A Neural Network-Based Structural Parameter Assessment Method for Prefabricated Concrete Pavement
Abstract
1. Introduction
2. JS-BP Neural Network Model Construction
2.1. BP Neural Network
2.2. JS-BP Neural Network
- (1)
- Calculate the new position using the current jellyfish position and the search radius. For the case of exceeding the search space, a boundary processing strategy is adopted to limit the position to the search range;
- (2)
- Calculate the fitness value of the new position and update the global optimal solution;
- (3)
- With each iteration, the search radius is gradually reduced. When the maximum number of iterations is achieved or the termination condition is met, the search process ends; otherwise, return to step 2.
- (1)
- Establish a BP neural network model; determine the network structure, transfer function, and learning rules; and input the database into the model for training.
- (2)
- Optimize the weights and thresholds of the BP neural network using the jellyfish search algorithm. If the training results reach the set parameters, stop the calculation and obtain the optimal network weights and thresholds; otherwise, recalculate.
- (3)
- After obtaining the optimal network weights and thresholds, use the BP neural network model to train and output the results.
2.3. Parameter Selection for JS-BP Neural Network Model
3. Database Construction and Training of the Neural Network
3.1. Finite Element Model
3.2. Database Construction
3.3. Neural Network Training
3.3.1. Evaluation Indexes
3.3.2. Training Results
3.4. Analysis of Influence from Aircraft Loads
3.4.1. Aircraft Load Location
3.4.2. Aircraft Load Size
4. Verification with Experiments of Prefabricated Pavement Models
4.1. Introduction of Experiments
4.1.1. Prefabricated Pavement Panel
4.1.2. Strain Sensor and Its Deployment
4.1.3. Pavement Base and Loading
4.2. Measurement of Structural Parameters
4.3. Inversion Results for Structural Parameters
5. Conclusions
- (1)
- The JS-BP neural network model can be constructed and trained for assessing the key parameters of prefabricated concrete pavement with a database obtained from the FE simulations; namely, the bending and tensile modulus, reaction modulus at the top of the subgrade, and seam equivalent modulus. The prediction accuracy is good enough as the difference between the predicted value and the true value for the three parameters is smaller than 1%.
- (2)
- The aircraft loads show some influence on the prediction results, in which the prediction error is about 5% for most cases, while it is up to 15% for assessing the top surface reaction modulus of the subgrade. Compared with the load size, the load location presents a larger influence on the results.
- (3)
- Compared with a BP network model, the JS-BP network model has a higher accuracy for parameter assessment for prefabricated concrete pavement, especially in the case of changing the aircraft loads. When the aircraft load location is changed, the largest error in parameter prediction is 15% for the JS-BP network model, while it is 25% for the BP network model. When the aircraft load size is changed, the largest error in parameter prediction is 5% for the JS-BP network model, while it is 10% for the BP network model.
- (4)
- The excellent performance of the trained JS-BP neural network model is verified with experiments on a small-scale model, as the relative errors between the predicted values and the measured values are smaller than 5% for all the three parameters of the prefabricated concrete pavement.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Structural Parameters | Bending and Tensile Modulus (MPa) | Reaction Modulus at Top of the Subgrade (MN/m3) | Seam Equivalent Modulus (MPa) |
---|---|---|---|
Range of values | 30,000–60,000 | 20–200 | 60–160 |
Step value | 2000 | 5 | 10 |
Step number | 16 | 37 | 11 |
Combination number | 16 × 37 × 11 = 6512 |
Index | MAE | MAPE | MSE | RMSE | R2 |
---|---|---|---|---|---|
Formula |
Sensor No. | Strain (με) | |||
---|---|---|---|---|
Cycle 1 | Cycle 2 | Cycle 3 | Average Value | |
S1 | 25 | 27 | 28 | 27 |
S2 | 57 | 58 | 58 | 58 |
S3 | 81 | 82 | 82 | 82 |
S4 | 54 | 57 | 55 | 55 |
S5 | 21 | 21 | 24 | 22 |
Parameter | Measured Value | Predicted Value | Absolute Error (MPa) | Relative Error (%) |
---|---|---|---|---|
Bending and tensile modulus (MPa) | 39,929 | 40,243 | 314 | 0.8 |
Reaction modulus at top of the subgrade (MN/m3) | 85 | 88.9 | 3.9 | 4.6 |
Seam equivalent modulus (MPa) | 50 | 50.4 | 0.4 | 0.8 |
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Tang, Y.; Lin, Y.; Yu, T. A Neural Network-Based Structural Parameter Assessment Method for Prefabricated Concrete Pavement. Buildings 2025, 15, 843. https://doi.org/10.3390/buildings15060843
Tang Y, Lin Y, Yu T. A Neural Network-Based Structural Parameter Assessment Method for Prefabricated Concrete Pavement. Buildings. 2025; 15(6):843. https://doi.org/10.3390/buildings15060843
Chicago/Turabian StyleTang, Yongsheng, Yunzhen Lin, and Tao Yu. 2025. "A Neural Network-Based Structural Parameter Assessment Method for Prefabricated Concrete Pavement" Buildings 15, no. 6: 843. https://doi.org/10.3390/buildings15060843
APA StyleTang, Y., Lin, Y., & Yu, T. (2025). A Neural Network-Based Structural Parameter Assessment Method for Prefabricated Concrete Pavement. Buildings, 15(6), 843. https://doi.org/10.3390/buildings15060843