Mr. Jianli Zhang Profile

Jianli Zhang

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Proceedings Article | 27 September 2024 Paper

Analysis and experimental research on key parameters of high-pressure water jet robot for breaking concrete

Jianli Zhang, Yupeng Wang, Weilong Dong

Proceedings Volume 13261, 132610W (2024) https://doi.org/10.1117/12.3046909

KEYWORDS: Nozzles, Engineering, Analytical research, Cements, Roads, Cutting equipment, Water, Vibration, Resistance, Control systems

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High pressure water jet has broken through a series of negative impacts such as noise and dust caused by traditional mechanical crushing equipment in the impact crushing of concrete, and has advantages such as high efficiency, economy, and environmental protection. However, in practical engineering applications, it is an urgent need to choose appropriate dismantling parameters to reduce dismantling costs and improve dismantling efficiency. Based on this, this article takes the Bernoulli equation as the theoretical basis to analyze the process of high-pressure water jet breaking concrete, and provides the theoretical values of nozzle outlet velocity and spray flow rate; And the influence of breaking parameters such as jet pressure, target distance, and nozzle lateral velocity on breaking depth was studied in the experiment. The results show that as the jet pressure increases, the depth of water jet breaking increases approximately linearly, indicating that a higher jet pressure is more conducive to water jet breaking; As the target distance increases, the breaking depth first increases and then decreases. When the jet target distance is 60mm, the breaking depth reaches the maximum value of 76mm; The depth of concrete breaking is inversely proportional to the lateral velocity of the nozzle. As the lateral velocity of the nozzle increases, the depth of breaking gradually decreases. When the lateral velocity reaches 60mm/s, the rate of attenuation gradually decreases. The research results provide data guidance for selecting reasonable demolition parameters in the actual demolition process of engineering.

Proceedings Article | 9 August 2023 Paper

Road pothole detection based on improved YOLOv7

Jianli Zhang, Jiaofei Lei

Proceedings Volume 12782, 127820V (2023) https://doi.org/10.1117/12.3000774

KEYWORDS: Object detection, Roads, Performance modeling, Deep learning, Detection and tracking algorithms, Data modeling, Mathematical optimization, Ablation

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According to the World Health Organization, the current global death toll from road traffic accidents is as high as 1.3 million annually. The main cause of road traffic accidents is poor road conditions, and potholes on roads are the most serious type of road diseases. Therefore, timely detection and treatment of road potholes is very necessary. This paper proposes a method based on the use of YOLOv7 deep learning model to detect potholes on the road. At the same time, CBAM attention mechanism and optimization of loss function are added on the basis of this method. Combined with the idea of transfer learning, the improved YOLOv7 network is trained. The final test results are significantly improved compared with other road potholes detection models. F1 score is 78%, Precision value can reach 85.81%, and mAP value can reach 83.02%.

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