Sustainable Intelligent Infrastructure Home About Editors Current Issue
-
CiteScore
0.80
Impact Factor
  1. Home
  2. Journals
  3. Sustainable Intelligent Infrastructure
  4. Volume 1, Issue 1
Development of an Adaptive Neuro-fuzzy Inference System (ANFIS) for Predicting Pavement Deterioration
Research Article | Published: 17 April 2025
Sustainable Intelligent Infrastructure Volume 1, Issue 1, 2025: 39-51
Volume 1, Issue 1, Sustainable Intelligent Infrastructure
Volume 1, Issue 1, 2025
Submit Manuscript Edit a Special Issue
Academic Editor
Nakkeeran Ganasen
Nakkeeran Ganasen
Madanapalle Institute of Technology & Science, India
Article QR Code
Article QR Code
Scan the QR code for reading
Popular articles
Research on A Ship Trajectory Classification Method Based on Deep Learning YOLOv7-Bw: A Dense Small Object Efficient Detector Based on Remote Sensing Image A Mimic Fusion Algorithm for Dual Channel Video Based on Possibility Distribution Synthesis Theory Bridging Modalities: A Survey of Cross-Modal Image-Text Retrieval Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data Visual Feature Extraction and Tracking Method Based on Corner Flow Detection Inaugural Editorial of the Chinese Journal of Information Fusion Simultaneous Spatiotemporal Bias Compensation and Data Fusion for Asynchronous Multisensor Systems YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems Extraction of Motion Information from Occupancy Grid Map Using Keystone Transform
Sustainable Intelligent Infrastructure, Volume 1, Issue 1, 2025: 39-51

