance Concrete with Consistent Mechanical Properties: A Modeling Pipeline for Sparse Data in Complex Manufacturing}, year = {2024} } |
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Farzad Rezazadeh, Axel Dürrbaum, Gregor Zimmermann, Andreas Kroll | Leveraging Ensemble Structures to Elucidate the Impact of Factors that Influence the Quality of Ultra–High Performance Concrete [BibTeX] |
2023 | 2023 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 180-187, Mexico City, Mexico, 5.-8. Dezember | DOI , URL | |
BibTeX: @inproceedings{RezazadehSSCI2023, address = {Mexico City, Mexico}, author = {Farzad Rezazadeh and Axel Dürrbaum and Gregor Zimmermann and Andreas Kroll}, booktitle = {2023 IEEE Symposium Series on Computational Intelligence (SSCI)}, doi = {10.1109/SSCI52147.2023.10371800}, language = {english}, month = {5.-8. Dezember}, mrtnote = {peer,EEpBeton}, owner = {fr}, pages = {180-187}, title = {Leveraging Ensemble Structures to Elucidate the Impact of Factors that Influence the Quality of Ultra–High Performance Concrete}, url = {https://ieeexplore.ieee.org/document/10371800}, year = {2023} } |
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Farzad Rezazadeh, Axel Dürrbaum, Gregor Zimmermann, Andreas Kroll | Holistic Modeling of Ultra-High Performance Concrete Production Process: Synergizing Mix Design, Fresh Concrete Properties and Curing Conditions [BibTeX] |
2023 | 33. Workshop Computational Intelligence, pp. 215-237, KIT Scientific Publishing, Berlin, Germany, Workshop CI, 23.-24. November | DOI , URL | |
BibTeX: @inproceedings{RezazadehGMA2023, address = {Berlin, Germany}, author = {Farzad Rezazadeh and Axel Dürrbaum and Gregor Zimmermann and Andreas Kroll}, booktitle = {33. Workshop Computational Intelligence}, doi = {10.5445/KSP/1000162754}, language = {english}, month = {23.--24. November}, mrtnote = {nopeer,presenter:Rezazadeh,EEpBeton}, organization = {Workshop CI}, owner = {fr}, pages = {215--237}, publisher = {KIT Scientific Publishing}, title = {Holistic Modeling of Ultra-High Performance Concrete Production Process: Synergizing Mix Design, Fresh Concrete Properties and Curing Conditions}, url = {https://doi.org/10.5445/KSP/1000162754}, year = {2023} } |
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Axel Dürrbaum, Farzad Rezazadeh, Andreas Kroll | Automatic Camera-based advanced Slump FlowTesting for Improved Reliability [BibTeX] |
2023 | IEEE Sensors 2023, Vienna, Austria, IEEE, 30. October | DOI , URL | |
BibTeX: @inproceedings{2023-ad_fr_ak_-Senors_2023-SFT_Camera, address = {Vienna, Austria}, author = {Axel Dürrbaum and Farzad Rezazadeh and Andreas Kroll}, booktitle = {IEEE Sensors 2023}, doi = {10.1109/SENSORS56945.2023.10325030}, language = {english}, month = {30. October}, mrtnote = {peer,presenter:Dürrbaum,EEpBeton}, organization = {IEEE}, owner = {duerrbaum}, title = {Automatic Camera-based advanced Slump FlowTesting for Improved Reliability}, url = {https://ieeexplore.ieee.org/document/10325030}, year = {2023} } |
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Farzad Rezazadeh, Andreas Kroll | Predicting the compressive strength of concrete up to 28 days-ahead: Comparison of machine learning algorithms on benchmark datasets [BibTeX] |
2022 | 32. Workshop Computational Intelligence, pp. 53-75, KIT Scientific Publishing, Berlin, Germany, GMA-FA 5.14, 1.-2. December | DOI , URL | |
BibTeX: @inproceedings{RezazadehGMACI2022, address = {Berlin, Germany}, author = {Farzad Rezazadeh and Andreas Kroll}, booktitle = {32. Workshop Computational Intelligence}, date = {2022}, doi = {10.5445/KSP/1000151141}, location = {Berlin}, month = {1.-2. December}, mrtnote = {nopeer,EEpBeton}, organization = {GMA-FA 5.14}, owner = {rezazadeh}, pages = {53-75}, publisher = {KIT Scientific Publishing}, timestamp = {2021.08.24}, title = {Predicting the compressive strength of concrete up to 28 days-ahead: Comparison of machine learning algorithms on benchmark datasets}, url = {https://doi.org/10.5445/KSP/1000151141}, year = {2022} } |
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