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Regional Variations in the Physical and Rheological Properties of Asphalt Binders Recovered from Earthquake-Damaged Roads Following the February 6, 2023 Türkiye Earthquakes

Year 2025, Volume: 4 Issue: 3, 657 - 669, 20.10.2025
https://doi.org/10.62520/fujece.1766725

Abstract

This study investigates the physical and rheological properties of asphalt binders recovered from earthquake-damaged road sections in Hatay, Kahramanmaras, Gaziantep, and Malatya following the February 6, 2023, earthquakes in southern Türkiye (Mw = 7.8 and Mw = 7.5). Asphalt mixture samples were collected from severely deteriorated pavements, and bitumen was extracted through solvent extraction and rotary evaporation. The recovered binders were subjected to penetration, softening point, rotational viscosity, and Dynamic Shear Rheometer (DSR) tests to evaluate aging, stiffness, high-temperature performance, and viscoelastic behavior. Results revealed significant regional differences: Hatay binders exhibited the lowest penetration, highest softening point, highest viscosity, and the greatest rutting resistance (high G*/sinδ values) while maintaining lower phase angles, indicating enhanced stiffness and elasticity. In contrast, Kahramanmaras binders showed the highest penetration, lowest softening point, and lower viscosity, combined with high phase angles, reflecting a softer, more viscous character with reduced deformation resistance. Malatya and Gaziantep binders displayed intermediate characteristics, with Malatya showing moderate stiffness and Gaziantep retaining greater flexibility. Across all samples, viscosity decreased markedly with increasing temperature, and rutting resistance diminished at higher service temperatures; however, binders with higher initial G*/sinδ values retained better performance. The findings highlight the influence of regional seismic intensity, climatic conditions, and pavement history on binder behavior and emphasize the need for region-specific maintenance and rehabilitation strategies in post-disaster pavement management.

Ethical Statement

There is no conflict of interest with any person/institution in the prepared article.

