Volume 25 , Issue 3 , PP: 194-205, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Imène Issaoui 1 , Afef Selmi 2
Doi: https://doi.org/10.54216/IJNS.250318
In decision-making, NS permits the representation of information with three membership functions: indeterminacy (I), false (F), and truth (T). All components in an NS have indeterminacy, non-, and membership degrees that are autonomous and vary from (0-1). This generates NS particularly appropriate in composite decision-making situations where information is incomplete, ambiguous, or contradictory, which allows strong and more complex solutions and analysis. Detecting road damage accurately and quickly enables the capability of road maintenance agencies to generate timely maintenance to road surfaces, retain optimum road conditions, enhance the safety of transportation, and reduce transportation charges. Research on road damage detection using AI models achieved more attention at present, particularly in smart cities. This paper develops a Boosting Road Damage Detection using DEMATEL with Bipolar Neutrosophic Dombi and Siberian Tiger Optimization (BRDD-DBNDSTO) algorithm. The presented BRDD-DBNDSTO technique is mainly intended to improve the accuracy and reliability of road damage classification for intelligent smart city infrastructure. To accomplish this, the BRDD-DBNDSTO technique employs adaptive bilateral filtering (ABF) using image preprocessing to effectively enhance image quality by reducing noise. Then, the SqueezeNet method was used to create a collection of feature vectors. For the classification and detection of road damage, the DEMATEL with bipolar neutrosophic Dombi model is exploited. At last, the Siberian tiger optimization (STO) algorithm is used to adjust the parameters related to the classifier model. To guarantee the improved performance of the BRDD-DBNDSTO method, an extensive experimental study was carried out and the gained outcomes illustrate the improvement of the BRDD-DBNDSTO model across the existing techniques.
Bipolar Neutrosophic Set , DEMATEL , Bipolar Neutrosophic Dombi , Road Damage Detection , Siberian Tiger Optimization
[1] Chinnadurai, V. and Sindhu, M.P., 2020. An introduction to neutro-fine topology with separation axioms and decision making. International Journal of Neutrosophic Science (IJNS) Volume 12, 2020, p.11.
[2] Chinnadurai, V. and Sindhu, M.P., 2020. An introduction to neutro-fine topology with separation axioms and decision making. International Journal of Neutrosophic Science (IJNS) Volume 12, 2020, p.11.
[3] Edalatpanah, S.A., 2020. A direct model for triangular neutrosophic linear programming. International journal of neutrosophic science, 1(1), pp.19-28.
[4] Dhanalakshmi, G., Sandhiya, S. and Smarandache, F., 2024. Selection of the best process for desalination under a Treesoft set environment using the multi-criteria decision-making method. International Journal of Neutrosophic Science, 23(3), pp.140-40.
[5] Almuhur, E., Miqdad, H., Al-labadi, M. and Idrisi, M.I., 2024. μ-L-Closed Subsets of Noetherian Generalized Topological Spaces. International Journal of Neutrosophic Science, 23(3), pp.148-48.
[6] B. Abu-Salih, P. Wongthongtham, K. Coutinho, R. Qaddoura, O. Alshaweesh, and M. Wedyan, ‘‘The development of a road network flood risk detection model using optimised ensemble learning,’’ Eng. Appl. Artif. Intell., vol. 122, Jun. 2023, Art. no. 106081.
[7] M. Al Duhayyim, A. A. Malibari, A. Alharbi, K. Afef, A. Yafoz, R. Alsini, O. Alghushairy, and H. Mohsen, ‘‘Road damage detection using the hunger games search with Elman neural network on high-resolution remote sensing images,’’ Remote Sens., vol. 14, no. 24, p. 6222, Dec. 2022.
[8] K. Zhao, J. Liu, Q. Wang, X. Wu, and J. Tu, ‘‘Road damage detection from post-disaster high-resolution remote sensing images based on TLD framework,’’ IEEE Access, vol. 10, pp. 43552–43561, 2022.
[9] G. Ochoa-Ruiz, A. A. Angulo-Murillo, A. Ochoa-Zezzatti, L. M. Aguilar-Lobo, J. A. Vega-Fernández, and S. Natraj, ‘‘An asphalt damage dataset and detection system based on RetinaNet for road conditions assessment,’’ Appl. Sci., vol. 10, no. 11, p. 3974, Jun. 2020.
[10] K. Hacıefendioğlu and H. B. Başağa, ‘‘Concrete road crack detection using deep learning-based faster R-CNN method,’’ Iranian J. Sci. Technol., Trans. Civil Eng., vol. 46, no. 2, pp. 1621–1633, Apr. 2022.
[11] Merolla, D., Latorre, V., Salis, A. and Boanelli, G., 2024. Automated Road Safety: Enhancing Sign and Surface Damage Detection with AI. arXiv preprint arXiv:2407.15406.
[12] Chu, H.H., Saeed, M.R., Rashid, J., Mehmood, M.T., Ahmad, I., Iqbal, R.S. and Ali, G., 2023. Deep learning method to detect the road cracks and potholes for smart cities. Comput Mater Contin, 75(1), pp.1863-1881.
[13] Adewopo, V.A. and Elsayed, N., 2024. Smart city transportation: Deep learning ensemble approach for traffic accident detection. IEEE Access.
[14] Silva, L.A., Leithardt, V.R.Q., Batista, V.F.L., González, G.V. and Santana, J.F.D.P., 2023. Automated road damage detection using UAV images and deep learning techniques. IEEE Access, 11, pp.62918-62931.
[15] Jagatheesaperumal, S.K., Bibri, S.E., Huang, J., Rajapandian, J. and Parthiban, B., 2024. Artificial intelligence of things for smart cities: advanced solutions for enhancing transportation safety. Computational Urban Science, 4(1), p.10.
[16] Chen, C., Yao, G., Liu, L., Pei, Q., Song, H. and Dustdar, S., 2023. A cooperative vehicle-infrastructure system for road hazards detection with edge intelligence. IEEE Transactions on Intelligent Transportation Systems, 24(5), pp.5186-5198.
[17] Gollamandala, U.B., Midasala, V. and Ratna, V.R., 2022. FPGA implementation of hybrid recursive reversable box filter-based fast adaptive bilateral filter for image denoising. Microprocessors and Microsystems, 90, p.104520.
[18] Chahil, S.T.H., Zakwan, M., Khan, K. and Fazil, A., 2024. Performance analysis of different signal representations and optimizers for CNN based automatic modulation classification.
[19] Yaacob, S.N., Hashim, H., Sulaiman, N.H., Awang, N.A., Al-Quran, A. and Abdullah, L., 2025. An Integrated DEMATEL with Bipolar neutrsophic Dombi-based Heronian Mean Operator and Its Applications in Decision-making Problem. International Journal of Neutrosophic Science, (1), pp.08-8.
[20] Lv, Y., Shen, Y., Zhang, A., Ren, L., Xie, J., Zhang, Z., Zhang, Z., An, L., Sun, J., Yan, Z. and Mi, O., 2024. Rock dynamic strength prediction in cold regions using optimized hybrid algorithmic models. Geomechanics and Geophysics for Geo-Energy and Geo-Resources, 10(1), pp.1-29.
[21] Alamgeer, M., Alkahtani, H.K., Maashi, M., Othman, M., Hilal, A.M., Alsaid, M.I., Osman, A.E. and Alneil, A.A., 2023. Optimal fuzzy wavelet neural network based road damage detection. IEEE Access, 11, pp.61986-61994.