Road performance of waterborne unsaturated polyester concrete cold patching materials for potholes in bituminous pavement

Zhang, Zhen; Zhang, Hongliang; Lv, Wei; Yang, Yunjie

doi:10.1016/j.conbuildmat.2022.128689

Construction and Building Materials

2022

DOI: 10.1016/j.conbuildmat.2022.128689

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Road performance of waterborne unsaturated polyester concrete cold patching materials for potholes in bituminous pavement

Zhen Zhang

Hongliang Zhang²,

Wei Lv³

et al.

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Cited by 7 publications

(1 citation statement)

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“…IJRITCC | October 2023, Available @ http://www.ijritcc.org poor drainage [14]. Subsequently, the potholes occur because of internal and external causes [15]. The internal conditions include depletion of the pavement materials, heavy snowfall, durability, and heavy rainfall [16].…”

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An Optimized Deep Learning Framework For Pothole Detection

Sridhar Patthi

2023

IJRITCC

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Pothole detection plays a crucial role in preventing road accidents and is effective in establishing road maintenance and safety. Although various pothole detection models are designed to accurately identify the pothole based on road images, they face issues in accuracy and hyperparameter tuning. The presented research work concentrates on developing a novel optimized deep learning model for the accurate prediction of potholes on the road infrastructure using the recurrent neural network (RNN) and grey wolf optimization (GWO). Initially, the road images are collected and pre-processed. The pre-processing includes the removal of noises, image resizing, etc., to improve the image quality. Further, texture-based feature extraction was employed to extract the most relevant features from the pre-processed image. Then, the RNN architecture was trained using the extracted features to learn the interconnections between the image features and pothole detection. In addition, the GWO fitness solution was integrated into the classification module to tune and optimize the RNN hyperparameters, which increases the detection performances such as accuracy, and reduces the loss function. Finally, the presented model was evaluated with the publically available road image detection database and the outcomes are determined. The performance assessment demonstrates that the designed model attained greater accuracy of 98.76%, and a loss function of 0.06. Furthermore, a comparative assessment was performed with existing methods to evaluate the effectiveness of the proposed model.

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An Optimized Deep Learning Framework For Pothole Detection

Sridhar Patthi

2023

IJRITCC

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Design and laboratory performance of reactive cold patching materials containing epoxy/unsaturated polyester blends

Wang

et al. 2023

Construction and Building Materials

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Effects of emulsified asphalt on the rheological behaviors of magnesium phosphate cement

Zhang,

Zhou,

Zhang

et al. 2023

Construction and Building Materials

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Road performance of waterborne unsaturated polyester concrete cold patching materials for potholes in bituminous pavement

Cited by 7 publications

References 39 publications

An Optimized Deep Learning Framework For Pothole Detection

An Optimized Deep Learning Framework For Pothole Detection

Design and laboratory performance of reactive cold patching materials containing epoxy/unsaturated polyester blends

Effects of emulsified asphalt on the rheological behaviors of magnesium phosphate cement

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