References

  • K. Hacıefendioğlu, and H.B. Başağa, “Concrete road crack detection using deep learning-based Faster R-CNN method,” Iran. J. Sci. Technol. Trans. Civ. Eng., vol. 46, no. 2, pp. 1621–1633, Apr. 2022.
  • A.C. Altunişik, M.E. Arslan, V. Kahya, B. Aslan, T. Sezdirmez, G. Dok, O. Kirtel, H. Öztürk, F. Sunca, A. Baltaci, M. Emiroğlu, M. Günaydin, S. Adanur, B. Atmaca, T. Akgül, A. Demir, T. Tatar, B. Aykanat, K. Haciefendioğlu, A. Saribiyik, M. Yurdakul, Y.E. Akbulut, F.Y. Okur, F. Şen, A.F. Genç, H.B. Başağa, E. Demirkaya, O. Güleş, and M. Nas, “Field observations and damage evaluation in reinforced concrete uildings after the February 6th, 2023, KahramanmaraşmTürkiye earthquakes,” J. Earthq. Tsunami, vol. 17, no. 06, Dec. 2023.
  • B. Atmaca et al., “On the earthquake-related damages of civil engineering structures within the areas impacted by Kahramanmaraş earthquakes,” J. Struct. Eng. Appl. Mech., vol. 6, no. 2, Jul. 2023.
  • B. Atmaca, M.E. Arslan, M. Emiroğlu, A.C. Altunışık, S. Adanur, A. Demir, M. Günaydın, O. Kırtel, T. Tatar, V. Kahya, F. Sunca, F.Y. Okur, K. Hacıefendioğlu, G. Dok, H. Öztürk, İ. Vural, O. Güleş, A.F. Genç, E. Demirkaya, M. Yurdakul, M. Nas, Y.E. Akbulut, A. Baltacı, B.A. Temel , H.B. Başağa, A. Sarıbıyık, F. Şen, B. Aykanat, İ.Ş. Öztürk, M.B. Navdar, F. Aydın, K. Öntürk, M. Utkucu, and T. Akgü, “Field observations and numerical investigations on seismic damage assessment of RC and masonry minarets during the February 6th, 2023, Kahramanmaraş (Mw 7.7 Pazarcık and Mw 7.6 Elbistan) earthquakes in Türkiye,” Int. J. Archit. Herit., vol. 19, no. 7, pp. 1117–1142, Jul. 2025.
  • F. Liu, J. Liu, and L. Wang, “Asphalt pavement crack detection based on convolutional neural network and infrared thermography,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 11, pp. 22145–22155, Nov. 2022.
  • F. Liu, and L. Wang, “UNet-based model for crack detection integrating visual explanations,” Constr. Build. Mater., vol. 322, p. 126265, Mar. 2022.
  • Z. Liu, Y. Cao, Y. Wang, and W. Wang, “Computer vision-based concrete crack detection using U-net fully convolutional networks,” Autom. Constr., vol. 104, pp. 129–139, Aug. 2019.
  • A. Cubero-Fernandez, F. J. Rodriguez-Lozano, R. Villatoro, J. Olivares, and J. M. Palomares, “Efficient pavement crack detection and classification,” EURASIP J. Image Video Process., vol. 2017, no. 1, p. 39, Dec. 2017.
  • G. Doğan, and B. Ergen, “Karayollarındaki asfalt çatlaklarının tespiti için yeni bir konvolüsyonel sinir ağı tabanlı yöntem,” Fırat Univ. Mühendislik Bilim. Derg., vol. 34, no. 2, pp. 485–494, Sep. 2022.
  • J. Guan, X. Yang, L. Ding, X. Cheng, V. C. S. Lee, and C. Jin, “Automated pixel-level pavement distress detection based on stereo vision and deep learning,” Autom. Constr., vol. 129, p. 103788, Sep. 2021.
  • B. Li, K. C. P. Wang, A. Zhang, E. Yang, and G. Wang, “Automatic classification of pavement crack using deep convolutional neural network,” Int. J. Pavement Eng., vol. 21, no. 4, pp. 457–463, Mar. 2020.
  • P. Miao ,and T. Srimahachota, “Cost-effective system for detection and quantification of concrete surface cracks by combination of convolutional neural network and image processing techniques,” Constr. Build. Mater., vol. 293, p. 123549, Jul. 2021.
  • U. A. Nnolim, “Automated crack segmentation via saturation channel thresholding, area classification and fusion of modified level set segmentation with Canny edge detection,” Heliyon, vol. 6, no. 12, p. e05748, Dec. 2020.
  • K. Hacıefendioğlu, H. B. Başağa, V. Kahya, K. Özgan, and A. C. Altunışık, “Automatic detection of collapsed buildings after the 6 February 2023 Türkiye earthquakes using post-disaster satellite images with deep learning-based semantic segmentation models,” Buildings, vol. 14, no. 3, p. 582, Feb. 2024.
  • S. Dorafshan, R. J. Thomas, and M. Maguire, “Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete,” Constr. Build. Mater., vol. 186, pp. 1031–1045, Oct. 2018.
  • K. Demir ,and O. Yaman, “A HOG feature extractor and KNN-based method for underwater image classification,” Fırat Univ. J. Exp. Comput. Eng., vol. 3, no. 1, pp. 1–10, Feb. 2024.
  • F. Demir, E. Yalcin, and M. Yilmaz, “CrackNet: A new deep learning-based strategy for automatic classification of road cracks after earthquakes,” Eng. Sci. Technol. Int. J., vol. 69, p. 102128, Sep. 2025.
  • M. Yılmaz; E. Yalçın, S. Kifah, F. Demir, A. Şengür, and R. Demir, All Authors, “Improving the classification performance of asphalt cracks after earthquake with a new feature selection algorithm,” IEEE Access, vol. 12, pp. 6604–6614, 2024.
  • M. Yilmaz, E. Yalcin, F. Demir, A.M. Ozdemir, M. Atar, A. Gune, “Automatic segmentation of asphalt cracks on highways after large-scale and severe earthquakes using deep learning-based approaches,” IEEE Access, vol. 13, pp. 22820–22830, 2025.
  • B. F. Yalçın, and M. Yilmaz, “Investigation of the performance of bio-oils from three different agricultural wastes as rejuvenators for recycled asphalt,” Turkish J. Civ. Eng., vol. 35, no. 3, pp. 95–123, May 2024.
  • B. F. Yalçın, M. Yilmaz, and H. S. Altundogan, “Experimental investigation of rejuvenated asphalt mixtures using bio-oils from different biomass sources,” Bitlis Eren Univ. Fen Bilim. Derg., vol. 13, no. 4, pp. 988–998, Dec. 2024.
  • Ö. Karadağ, and M. Saltan, “Investigation of adhesion and stripping properties of asphalt modified with bio-oil,” Fırat Univ. J. Exp. Comput. Eng., vol. 4, no. 2, pp. 262–275, Jun. 2025.
  • E. Yalçın, “Investigation of rheological properties of aged polymer modified binders,” J. Innov. Sci. Eng., Apr. 2024.
  • H. Aydin, E. Yalçin, M. Yilmaz, and T. Alataş, “Investigation of physical and rheological properties of Elvaloy and polyphosphoric acid modified bitumen,” Afyon Kocatepe Univ. J. Sci. Eng., vol. 22, no. 5, pp. 1122–1128, Oct. 2022.

