Road Defect Assessment Algorithm on Flexible Pavement

Authors

Ida Ayu Ari Angreni , Diyanti

DOI:

10.29303/jppipa.v11i3.10471

Published:

2025-03-31

Issue:

Vol. 11 No. 3 (2025): March

Keywords:

Damage value algorithm, Road damage, Road damage value, Visual inspection

Research Articles

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Angreni, I. A. A., & Diyanti, D. (2025). Road Defect Assessment Algorithm on Flexible Pavement. Jurnal Penelitian Pendidikan IPA, 11(3), 1123–1130. https://doi.org/10.29303/jppipa.v11i3.10471
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Abstract

This study aims to examine the condition of road pavement mechanically which requires large, time-consuming, impractical, and can only identify one type of road damage. The development of digital technology, then identifying the type of damage can be done with an algorithm or method to detect and analyze the type of road damage quickly and accurately. The purpose of the study is to identify the value of road damage with the visual method of Dirgolaksono and Mochtar, create a model of a road damage assessment algorithm based on digital imagery, and apply the digital image method to the road section being reviewed. The research method with the initial step of the algorithm process is taking pictures using a type of digital camera, so that a digital image is produced which is then processed using Matlab R2016a. The results obtained are the classification of road damage and the damage value of the road section obtained by visual road damage and digital imagery accurately. Validation is carried out with a strong correlation between visual and digital damage, which means that there is no difference between the visual damage value and the digital image damage value

References

Albalate, D., & Fageda, X. (2019). Congestion, Road Safety, and the Effectiveness of Public Policies in Urban Areas. Sustainability, 11(18), 5092. https://doi.org/10.3390/su11185092

Azouz, Z., Asli, B. H. S., & Khan, M. (2023). Evolution of Crack Analysis in Structures Using Image Processing Technique: A Review. Electronics, 12(18), 3862. https://doi.org/10.3390/electronics12183862

Chen, X., Li, S., Liang, Y., Wang, S., Yan, L., & Du, S. (2024). Fatigue Experiment and Failure Mechanism Analysis of Aircraft Titanium Alloy Wing–Body Connection Joint. Sensors, 25(1), 150. https://doi.org/10.3390/s25010150

Deng, L., Zhang, A., Guo, J., & Liu, Y. (2023). An Integrated Method for Road Crack Segmentation and Surface Feature Quantification under Complex Backgrounds. Remote Sensing, 15(6), 1530. https://doi.org/10.3390/rs15061530

Ding, Q.-K., & Liang, H.-E. (2024). Digital Restoration and Reconstruction of Heritage Clothing: A Review. Heritage Science, 12(1), 225. https://doi.org/10.1186/s40494-024-01349-4

Dobosz, B., Gozdowski, D., Koronczok, J., Žukovskis, J., & Wójcik-Gront, E. (2025). Detection of Crop Damage in Maize Using Red–Green–Blue Imagery and LiDAR Data Acquired Using an Unmanned Aerial Vehicle. Agronomy, 15(1), 238. https://doi.org/10.3390/agronomy15010238

Doyle, A. (2023). Disturbing the Teacher’s Role as Assessor: The Case of Calculated Grades 2020–2021 in Ireland. Irish Educational Studies, 1–24. https://doi.org/10.1080/03323315.2023.2236597

Du, Z., Deng, M., Lyu, N., & Wang, Y. (2023). A Review of Road Safety Evaluation Methods Based on Driving Behavior. Journal of Traffic and Transportation Engineering (English Edition), 10(5), 743–761. https://doi.org/10.1016/j.jtte.2023.07.005

Ercetin, A., Der, O., Akkoyun, F., Chandrashekarappa, M. P. G., Şener, R., Çalışan, M., Olgun, N., Chate, G., & Bharath, K. N. (2024). Review of Image Processing Methods for Surface and Tool Condition Assessments in Machining. Journal of Manufacturing and Materials Processing, 8(6), 244. https://doi.org/10.3390/jmmp8060244

Ghazali, M. F. H. M., Hasan, M. R. M., Seman, A. A., Dorett, D. D., Mukhtar, N., & Jaya, R. P. (2021). A Review of Asphaltic Crack Healing Approaches and Its Mechanism. Advances in Materials Science and Engineering, 2021(1), 1093224. https://doi.org/10.1155/2021/1093224

