Enhanced probabilistic prediction of pavement deterioration using Bayesian neural networks and cuckoo search optimization
Abstract The predictive performance of probabilistic pavement condition deterioration is critical for effective maintenance and rehabilitation decisions. Currently, numerous improved models exist, but few rely on probabilistic models to improve pavement deterioration prediction. Therefore, this stud...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-92469-9 |
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| author | Feng Xiao Biying Shi Jie Gao Huapeng Chen Di Yang |
| author_facet | Feng Xiao Biying Shi Jie Gao Huapeng Chen Di Yang |
| author_sort | Feng Xiao |
| collection | DOAJ |
| description | Abstract The predictive performance of probabilistic pavement condition deterioration is critical for effective maintenance and rehabilitation decisions. Currently, numerous improved models exist, but few rely on probabilistic models to improve pavement deterioration prediction. Therefore, this study proposed an improved probabilistic model for pavement deterioration prediction based on the coupling of Bayesian neural network (BNN) and cuckoo search (CS) algorithm. The model prediction performance is evaluated against two metrics: determination coefficient (R2) and standard deviation (stability). Finally, based on the data from the pavement management system in Shanxi Province, it was verified that the CS-BNN model outperforms the genetic algorithm-BNN, particle swarm optimization-BNN, and BNN models in terms of the two metrics. Sensitivity analysis further confirms the robustness of the CS-BNN model. The findings indicate that the CS-BNN model provides more reliable predictions with lower uncertainty, aiding road engineers in optimizing maintenance schedules and costs. |
| format | Article |
| id | doaj-art-ca4c1699e297454ea062cd61592c2b08 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-ca4c1699e297454ea062cd61592c2b082025-08-20T02:56:11ZengNature PortfolioScientific Reports2045-23222025-03-0115111810.1038/s41598-025-92469-9Enhanced probabilistic prediction of pavement deterioration using Bayesian neural networks and cuckoo search optimizationFeng Xiao0Biying Shi1Jie Gao2Huapeng Chen3Di Yang4School of Transportation Engineering, East China JiaoTong UniversitySchool of Architectural and Art, Jiangxi Industry Polytechnic CollegeSchool of Civil Engineering and Architecture, East China JiaoTong UniversitySchool of Transportation Engineering, East China JiaoTong UniversityInstitute of Transportation Development Strategy & Planning of Sichuan ProvinceAbstract The predictive performance of probabilistic pavement condition deterioration is critical for effective maintenance and rehabilitation decisions. Currently, numerous improved models exist, but few rely on probabilistic models to improve pavement deterioration prediction. Therefore, this study proposed an improved probabilistic model for pavement deterioration prediction based on the coupling of Bayesian neural network (BNN) and cuckoo search (CS) algorithm. The model prediction performance is evaluated against two metrics: determination coefficient (R2) and standard deviation (stability). Finally, based on the data from the pavement management system in Shanxi Province, it was verified that the CS-BNN model outperforms the genetic algorithm-BNN, particle swarm optimization-BNN, and BNN models in terms of the two metrics. Sensitivity analysis further confirms the robustness of the CS-BNN model. The findings indicate that the CS-BNN model provides more reliable predictions with lower uncertainty, aiding road engineers in optimizing maintenance schedules and costs.https://doi.org/10.1038/s41598-025-92469-9Pavement management systemsDeteriorationProbabilistic predictionBayesian neural networkCuckoo search algorithm |
| spellingShingle | Feng Xiao Biying Shi Jie Gao Huapeng Chen Di Yang Enhanced probabilistic prediction of pavement deterioration using Bayesian neural networks and cuckoo search optimization Scientific Reports Pavement management systems Deterioration Probabilistic prediction Bayesian neural network Cuckoo search algorithm |
| title | Enhanced probabilistic prediction of pavement deterioration using Bayesian neural networks and cuckoo search optimization |
| title_full | Enhanced probabilistic prediction of pavement deterioration using Bayesian neural networks and cuckoo search optimization |
| title_fullStr | Enhanced probabilistic prediction of pavement deterioration using Bayesian neural networks and cuckoo search optimization |
| title_full_unstemmed | Enhanced probabilistic prediction of pavement deterioration using Bayesian neural networks and cuckoo search optimization |
| title_short | Enhanced probabilistic prediction of pavement deterioration using Bayesian neural networks and cuckoo search optimization |
| title_sort | enhanced probabilistic prediction of pavement deterioration using bayesian neural networks and cuckoo search optimization |
| topic | Pavement management systems Deterioration Probabilistic prediction Bayesian neural network Cuckoo search algorithm |
| url | https://doi.org/10.1038/s41598-025-92469-9 |
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