6 Şubat 2023 Türkiye Depremleri Sonrası Hasar Görmüş Yollardan Elde Edilen Asfalt Bağlayıcılarının Fiziksel ve Reolojik Özelliklerindeki Bölgesel Farklılıklar

Year 2025, Volume: 4 Issue: 3, 657 - 669, 20.10.2025
https://doi.org/10.62520/fujece.1766725

Abstract

Bu çalışma, 6 Şubat 2023’te Güneydoğu Türkiye’de meydana gelen (Mw = 7.8 ve Mw = 7.5) depremler sonrasında Hatay, Kahramanmaraş, Gaziantep ve Malatya’daki hasar görmüş yol kesimlerinden alınan asfalt karışım numunelerinden elde edilen bağlayıcıların fiziksel ve reolojik özelliklerini incelemektedir. Şiddetli bozulmaların gözlendiği kaplamalardan alınan numunelerden, çözücü ekstraksiyonu ve rotary evaporasyon yöntemleri ile bitüm geri kazanılmıştır. Geri kazanılan bağlayıcılar; yaşlanma, sertlik, yüksek sıcaklık performansı ve viskoelastik davranışlarını değerlendirmek amacıyla penetrasyon, yumuşama noktası, dönel viskozite ve Dinamik Kesme Reometresi (DSR) deneylerine tabi tutulmuştur. Sonuçlar, bölgeler arasında belirgin farklılıklar olduğunu göstermiştir: Hatay bağlayıcıları en düşük penetrasyon, en yüksek yumuşama noktası, en yüksek viskozite ve en yüksek tekerlek izi direncine (yüksek G*/sinδ değerleri) sahip olup, daha düşük faz açıları ile yüksek sıcaklıklarda dahi elastikiyetini korumuştur. Buna karşılık Kahramanmaraş bağlayıcıları en yüksek penetrasyon, en düşük yumuşama noktası, düşük viskozite ve yüksek faz açıları ile daha yumuşak, daha viskoz bir yapı sergilemiş ve yüksek sıcaklıklarda kalıcı deformasyona karşı daha düşük direnç göstermiştir. Malatya ve Gaziantep bağlayıcıları ise ara özellikler sunmuş; Malatya bağlayıcıları orta sertlikte, Gaziantep bağlayıcıları ise daha yüksek esnekliğini koruyan yapıda bulunmuştur. Tüm numunelerde sıcaklık arttıkça viskozite değerleri önemli ölçüde azalmış, DSR sonuçları ise yüksek sıcaklıklarda tekerlek izi direncinin düştüğünü, ancak başlangıçta yüksek G*/sinδ değerine sahip bağlayıcıların performansını daha iyi koruduğunu ortaya koymuştur. Bulgular, yerel deprem şiddeti, iklim koşulları ve kaplama kullanım geçmişinin deprem sonrası bağlayıcı davranışı üzerinde doğrudan etkili olduğunu ve afet sonrası bakım-onarım stratejilerinin her bölgenin özgün malzeme özelliklerine göre uyarlanması gerektiğini vurgulamaktadır.

