The moving load identification method on asphalt roads based on the BP neural network and FBG sensor monitoring

Liao, Menghui; Liang, Sheng; Luo, Rong; Chen, Yu

doi:10.1016/j.conbuildmat.2023.131216

Cited by 12 publications

(4 citation statements)

References 26 publications

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“…The optimized BP neural networks achieve convergence within 250-300 iterations, while traditional BP neural networks typically require 300-400 iterations to converge gradually. Moreover, the optimized BP neural net-works based on genetic algorithms achieve better minimum values for the mean square error (MSE) compared to regular BP neural networks [27,28]. From Figure 13, it is evident that both the regular BP neural network and the improved GA-BP neural network can fit the real values reasonably well.…”

Section: Comparison Of Training Resultsmentioning

confidence: 96%

ADAS Simulation Result Dataset Processing Based on Improved BP Neural Network

Zhao,

Chen,

Huang

2024

Data

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The autonomous driving simulation field lacks evaluation and forecasting systems for simulation results. The data obtained from the simulation of target algorithms and vehicle models cannot be reasonably estimated. This problem affects subsequent vehicle improvement and parameter calibration. The authors relied on the simulation results of the AEB algorithm. We selected the BP Neural Network as the basis and improved it with a genetic algorithm optimized via a roulette algorithm. The regression evaluation indicators of the prediction results show that the GA-BP neural network has better prediction accuracy and generalization ability than the original BP neural network and other optimized BP neural networks. This GA-BP neural network also fills the Gap in Evaluation and Prediction Systems.

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Section: Comparison Of Training Resultsmentioning

confidence: 96%

ADAS Simulation Result Dataset Processing Based on Improved BP Neural Network

Zhao,

Chen,

Huang

2024

Data

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“…FBG sensors have gained popularity in the evaluation of asphalt mixture performance and monitoring its reaction [71,72]. FBG sensors are a type of sensor that rapidly developed in recent decades.…”

Section: Fiber Bragg Grating Sensorsmentioning

confidence: 99%

Recent Development in Intelligent Compaction for Asphalt Pavement Construction: Leveraging Smart Sensors and Machine Learning

Wang,

Li,

Zhang

et al. 2024

Sensors

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Intelligent compaction (IC) has emerged as a breakthrough technology that utilizes advanced sensing, data transmission, and control systems to optimize asphalt pavement compaction quality and efficiency. However, accurate assessment of compaction status remains challenging under real construction conditions. This paper reviewed recent progress and applications of smart sensors and machine learning (ML) to address existing limitations in IC. The principles and components of various advanced sensors deployed in IC systems were introduced, including SmartRock, fiber Bragg grating, and integrated circuit piezoelectric acceleration sensors. Case studies on utilizing these sensors for particle behavior monitoring, strain measurement, and impact data collection were reviewed. Meanwhile, common ML algorithms including regression, classification, clustering, and artificial neural networks were discussed. Practical examples of applying ML to estimate mechanical properties, evaluate overall compaction quality, and predict soil firmness through supervised and unsupervised models were examined. Results indicated smart sensors have enhanced compaction monitoring capabilities but require robustness improvements. ML provides a data-driven approach to complement traditional empirical methods but necessitates extensive field validation. Potential integration with digital construction technologies such as building information modeling and augmented reality was also explored. In conclusion, leveraging emerging sensing and artificial intelligence presents opportunities to optimize the IC process and address key challenges. However, cooperation across disciplines will be vital to test and refine technologies under real-world conditions. This study serves to advance understanding and highlight priority areas for future research toward the realization of IC’s full potential.

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“…To address the issue of disparate scales among feature values, it is necessary to normalize the data, aiming to enhance the model's generalization ability and improve convergence performance [23]. This normalization process involves utilizing Eq.…”

Section: Normalization Processmentioning

confidence: 99%

Prediction of Laser Cladding Layer Cross-Sectional Morphology - Based on SSA-BP Neural Network

Cui¹,

Liu²,

Guo³

et al. 2023

Preprint

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In the face of challenges such as difficulties in controlling the morphology of laser cladding layers and high costs in laser cladding technology, this paper utilizes the Sparrow Search Algorithm (SSA) to optimize the error Back Propagation (BP) neural network. The SSA-BP network is applied to predict laser cladding cross-sectional morphology. A comparison is made between the predicted and the experimental values, using R2, RMSE, and MAPE as evaluation metrics for the model. The results indicate that the predicted heights of the cladding layers are 0.972, 0.076, and 3.948%, respectively, while the predicted widths are 0.862, 0.099, and 2.004%, respectively. Furthermore, experiments beyond the input range of process parameters are conducted to validate the universality of the model. The results demonstrate that the predicted heights of the cladding layers are 0.952, 0.118, and 4.771%, respectively, and the predicted widths are 0.813, 0.209, and 3.688%, respectively. Moreover, we also compared the predictions from models of SSA-BP, MPA-BP, SOA-BP, and PSO-BP. In conclusion, this paper's model exhibits excellent predictive capabilities and good universality, providing reference for the prediction and control of cross-sectional morphology in laser cladding technology.

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The moving load identification method on asphalt roads based on the BP neural network and FBG sensor monitoring

Cited by 12 publications

References 26 publications

ADAS Simulation Result Dataset Processing Based on Improved BP Neural Network

ADAS Simulation Result Dataset Processing Based on Improved BP Neural Network

Recent Development in Intelligent Compaction for Asphalt Pavement Construction: Leveraging Smart Sensors and Machine Learning

Prediction of Laser Cladding Layer Cross-Sectional Morphology - Based on SSA-BP Neural Network

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