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Related Concept Videos

Design Example: Joints in Concrete Pavements01:28

Design Example: Joints in Concrete Pavements

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Concrete pavement joints are essential for maintaining the structural integrity and longevity of pavement by controlling where and how the pavement cracks. These joints can be categorized based on their functions, such as contraction or control joints, construction joints, isolation joints, and expansion joints.
Contraction joints are typically formed by sawing a groove into the concrete shortly after it has hardened. This creates a weakened vertical plane, deliberately encouraging cracking at...
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Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
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Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot.

Balakrishnan Ramalingam1, Abdullah Aamir Hayat1, Mohan Rajesh Elara1

  • 1Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore.

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Summary

This study introduces a deep learning framework for pavement inspection, accurately detecting cracks and garbage using the Panthera robot. The system is suitable for real-time deployment in automated cleaning and sweeping tasks.

Keywords:
garbage detectionmachine learningpavement cracks detectionpavement sweeping robotself-reconfigurable

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Area of Science:

  • Robotics and Artificial Intelligence
  • Civil Engineering and Infrastructure Management

Background:

  • Pavement inspection for cracks and garbage is crucial and frequently performed.
  • Current inspection methods can be integrated with sweeping machines for efficiency.

Purpose of the Study:

  • To propose a deep learning-based pavement inspection framework for the Panthera robot.
  • To enable automated detection and localization of pavement defects and garbage.

Main Methods:

  • Utilized SegNet for semantic segmentation of pavement areas.
  • Employed Deep Convolutional Neural Networks (DCNNs) for object detection of defects and garbage.
  • Integrated Mobile Mapping System (MMS) for precise geotagging of identified issues.

Main Results:

  • The proposed framework achieved high accuracy in identifying pavement defects and garbage.
  • Experimental results demonstrate the system's effectiveness in crack and litter detection.
  • The system proved suitable for real-time application in pavement maintenance.

Conclusions:

  • The deep learning framework is effective for automated pavement inspection.
  • The system can be deployed for real-time garbage detection and subsequent cleaning operations.
  • This approach enhances the efficiency and accuracy of pavement maintenance tasks.