Ethical Statement

Hazırlanan makalede herhangi bir kişi/kurumla çıkar çatışması bulunmamaktadır.

References

  • K. Hacıefendioğlu, and H.B. Başağa, “Concrete road crack detection using deep learning-based Faster R-CNN method,” Iran. J. Sci. Technol. Trans. Civ. Eng., vol. 46, no. 2, pp. 1621–1633, Apr. 2022.
  • A.C. Altunişik, M.E. Arslan, V. Kahya, B. Aslan, T. Sezdirmez, G. Dok, O. Kirtel, H. Öztürk, F. Sunca, A. Baltaci, M. Emiroğlu, M. Günaydin, S. Adanur, B. Atmaca, T. Akgül, A. Demir, T. Tatar, B. Aykanat, K. Haciefendioğlu, A. Saribiyik, M. Yurdakul, Y.E. Akbulut, F.Y. Okur, F. Şen, A.F. Genç, H.B. Başağa, E. Demirkaya, O. Güleş, and M. Nas, “Field observations and damage evaluation in reinforced concrete uildings after the February 6th, 2023, KahramanmaraşmTürkiye earthquakes,” J. Earthq. Tsunami, vol. 17, no. 06, Dec. 2023.
  • B. Atmaca et al., “On the earthquake-related damages of civil engineering structures within the areas impacted by Kahramanmaraş earthquakes,” J. Struct. Eng. Appl. Mech., vol. 6, no. 2, Jul. 2023.
  • B. Atmaca, M.E. Arslan, M. Emiroğlu, A.C. Altunışık, S. Adanur, A. Demir, M. Günaydın, O. Kırtel, T. Tatar, V. Kahya, F. Sunca, F.Y. Okur, K. Hacıefendioğlu, G. Dok, H. Öztürk, İ. Vural, O. Güleş, A.F. Genç, E. Demirkaya, M. Yurdakul, M. Nas, Y.E. Akbulut, A. Baltacı, B.A. Temel , H.B. Başağa, A. Sarıbıyık, F. Şen, B. Aykanat, İ.Ş. Öztürk, M.B. Navdar, F. Aydın, K. Öntürk, M. Utkucu, and T. Akgü, “Field observations and numerical investigations on seismic damage assessment of RC and masonry minarets during the February 6th, 2023, Kahramanmaraş (Mw 7.7 Pazarcık and Mw 7.6 Elbistan) earthquakes in Türkiye,” Int. J. Archit. Herit., vol. 19, no. 7, pp. 1117–1142, Jul. 2025.
  • F. Liu, J. Liu, and L. Wang, “Asphalt pavement crack detection based on convolutional neural network and infrared thermography,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 11, pp. 22145–22155, Nov. 2022.
  • F. Liu, and L. Wang, “UNet-based model for crack detection integrating visual explanations,” Constr. Build. Mater., vol. 322, p. 126265, Mar. 2022.
  • Z. Liu, Y. Cao, Y. Wang, and W. Wang, “Computer vision-based concrete crack detection using U-net fully convolutional networks,” Autom. Constr., vol. 104, pp. 129–139, Aug. 2019.
  • A. Cubero-Fernandez, F. J. Rodriguez-Lozano, R. Villatoro, J. Olivares, and J. M. Palomares, “Efficient pavement crack detection and classification,” EURASIP J. Image Video Process., vol. 2017, no. 1, p. 39, Dec. 2017.
  • G. Doğan, and B. Ergen, “Karayollarındaki asfalt çatlaklarının tespiti için yeni bir konvolüsyonel sinir ağı tabanlı yöntem,” Fırat Univ. Mühendislik Bilim. Derg., vol. 34, no. 2, pp. 485–494, Sep. 2022.
  • J. Guan, X. Yang, L. Ding, X. Cheng, V. C. S. Lee, and C. Jin, “Automated pixel-level pavement distress detection based on stereo vision and deep learning,” Autom. Constr., vol. 129, p. 103788, Sep. 2021.