Open Access | Research Article | 17 April 2025
Development of an Adaptive Neuro-fuzzy Inference System (ANFIS) for Predicting Pavement Deterioration
by
Jesam Abam Ujong Jesam Abam Ujong 1
Fortune K. C. Onyelowe Fortune K. C. Onyelowe 2 *
Ekeoma Emmanuel Ekeoma Emmanuel 3
M. Vishnupriyan M. Vishnupriyan 4
Kizito C. Ibe Kizito C. Ibe 5
Benjamin Ugorji Benjamin Ugorji 5
Chidobere Nwa-David Chidobere Nwa-David 5
Light Ihenna Light Ihenna 6
Jesuborn Obimba-Wogu Jesuborn Obimba-Wogu 7
1 Department of Civil Engineering, Veritas University, Bwari Campus, Abuja, Nigeria
2 Department of Computer Engineering, Michael Okpara University of Agriculture, Umudike 440109, Nigeria
3 Department of Civil Engineering, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom
4 Department of Civil Engineering, SR University, Warangal 506371, Telangana, India
5 Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike 440109, Nigeria
6 Department of Environmental Engineering, Technical University of Munich, 80333 München, Germany
7 Department of Civil Engineering, Université Grenoble Alpes, 38400 Saint-Martin-d'Hères, France
* Corresponding Author: Fortune K. C. Onyelowe, [email protected]
DOI: 10.62762/SII.2025.494563
Received: 26 February 2025, Accepted: 01 April 2025, Published: 17 April 2025  
   PDF  (2.01 MB) Full-Text HTML XML
Article Metrics Cite This Article
Abstract
Pavement maintenance is a critical aspect of transportation and infrastructure management, as it directly impacts traffic flow, vehicle maintenance, safety and accident rate. Effective prediction and prevention of pavement deterioration are essential for optimizing pavement maintenance strategies, reducing cost, and ensuring the lifespan or longevity of transportation. This study presents the development of Adaptive Neuro-Fuzzy inference system (ANFIS) for predicting pavement deterioration. The data used for this analysis is a historical data and field investigation data from the Cross River State pavement Maintenance Agency, Calabar, Nigeria. The ANFIS model was trained using a dataset with input variables, the outcome from the membership functions shows that in1=4.4, in2=3.15, in3=13.5 and output variable (out1=0.541). The model consists of 270 nodes, 33 fuzzy set, and utilizes sub clustering (FIS type), hybrid optimization method, Gaussmf membership function, and wtaver defuzzification. The study result showed that the ANFIS model accurately predicted pavement deterioration with Root mean Squared Error (RMSE) 0f 0.851716. The developed ANFIS model can be used by transportation agencies to predict pavement deterioration and prioritize maintenance activities. The Model’s ability to handle nonlinear relationships and uncertainty makes it a valuable tool for pavement management.
Graphical Abstract
Development of an Adaptive Neuro-fuzzy Inference System (ANFIS) for Predicting Pavement Deterioration
Keywords
pavement deterioration
adaptive neuro-fuzzy inference system (ANFIS)
pavement maintenance
pavement management
Data Availability Statement
Data will be made available on request.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
References
  1. Huang, Y. H. (2004). Pavement Analysis and Design. Pearson.
    [Google Scholar]
  2. Beskou, N. D., & Muho, E. V. (2023). Review on dynamic response of pavement pavements to moving vehicle loads; part 2: Flexible pavements. Soil Dynamics and Earthquake Engineering, 175, 108248.
    [CrossRef]   [Google Scholar]
  3. Hasan, M. A., Jrew, B. K., Abed, F. H., & Msallam, M. S. (2020, July). Developing a pavement maintenance management system of multi-lane highway in Iraq. In IOP Conference Series: Materials Science and Engineering (Vol. 881, No. 1, p. 012171). IOP Publishing.
    [CrossRef]   [Google Scholar]
  4. Wang, K., Ma, C., Qiao, Y., Lu, X., Hao, W., & Dong, S. (2021). A hybrid deep learning model with 1DCNN-LSTM-Attention networks for short-term traffic flow prediction. Physica A: Statistical Mechanics and its Applications, 583, 126293.
    [CrossRef]   [Google Scholar]