Husein, A. M., Calvin, C., Halim, D., Leo, R., & William. (2019). Motion Detect Application with Frame Difference Method on a Surveillance Camera. Journal of Physics: Conference Series, 1230(1), 012017. https://doi.org/10.1088/1742-6596/1230/1/012017

Ivanova, S., Kuznetsov, A., Zverev, R., & Rada, A. (2023). Artificial Intelligence Methods for the Construction and Management of Buildings. Sensors, 23(21), 8740. https://doi.org/10.3390/s23218740

Kilani, M. A., & Kobziev, V. (2016). An Overview of Research Methodology in Information System (IS). OALib, 03(11), 1–9. https://doi.org/10.4236/oalib.1103126

Kim, H., Park, S., & Jeong, S.-Y. (2023). Machine Learning-Based Image Processing for Ice Concentration During Chukchi and Beaufort Sea Trials. Journal of Marine Science and Engineering, 11(12), 2281. https://doi.org/10.3390/jmse11122281

Krishna, N., & Nagamani, K. (2022). Understanding and Visualization of Different Feature Extraction Processes in Glaucoma Detection. Journal of Physics: Conference Series, 2327(1), 012023. https://doi.org/10.1088/1742-6596/2327/1/012023

Kumar, D., Pandey, R. C., & Mishra, A. K. (2024). A Review of Image Features Extraction Techniques and Their Applications in Image Forensic. Multimedia Tools and Applications, 83(40), 87801–87902. https://doi.org/10.1007/s11042-023-17950-x

Li, N., Pan, X., Yang, L., Huang, Z., Wu, Z., & Zheng, G. (2022). Adaptive CFAR Method for SAR Ship Detection Using Intensity and Texture Feature Fusion Attention Contrast Mechanism. Sensors, 22(21), 8116. https://doi.org/10.3390/s22218116

Liu, G., Wang, S., Wang, D., Yang, Z., & Zan, Y. (2024). Characteristics of Deformation and Damage and Acoustic Properties of Sandstone in Circular Tunnel Morphology under Varying Inundation Depths. Water, 16(20), 2938. https://doi.org/10.3390/w16202938

Liu, K., Li, J., & Bukhari, S. S. H. (2022). Overview of Image Inpainting and Forensic Technology. Security and Communication Networks, 2022, 1–27. https://doi.org/10.1155/2022/9291971

Mardanirad, S., Wood, D. A., & Zakeri, H. (2021). The Application of Deep Learning Algorithms to Classify Subsurface Drilling Lost Circulation Severity in Large Oil Field Datasets. SN Applied Sciences, 3(9), 785. https://doi.org/10.1007/s42452-021-04769-0

Masri, Y., Siahaya, V. T. C., & Istia, P. T. (2023). Pengaruh Penambahan Aspal Terhadap Stabilitas Marshall pada Material Reclaimed Asphalt Pavement (RAP) di Ruas Jalan Jenderal Sudirmal–Rijali Kota Ambon. Jurnal Indonesia Sosial Teknologi, 4(5), 543–555. https://doi.org/10.59141/jist.v4i5.614

Ningrum, M. H., Fahmi, S., & Laksmita, A. N. (2024). Analysis of the Environmental Damage and Agricultural Utilization in Forest Area of Dieang Plateau, Central Java, Indonesia. IOP Conference Series: Earth and Environmental Science, 1315(1), 012049. https://doi.org/10.1088/1755-1315/1315/1/012049

Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic Analysis: Striving to Meet the Trustworthiness Criteria. International Journal of Qualitative Methods, 16(1), 1609406917733847. https://doi.org/10.1177/1609406917733847

Putra, B. H. R., Yogi, M. R. A., Elianora, E., & Prakasa, R. R. (2022). Penentuan Pekerjaan Preservasi Jalan Nasional Pematang Reba-Rengat Berdasarkan Umur Sisa Perkerasan. Konstruksia, 14(1), 8. https://doi.org/10.24853/jk.14.1.8-17

Radoux, J., & Bogaert, P. (2017). Good Practices for Object-Based Accuracy Assessment. Remote Sensing, 9(7), 646. https://doi.org/10.3390/rs9070646

