Method for Detecting Roadbed Compaction Degree Based on Machine Learning and Vibration Acceleration
DOI:
https://doi.org/10.62051/ijmee.v3n3.07Keywords:
Machine Learning, Vibration Acceleration, Roadbed Compaction, Detection MethodsAbstract
Roadbed construction occupies a core position in highway construction, but its quality is easily constrained by multiple factors such as changing environmental factors, the performance of construction equipment, and the professional abilities of construction personnel, leading to potential quality risks. Traditional quality inspection methods are mostly carried out after construction is completed, making it difficult to achieve continuous and real-time monitoring of roadbed compaction quality, which to some extent limits the real-time feedback and adjustment of construction quality. Vibration compaction technology has been widely used in the field of highway engineering due to its high efficiency and speed. The compaction degree is directly related to the durability and service life of the highway; therefore, accurate and efficient detection of compaction degree is crucial. This article proposes a method for detecting roadbed compaction degree by integrating machine learning (ML) and vibration acceleration signals. This method aims to achieve accurate evaluation of roadbed compaction by real-time monitoring and analysis of vibration acceleration data, combined with the powerful prediction and classification capabilities of ML algorithms. The experimental results show that this method not only improves the detection efficiency, but also significantly enhances the accuracy of compaction degree detection.
References
[1] Li P, Dong Y. Characterization and impact analysis of freezing and expansion disease of roadbed in seasonal freezing zone: A case of heavy railroads[J]. Geohazard Mechanics, 2023, 1(3): 218-230.
[2] Wu W, Wang G, Wang L, et al. Dynamic Response Analysis of Non-Uniform Unsaturated Soil Layer Roadbed under Uniform Moving Load[J]. Mechanics of Solids, 2024, 59(1): 280-296.
[3] Wu K, Sun W, Liu S, et al. Discrete element modeling of vibration compaction effect of the vibratory roller in roundtrips on gravels[J]. Journal of Testing and Evaluation, 2021, 49(5): 3869-3884.
[4] Beloborodov R, Gunning J, Pervukhina M, et al. Rock-physics machine learning toolkit for joint litho-fluid facies classification and compaction modeling[J]. The Leading Edge, 2021, 40(10): 742-750.
[5] Wang Z Y, Ling X Z, Zhao Y Y, et al. Numerical simulation of vibrational response characteristics of railway subgrades with insulation boards[J]. Sciences in Cold and Arid Regions, 2022, 14(1): 23-31.
[6] Ran Y, Bai W, Kong L, et al. Prediction of soil degree of compaction based on machine learning: a case study of two fine-grained soils[J]. Engineering Computations, 2024, 41(1): 46-67.
[7] Yu S, Shen S. Compaction prediction for asphalt mixtures using wireless sensor and machine learning algorithms[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 24(1): 778-786.
[8] Farshbaf Aghajani H, Karimi S, Hatefi Diznab M. An experimental and machine-learning investigation into compaction of the cemented sand-gravel mixtures and influencing factors[J]. Transportation Infrastructure Geotechnology, 2023, 10(5): 816-855.
[9] Benbouras M A, Lefilef L. Progressive machine learning approaches for predicting the soil compaction parameters [J]. Transportation Infrastructure Geotechnology, 2023, 10(2): 211-238.
[10] Xu Z, Khabbaz H, Fatahi B, et al. Real-time determination of sandy soil stiffness during vibratory compaction incorporating machine learning method for intelligent compaction[J]. Journal of rock mechanics and geotechnical engineering, 2022, 14(5): 1609-1625.
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