Performance evaluation of highway pavement based on particle swarm optimization
In order to improve the scientific and accurate evaluation of expressway pavement performance, we construct a pavement performance evaluation index system based on the measured data from the Jinan section of the Beijing-Shanghai Highway in 2022. A back propagation (BP) neural network pavement perfor...
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| Vydáno v: | Shenzhen da xue xue bao. Li gong ban Ročník 41; číslo 5; s. 619 - 625 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Science Press (China Science Publishing & Media Ltd.)
01.09.2024
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| ISSN: | 1000-2618 |
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| Abstract | In order to improve the scientific and accurate evaluation of expressway pavement performance, we construct a pavement performance evaluation index system based on the measured data from the Jinan section of the Beijing-Shanghai Highway in 2022. A back propagation (BP) neural network pavement performance evaluation algorithm based on particle swarm optimization (PSO), referred to as the PSO-BP algorithm, is proposed. The results show that the PSO-BP algorithm achieves the prediction accuracy of 99.7% on the training set and 99.4% on the test set, which is 19.2% and 19.1% higher than that of the traditional BP neural network, respectively. This indicates that using the particle swarm optimization algorithm to optimize the initial weights and thresholds of the BP neural network can improve the prediction ability and accuracy of the model. The prediction results of the PSO-BP algorithm are highly consistent with the actual evaluation, demonstrating good reliability and stability. The PSO-BP algorithm can accurately evaluate and predict the performance grade of highway asphalt pavement, providing an important basis for highway maintenance decision-making. |
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| AbstractList | In order to improve the scientific and accurate evaluation of expressway pavement performance, we construct a pavement performance evaluation index system based on the measured data from the Jinan section of the Beijing-Shanghai Highway in 2022. A back propagation (BP) neural network pavement performance evaluation algorithm based on particle swarm optimization (PSO), referred to as the PSO-BP algorithm, is proposed. The results show that the PSO-BP algorithm achieves the prediction accuracy of 99.7% on the training set and 99.4% on the test set, which is 19.2% and 19.1% higher than that of the traditional BP neural network, respectively. This indicates that using the particle swarm optimization algorithm to optimize the initial weights and thresholds of the BP neural network can improve the prediction ability and accuracy of the model. The prediction results of the PSO-BP algorithm are highly consistent with the actual evaluation, demonstrating good reliability and stability. The PSO-BP algorithm can accurately evaluate and predict the performance grade of highway asphalt pavement, providing an important basis for highway maintenance decision-making. |
| Author | WAN Yingying LIU Zhaohui CHEN Zheng WANG Lin DUAN Meidong ZHAO Quanman |
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| SubjectTerms | highway neural network particle swarm optimization algorithm pavement maintenance performance evaluation road engineering |
| Title | Performance evaluation of highway pavement based on particle swarm optimization |
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