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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
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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
ARK: ark:/57805/SII.2025.494563
Received: 26 February 2025, Accepted: 01 April 2025, Published: 17 April 2025  
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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.
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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
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TY  - JOUR
AU  - Ujong, Jesam Abam
AU  - Onyelowe, Fortune K. C.
AU  - Emmanuel, Ekeoma
AU  - Vishnupriyan, M.
AU  - Ibe, Kizito C.
AU  - Ugorji, Benjamin
AU  - Nwa-David, Chidobere
AU  - Ihenna, Light
AU  - Obimba-Wogu, Jesuborn
PY  - 2025
DA  - 2025/04/17
TI  - Development of an Adaptive Neuro-fuzzy Inference System (ANFIS) for Predicting Pavement Deterioration
JO  - Sustainable Intelligent Infrastructure
T2  - Sustainable Intelligent Infrastructure
JF  - Sustainable Intelligent Infrastructure
VL  - 1
IS  - 1
SP  - 39
EP  - 51
DO  - 10.62762/SII.2025.494563
UR  - https://www.icck.org/article/abs/SII.2025.494563
KW  - pavement deterioration
KW  - adaptive neuro-fuzzy inference system (ANFIS)
KW  - pavement maintenance
KW  - pavement management
AB  - 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.
SN  - 3067-8137
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
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@article{Ujong2025Developmen,
  author = {Jesam Abam Ujong and Fortune K. C. Onyelowe and Ekeoma Emmanuel and M. Vishnupriyan and Kizito C. Ibe and Benjamin Ugorji and Chidobere Nwa-David and Light Ihenna and Jesuborn Obimba-Wogu},
  title = {Development of an Adaptive Neuro-fuzzy Inference System (ANFIS) for Predicting Pavement Deterioration},
  journal = {Sustainable Intelligent Infrastructure},
  year = {2025},
  volume = {1},
  number = {1},
  pages = {39-51},
  doi = {10.62762/SII.2025.494563},
  url = {https://www.icck.org/article/abs/SII.2025.494563},
  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.},
  keywords = {pavement deterioration, adaptive neuro-fuzzy inference system (ANFIS), pavement maintenance, pavement management},
  issn = {3067-8137},
  publisher = {Institute of Central Computation and Knowledge}
}
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