  5. Tedjopurnomo, D. A., Bao, Z., Zheng, B., Choudhury, F. M., & Qin, A. K. (2020). A survey on modern deep neural network for traffic prediction: Trends, methods and challenges. IEEE Transactions on Knowledge and Data Engineering, 34(4), 1544-1561.
    [CrossRef]   [Google Scholar]
  6. Emmanuel, U. O., Ogbonnaya, I., & Uche, U. B. (2021). An investigation into the cause of pavement failure along Sagamu-Papalanto highway southwestern Nigeria. Geoenvironmental Disasters, 8, 1-19.
    [CrossRef]   [Google Scholar]
  7. Kale, Ö. A., & Baradan, S. (2020). Identifying factors that contribute to severity of construction injuries using logistic regression model. Teknik Dergi, 31(2), 9919-9940.
    [CrossRef]   [Google Scholar]
  8. Abdulazeez, R. (2024). A review of pavement pavement failure: A case study of Nigerian pavement. Advanced Journal of Science, Technology and Engineering, 4(2), 8-23.
    [CrossRef]   [Google Scholar]
  9. Xu, G., Bai, L., & Sun, Z. (2014). Pavement Deterioration Modeling and Prediction for Kentucky Interstate and Highways. In IIE Annual Conference. Proceedings (p. 993). Institute of Industrial and Systems Engineers (IISE).
    [Google Scholar]
  10. Onyelowe, K. C., Shakeri, J., Amini-Khoshalann, H., Salahudeen, A. B., Arinze, E. E., & Ugwu, H. U. (2021). Application of ANFIS hybrids to predict coefficients of curvature and uniformity of treated unsaturated lateritic soil for sustainable earthworks. Cleaner Materials, 1, 100005.
    [CrossRef]   [Google Scholar]
  11. Aldabbas, L. J. (2023). Empirical Models Investigation of Pavement Management for Advancing the pavement’s Planning Using Predictive Maintenance. Civil Engineering and Architecture, 11(3), 1346-1354.
    [CrossRef]   [Google Scholar]
  12. Alaneme, G. U., Mbadike, E. M., Attah, I. C., & Udousoro, I. M. (2022). Mechanical behaviour optimization of saw dust ash and quarry dust concrete using adaptive neuro-fuzzy inference system. Innovative Infrastructure Solutions, 7, 1-16.
    [CrossRef]   [Google Scholar]
  13. Shihabudheen, K. V., Mahesh, M., & Pillai, G. N. (2018). Particle swarm optimization based extreme learning neuro-fuzzy system for regression and classification. Expert Systems with Applications, 92, 474-484.
    [CrossRef]   [Google Scholar]
  14. Thube, D. T. (2012). Artificial neural network (ANN) based pavement deterioration models for low volume pavements in India. International Journal of Pavement Research and Technology, 5(2), 115–120.
    [Google Scholar]
  15. Choi, S., & Do, M. (2018). Prediction of Asphalt Pavement Service Life using Deep Learning. International Journal of Highway Engineering, 20(2), 57-65.
    [CrossRef]   [Google Scholar]
  16. Yun, J. J., Lee, D., Ahn, H., Park, K., & Yigitcanlar, T. (2016). Not deep learning but autonomous learning of open innovation for sustainable artificial intelligence. Sustainability, 8(8), 797.
    [CrossRef]   [Google Scholar]
  17. Ceylan, H., Bayrak, M. B., & Gopalakrishnan, K. (2014). Neural networks applications in pavement engineering: A recent survey. International Journal of Pavement Research and Technology, 7(6), 434–444.
    [CrossRef]   [Google Scholar]
  18. Attoh-Okine, N. O. (1999). Analysis of learning rate and momentum term in backpropagation neural network algorithm trained to predict pavement performance. Advances in engineering software, 30(4), 291-302.
    [CrossRef]   [Google Scholar]
  19. Attoh-Okine, N. O. (1994). Predicting roughness progression in flexible pavements using artificial neural networks. In Transportation research board conference proceedings (Vol. 1, No. 1).
    [Google Scholar]
  20. Choi, J. H., Adams, T. M., & Bahia, H. U. (2004). Pavement roughness modeling using back‐propagation neural networks. Computer‐Aided Civil and Infrastructure Engineering, 19(4), 295-303.
    [CrossRef]   [Google Scholar]
  21. Mazari, M., & Rodriguez, D. D. (2016). Prediction of pavement roughness using a hybrid gene expression programming-neural network technique. Journal of Traffic and Transportation Engineering (English Edition), 3(5), 448-455.
