Research on Cement Concrete Bridge Asphalt Pavement Compaction Technology

Ying, Xiao Meng; Zhang, Deng Feng; Li, Qin Yong; Meng, Lin Chun

doi:10.4028/www.scientific.net/kem.599.224

Cited by 2 publications

(1 citation statement)

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“…Gunther [22] and Bild [23] calculated and analyzed the mechanical response of steel bridge deck pavement by using a finite element model, compared the computational results of the finite element model with testing data of bridge engineering projects, and assessed the factors affecting the service life of bridge deck pavement from different perspectives. Ying [24] suggested that the combination of vibratory compaction and rubber rolling can guarantee the construction quality of asphalt mixture pavement over cement-concrete bridge deck and prevent inadequate compaction. Wang et al [25] performed a comprehensive analysis of the static and dynamic stresses in a bridge deck slab during bridge deck pavement construction and proposed a sustainable technique for bridge deck pavement construction.…”

Section: Introductionmentioning

confidence: 99%

Experiment and Numerical Simulation of the Dynamic Response of Bridges under Vibratory Compaction of Bridge Deck Asphalt Pavement

Peng

Dan

Yang

2019

Mathematical Problems in Engineering

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Vibratory compaction of bridge deck pavement impacts the structural integrity of bridges to certain degrees. In this study, we analyzed the dynamic response of different types of concrete-beam bridges (continuous beam and simply supported beam) with different cross-sectional designs (T-beam and hollow-slab beam) under vibratory compaction of bridge deck asphalt pavement. The dynamic response patterns of the dynamic deformation and acceleration of bridges under pavement compaction were obtained by performing a series of field experiments and a three-dimensional finite element simulation. Based on the finite element model, the dynamic responses of bridge structures with different spans and cross-sectional designs under different working conditions of vibratory compaction were analyzed. The use of different vibration parameters for different bridge structures was proposed to safeguard their structural safety and reliability.

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Section: Introductionmentioning

confidence: 99%

Experiment and Numerical Simulation of the Dynamic Response of Bridges under Vibratory Compaction of Bridge Deck Asphalt Pavement

Peng

Dan

Yang

2019

Mathematical Problems in Engineering

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Measurement of Construction Materials Properties Using Wi-Fi and Convolutional Neural Networks

et al. 2022

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The process of identifying the physical properties of raw construction materials is vital in several industrial and quality assurance applications. Ideally, this process needs to be performed without damaging the sample and at low-cost, while obtaining high-accurate results. In this work, a novel non-destructive construction materials classification tool is proposed. The proposed method is based on passing Wi-Fi signals through the observed samples, then analyzing the Channel State Information (CSI) magnitude and phase components. The collected CSI data packets are pre-processed by performing an averaging operation. Then, the resulting data are divided into training and validation sets and used to train Convolutional Neural Networks (CNNs). Here, the trained CNN models are formulated either as classifiers or regression models, depending on the material under test. If the objective is to sort materials within specific classes, then the CNN is formulated as a classifier. Alternatively, if the goal is to estimate a continuously varying parameter in a material, then the CNN is formulated as a regression model. Furthermore, as per the collected data, the proposed method is used to identify the construction materials based on their thickness, water content value (moisture), and compaction. The obtained experimental results effectively demonstrate the potential and merits of the proposed method. Overall, the trained CNN models achieved a 100% validation accuracy and a low validation loss, which confirms that the method is valid and highly accurate.INDEX TERMS Channel state information (CSI), classification, construction materials, convolutional neural network (CNN), Wi-Fi sensing.[1], [2]. In this work, we focus on observing and measuring three physical parameters namely, thickness, moisture content, and degree of compaction, in different individual raw construction materials. The conducted experiments were designed to study the internal physical properties of the observed materials, as well as evaluate the proposed method's performance robustness to variations in physical traits such as thickness. These parameters have a substantial significance in the industry and quality assurance processes. Furthermore, the existing material classification methods that investigate the mentioned parameters often have a compromise between accuracy, cost, and deployability in practical setups. In the industry, there is considerable interest in measuring the

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Research on Cement Concrete Bridge Asphalt Pavement Compaction Technology

Cited by 2 publications

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Experiment and Numerical Simulation of the Dynamic Response of Bridges under Vibratory Compaction of Bridge Deck Asphalt Pavement

Experiment and Numerical Simulation of the Dynamic Response of Bridges under Vibratory Compaction of Bridge Deck Asphalt Pavement

Measurement of Construction Materials Properties Using Wi-Fi and Convolutional Neural Networks

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