  • B. Li, K. C. P. Wang, A. Zhang, E. Yang, and G. Wang, “Automatic classification of pavement crack using deep convolutional neural network,” Int. J. Pavement Eng., vol. 21, no. 4, pp. 457–463, Mar. 2020.
  • P. Miao ,and T. Srimahachota, “Cost-effective system for detection and quantification of concrete surface cracks by combination of convolutional neural network and image processing techniques,” Constr. Build. Mater., vol. 293, p. 123549, Jul. 2021.
  • U. A. Nnolim, “Automated crack segmentation via saturation channel thresholding, area classification and fusion of modified level set segmentation with Canny edge detection,” Heliyon, vol. 6, no. 12, p. e05748, Dec. 2020.
  • K. Hacıefendioğlu, H. B. Başağa, V. Kahya, K. Özgan, and A. C. Altunışık, “Automatic detection of collapsed buildings after the 6 February 2023 Türkiye earthquakes using post-disaster satellite images with deep learning-based semantic segmentation models,” Buildings, vol. 14, no. 3, p. 582, Feb. 2024.
  • S. Dorafshan, R. J. Thomas, and M. Maguire, “Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete,” Constr. Build. Mater., vol. 186, pp. 1031–1045, Oct. 2018.
  • K. Demir ,and O. Yaman, “A HOG feature extractor and KNN-based method for underwater image classification,” Fırat Univ. J. Exp. Comput. Eng., vol. 3, no. 1, pp. 1–10, Feb. 2024.
  • F. Demir, E. Yalcin, and M. Yilmaz, “CrackNet: A new deep learning-based strategy for automatic classification of road cracks after earthquakes,” Eng. Sci. Technol. Int. J., vol. 69, p. 102128, Sep. 2025.
  • M. Yılmaz; E. Yalçın, S. Kifah, F. Demir, A. Şengür, and R. Demir, All Authors, “Improving the classification performance of asphalt cracks after earthquake with a new feature selection algorithm,” IEEE Access, vol. 12, pp. 6604–6614, 2024.
  • M. Yilmaz, E. Yalcin, F. Demir, A.M. Ozdemir, M. Atar, A. Gune, “Automatic segmentation of asphalt cracks on highways after large-scale and severe earthquakes using deep learning-based approaches,” IEEE Access, vol. 13, pp. 22820–22830, 2025.
  • B. F. Yalçın, and M. Yilmaz, “Investigation of the performance of bio-oils from three different agricultural wastes as rejuvenators for recycled asphalt,” Turkish J. Civ. Eng., vol. 35, no. 3, pp. 95–123, May 2024.
  • B. F. Yalçın, M. Yilmaz, and H. S. Altundogan, “Experimental investigation of rejuvenated asphalt mixtures using bio-oils from different biomass sources,” Bitlis Eren Univ. Fen Bilim. Derg., vol. 13, no. 4, pp. 988–998, Dec. 2024.
  • Ö. Karadağ, and M. Saltan, “Investigation of adhesion and stripping properties of asphalt modified with bio-oil,” Fırat Univ. J. Exp. Comput. Eng., vol. 4, no. 2, pp. 262–275, Jun. 2025.
  • E. Yalçın, “Investigation of rheological properties of aged polymer modified binders,” J. Innov. Sci. Eng., Apr. 2024.
  • H. Aydin, E. Yalçin, M. Yilmaz, and T. Alataş, “Investigation of physical and rheological properties of Elvaloy and polyphosphoric acid modified bitumen,” Afyon Kocatepe Univ. J. Sci. Eng., vol. 22, no. 5, pp. 1122–1128, Oct. 2022.
There are 24 citations in total.