    [CrossRef]   [Google Scholar]
  22. La Torre, F., Domenichini, L., & Darter, M. I. (1998, May). Roughness prediction model based on the artificial neural network approach. In Fourth International Conference on Managing Pavements (Vol. 2).
    [Google Scholar]
  23. Milad, A. B. D. A. L. R. H. M. A. N., Majeed, S. A., Adwan, I. B. R. A. H. I. M., Khalifa, N. A., & Yusoff, N. I. M. (2022). Adaptive neuro fuzzy inference system for predicting flexible pavement distress in tropical regions. Journal of Engineering Science and Technology, 17(1), 1-14.
    [Google Scholar]
  24. Abdelrahim, A. M., & George, K. P. (2000). Artificial neural network for enhancing selection of pavement maintenance strategy. Transportation research record, 1699(1), 16-22.
    [CrossRef]   [Google Scholar]
  25. Abam, U. J., Oduma, O., & Mbadike, M. E. (2024). Analysis of cost control challenges in the construction industry, using Artificial Neural Network. Poljoprivredna tehnika, 49(4), 39-53.
    [CrossRef]   [Google Scholar]
  26. Olayode, I. O., Tartibu, L. K., & Alex, F. J. (2023). Comparative study analysis of ANFIS and ANFIS-GA models on flow of vehicles at pavement Intersections. Applied sciences, 13(2), 744.
    [CrossRef]   [Google Scholar]
  27. Issa, A., Samaneh, H., & Ghanim, M. (2022). Predicting pavement condition index using artificial neural networks approach. Ain Shams Engineering Journal, 13(1), 101490.
    [CrossRef]   [Google Scholar]
  28. Wagale, M., & Singh, A. P. (2019). The application of adaptive neuro-fuzzy inference system and fuzzy Delphi technique to assess socio-economic impacts of construction of rural pavements. Transport and Telecommunication, 20(4), 325-345. https://DOI.org10.2478/ttj-2019-0027
    [Google Scholar]
  29. Gharieb, M., Nishikawa, T., Nakamura, S., & Thepvongsa, K. (2022). Application of Adaptive Neuro–Fuzzy Inference System for Forecasting Pavement Roughness in Laos. Coatings, 12(3), 380.
    [CrossRef]   [Google Scholar]
  30. Kırbaş, U., & Karaşahin, M. (2016). Performance models for hot mix asphalt pavements in urban pavements. Construction and Building Materials, 116, 281-288..
    [CrossRef]   [Google Scholar]
  31. Saltan, M., & Terzi, S. (2009). Backcalculation of pavement layer thickness and moduli using adaptive neuro-fuzzy inference system. In Intelligent and Soft Computing in Infrastructure Systems Engineering: Recent Advances (pp. 177-204). Berlin, Heidelberg: Springer Berlin Heidelberg.
    [CrossRef]   [Google Scholar]
  32. Cano-Ortiz, S., Pascual-Muñoz, P., & Castro-Fresno, D. (2022). Machine learning algorithms for monitoring pavement performance. Automation in Construction, 139, 104309.
    [CrossRef]   [Google Scholar]
  33. Sholevar, N., Golroo, A., & Esfahani, S. R. (2022). Machine learning techniques for pavement condition evaluation. Automation in Construction, 136, 104190.
    [CrossRef]   [Google Scholar]
  34. Sayed, T., Tavakolie, A., & Razavi, A. (2003). Comparison of adaptive network based fuzzy inference systems and B-spline neuro-fuzzy mode choice models. Journal of computing in civil engineering, 17(2), 123-130.
    [CrossRef]   [Google Scholar]
  35. Alaneme, G. U., Mbadike, E. M., Iro, U. I., Udousoro, I. M., & Ifejimalu, W. C. (2021). Adaptive neuro-fuzzy inference system prediction model for the mechanical behaviour of rice husk ash and periwinkle shell concrete blend for sustainable construction. Asian Journal of Civil Engineering, 22, 959-974.
    [CrossRef]   [Google Scholar]
  36. Mc Duling, J. J., Cloete, C. E., & Horak, E. (2006). The application of Neuro-Fuzzy methodology to maintenance of buildings.
    [Google Scholar]
  37. Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
    [CrossRef]   [Google Scholar]
  38. Fernandes, G., Rodrigues, J. J., Carvalho, L. F., Al-Muhtadi, J. F., & Proença, M. L. (2019). A comprehensive survey on network anomaly detection. Telecommunication Systems, 70, 447-489. Fernandes, G., Rodrigues, J. J., Carvalho, L. F., Al-Muhtadi, J. F., & Proença, M. L. (2019). A comprehensive survey on network anomaly detection. Telecommunication Systems, 70, 447-489.