Rodriguez-Segura, D., & Schueler, B. E. (2023). Assessors Influence Results: Evidence on Enumerator Effects and Educational Impact Evaluations. Journal of Development Economics, 163, 103057. https://doi.org/10.1016/j.jdeveco.2023.103057

Rusek, J., Chomacki, L., & Słowik, L. (2025). Bayesian Predictive System for Assessing the Damage Intensity of Residential Masonry Buildings under the Impact of Continuous Ground Deformation. Scientific Reports, 15(1), 1526. https://doi.org/10.1038/s41598-024-82038-x

Seo, J., Chae, S., Shim, J., Kim, D., Cheong, C., & Han, T.-D. (2016). Fast Contour-Tracing Algorithm Based on a Pixel-Following Method for Image Sensors. Sensors, 16(3), 353. https://doi.org/10.3390/s16030353

Sun, W., Wang, Y., Hu, B., & Wang, Q. (2024). Exploration of Eye Fatigue Detection Features and Algorithm Based on Eye-Tracking Signal. Electronics, 13(10), 1798. https://doi.org/10.3390/electronics13101798

Sun, X., Ilanko, S., Mochida, Y., & Tighe, R. C. (2023). A Review on Vibration-Based Damage Detection Methods for Civil Structures. Vibration, 6(4), 843–875. https://doi.org/10.3390/vibration6040051

Talaghat, M. A., Golroo, A., Kharbouch, A., Rasti, M., Heikkilä, R., & Jurva, R. (2024). Digital Twin Technology for Road Pavement. Automation in Construction, 168, 105826. https://doi.org/10.1016/j.autcon.2024.105826

Vlašić, A., Srbić, M., Skokandić, D., & Ivanković, A. M. (2022). Post-Earthquake Rapid Damage Assessment of Road Bridges in Glina County. Buildings, 12(1), 42. https://doi.org/10.3390/buildings12010042

Wang, J., Qin, Q., Zhao, J., Ye, X., Feng, X., Qin, X., & Yang, X. (2015). Knowledge-Based Detection and Assessment of Damaged Roads Using Post-Disaster High-Resolution Remote Sensing Image. Remote Sensing, 7(4), 4948–4967. https://doi.org/10.3390/rs70404948

Wang, X., Wang, X., Li, J., Liang, W., & Bi, C. (2024). Research on Pavement Crack Detection Based on Random Structure Forest and Density Clustering. Automation, 5(4), 467–483. https://doi.org/10.3390/automation5040027

Wu, L., Xiao, G., Huang, D., Zhang, X., Ye, D., & Weng, H. (2025). Edge Computing-Based Machine Vision for Non-Invasive and Rapid Soft Sensing of Mushroom Liquid Strain Biomass. Agronomy, 15(1), 242. https://doi.org/10.3390/agronomy15010242

Wu, S., & Fu, F. (2023). Crack Control Optimization of Basement Concrete Structures Using the Mask-RCNN and Temperature Effect Analysis. PLOS ONE, 18(10), e0292437. https://doi.org/10.1371/journal.pone.0292437

Yu, Y., Wang, C., Fu, Q., Kou, R., Huang, F., Yang, B., Yang, T., & Gao, M. (2023). Techniques and Challenges of Image Segmentation: A Review. Electronics, 12(5), 1199. https://doi.org/10.3390/electronics12051199

Yuan, Q., Shi, Y., & Li, M. (2024). A Review of Computer Vision-Based Crack Detection Methods in Civil Infrastructure: Progress and Challenges. Remote Sensing, 16(16), 2910. https://doi.org/10.3390/rs16162910

Zhang, H., Wu, Z., Qiu, Y., Zhai, X., Wang, Z., Xu, P., Liu, Z., Li, X., & Jiang, N. (2022). A New Road Damage Detection Baseline with Attention Learning. Applied Sciences, 12(15), 7594. https://doi.org/10.3390/app12157594

Zulfachmi, Z., Fatahillah, M. R., & Silalahi, S. B. (2024). Analisis Keretakan Permukaan Jalan Menggunakan Fitur Thresholding, Median Filter, dan Teknik Morfologi. Jurnal Bangkit Indonesia, 13(1), 33–39. https://doi.org/10.52771/bangkitindonesia.v13i1.277

Author Biographies

Ida Ayu Ari Angreni, Universitas Gunadarma

Diyanti, Universitas Gunadarma

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