Details

Primary Language English
Subjects Transportation Engineering
Journal Section Research Articles
Authors

Beyza Furtana Yalçın 0000-0003-4565-7324

Publication Date October 20, 2025
Submission Date August 15, 2025
Acceptance Date September 18, 2025
Published in Issue Year 2025 Volume: 4 Issue: 3

Cite

APA Yalçın, B. F. (2025). Regional Variations in the Physical and Rheological Properties of Asphalt Binders Recovered from Earthquake-Damaged Roads Following the February 6, 2023 Türkiye Earthquakes. Firat University Journal of Experimental and Computational Engineering, 4(3), 657-669. https://doi.org/10.62520/fujece.1766725
AMA Yalçın BF. Regional Variations in the Physical and Rheological Properties of Asphalt Binders Recovered from Earthquake-Damaged Roads Following the February 6, 2023 Türkiye Earthquakes. FUJECE. October 2025;4(3):657-669. doi:10.62520/fujece.1766725
Chicago Yalçın, Beyza Furtana. “Regional Variations in the Physical and Rheological Properties of Asphalt Binders Recovered from Earthquake-Damaged Roads Following the February 6, 2023 Türkiye Earthquakes”. Firat University Journal of Experimental and Computational Engineering 4, no. 3 (October 2025): 657-69. https://doi.org/10.62520/fujece.1766725.
EndNote Yalçın BF (October 1, 2025) Regional Variations in the Physical and Rheological Properties of Asphalt Binders Recovered from Earthquake-Damaged Roads Following the February 6, 2023 Türkiye Earthquakes. Firat University Journal of Experimental and Computational Engineering 4 3 657–669.
IEEE B. F. Yalçın, “Regional Variations in the Physical and Rheological Properties of Asphalt Binders Recovered from Earthquake-Damaged Roads Following the February 6, 2023 Türkiye Earthquakes”, FUJECE, vol. 4, no. 3, pp. 657–669, 2025, doi: 10.62520/fujece.1766725.
ISNAD Yalçın, Beyza Furtana. “Regional Variations in the Physical and Rheological Properties of Asphalt Binders Recovered from Earthquake-Damaged Roads Following the February 6, 2023 Türkiye Earthquakes”. Firat University Journal of Experimental and Computational Engineering 4/3 (October2025), 657-669. https://doi.org/10.62520/fujece.1766725.
JAMA Yalçın BF. Regional Variations in the Physical and Rheological Properties of Asphalt Binders Recovered from Earthquake-Damaged Roads Following the February 6, 2023 Türkiye Earthquakes. FUJECE. 2025;4:657–669.
MLA Yalçın, Beyza Furtana. “Regional Variations in the Physical and Rheological Properties of Asphalt Binders Recovered from Earthquake-Damaged Roads Following the February 6, 2023 Türkiye Earthquakes”. Firat University Journal of Experimental and Computational Engineering, vol. 4, no. 3, 2025, pp. 657-69, doi:10.62520/fujece.1766725.
Vancouver Yalçın BF. Regional Variations in the Physical and Rheological Properties of Asphalt Binders Recovered from Earthquake-Damaged Roads Following the February 6, 2023 Türkiye Earthquakes. FUJECE. 2025;4(3):657-69.