    [Google Scholar]
  39. Hasan, M. F., & Sobhan, M. A. (2020). Describing fuzzy membership function and detecting the outlier by using five number summary of data. American Journal of Computational Mathematics, 10(03), 410.
    [CrossRef]   [Google Scholar]
  40. Gollapalli, M., Musleh, D., Ibrahim, N., Khan, M. A., Abbas, S., Atta, A., ... & Omer, A. (2022). A Neuro-Fuzzy Approach to pavement Traffic Congestion Prediction. Computers, Materials and Continua, 73(1), 295-310.
    [CrossRef]   [Google Scholar]
  41. Obianyo, J. I., Udeala, R. C., & Alaneme, G. U. (2023). Application of neural networks and neuro-fuzzy models in construction scheduling. Scientific Reports, 13(1), 8199.
    [CrossRef]   [Google Scholar]
  42. Afradi, A., & Ebrahimabadi, A. (2021). Prediction of TBM penetration rate using the imperialist competitive algorithm (ICA) and quantum fuzzy logic. Innovative Infrastructure Solutions, 6(2), 103.
    [CrossRef]   [Google Scholar]
  43. Ujong, J. A., Mbadike, E. M., & Alaneme, G. U. (2022). Prediction of cost and duration of building construction using artificial neural network. Asian Journal of Civil Engineering, 23(7), 1117-1139.
    [CrossRef]   [Google Scholar]
  44. Wang, W. C., Bilozerov, T., Dzeng, R. J., Hsiao, F. Y., & Wang, K. C. (2017). Conceptual cost estimations using neuro-fuzzy and multi-factor evaluation methods for building projects. Journal of Civil Engineering and Management, 23(1), 1-14.
    [CrossRef]   [Google Scholar]
  45. Tokede, O., Ahiaga-Dagbui, D., Smith, S., & Wamuziri, S. (2014). Mapping relational efficiency in neuro-fuzzy hybrid cost models. In Construction Research Congress 2014: Construction in a Global Network (pp. 1458-1467).
    [CrossRef]   [Google Scholar]
  46. Alimoradi, A., Pezeshk, S., & Naeim, F. (2004). Identification of input ground motion records for seismic design using neuro-fuzzy pattern recognition and genetic algorithms. In Structures 2004: Building on the Past, Securing the Future (pp. 1-12).
    [CrossRef]   [Google Scholar]
  47. Okonkwo, U. N., Ekeoma, E. C., & Ndem, H. E. (2023). Exponential logarithmic models for strength properties of lateritic soil treated with cement and rice husk ash as pavement of low-cost pavements. International Journal of Pavement Research and Technology, 16(2), 333-342.
    [CrossRef]   [Google Scholar]
  48. Okonkwo, U. N., Ekeoma, E. C., & Eleke, L. O. (2023). Polynomial models for predicting time limits for compaction after mixing operation of lateritic soil reinforced using cement or lime. Journal of Civil Engineering, Science and Technology, 14(1), 26-34.
    [CrossRef]   [Google Scholar]
  49. Isradi, M., Rifai, A. I., Prasetijo, J., Kinasih, R. K., & Setiawan, M. I. (2024). Development of Pavement Deterioration Models Using Markov Chain Process. Civil Engineering Journal, 10(9), 2954-2965.
    [CrossRef]   [Google Scholar]
Cite This Article
APA Style
Ujong, J. A., Onyelowe, F. K. C., Emmanuel, E., Vishnupriyan, M., Ibe, K. C., Ugorji, B., Nwa-David, C., Ihenna, L., & Obimba-Wogu, J. (2025). Development of an Adaptive Neuro-fuzzy Inference System (ANFIS) for Predicting Pavement Deterioration. Sustainable Intelligent Infrastructure, 1(1), 39–51. https://doi.org/10.62762/SII.2025.494563
Article Metrics
Citations:

Google Scholar
0

Crossref

0

Scopus

0

Web of Science

0
Article Access Statistics:
Views: 536
PDF Downloads: 33
Publisher's Note
IECE stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
CC BY Copyright © 2025 by the Author(s). Published by Institute of Emerging and Computer Engineers. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
Sustainable Intelligent Infrastructure

Sustainable Intelligent Infrastructure

ISSN: request pending (Online) | ISSN: request pending (Print)

Email: [email protected]

Portico

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/iece/

Copyright © 2025 Institute of Emerging and Computer Engineers Inc.