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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
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Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning.

Rogelio Bustamante-Bello1, Alec García-Barba1, Luis A Arce-Saenz1

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This study introduces a novel system using vehicle sensors and machine learning to detect road surface anomalies in real-time. It enables better public spending decisions for urban mobility by identifying pavement issues proactively.

Keywords:
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Area of Science:

  • Civil Engineering
  • Computer Science
  • Data Science

Background:

  • Urban infrastructure maintenance often relies on reactive measures like citizen reports or incident responses.
  • Current systems lack real-time capabilities for detecting pavement anomalies, leading to inefficient public spending on mobility.

Purpose of the Study:

  • To develop and evaluate a real-time system for detecting and classifying road surface anomalies using vehicle-mounted sensors.
  • To leverage fog computing and Vehicle-to-Infrastructure (V2I) networks for efficient data processing and anomaly detection.
  • To compare the effectiveness of different Machine Learning Algorithms (MLA) for pavement condition analysis.

Main Methods:

  • Utilizing accelerometry sensors in instrumented vehicles to capture road roughness data and establish a flat reference.
  • Implementing a fog-computing architecture integrated with a V2I network for data transmission and processing.
  • Applying supervised Machine Learning Algorithms, specifically Artificial Neural Networks and K-Nearest Neighbors, for anomaly detection and classification.

Main Results:

  • The developed system successfully detects and classifies various road problems and abnormal pavement conditions.
  • Comparison of MLA performance indicated suitability for analyzing acquired road data.
  • The system provides a mechanism for visualizing street quality and mapping areas with significant anomalies.

Conclusions:

  • The proposed system offers a proactive approach to urban infrastructure maintenance, enhancing decision-making for public spending on mobility.
  • Real-time anomaly detection through vehicle-mounted sensors and fog computing is feasible and effective.
  • This technology can significantly improve the management and upkeep of city streets and avenues.