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Related Concept Videos

Response Surface Methodology01:16

Response Surface Methodology

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:

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Related Experiment Video

Updated: Jun 20, 2026

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data
09:55

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data

Published on: December 12, 2013

Dataset for surface and subsurface characterization of removable urban pavements with a functionalized surface

Grégory Andreoli1, Margarita Skamantzari2, Franziska Schmidt3

  • 1Gustave Eiffel University, MAST/EMGCU Salon-de-Provence, France.

Data in Brief
|June 19, 2026
PubMed
Summary

Innovative Removable Urban Pavements (RUP) offer faster access for utility maintenance. A drone and smartphone system combined with Ground Penetrating Radar (GPR) enables efficient monitoring and anomaly detection in smart city infrastructure.

Keywords:
3D GPRImageryRUPYOLO

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Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces
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Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces

Published on: September 11, 2018

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Last Updated: Jun 20, 2026

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data
09:55

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data

Published on: December 12, 2013

Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces
06:14

Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces

Published on: September 11, 2018

Area of Science:

  • Civil Engineering
  • Materials Science
  • Geophysics

Background:

  • Transportation infrastructure faces degradation from extreme weather and increased traffic.
  • Mechanical stresses and settlements impact underground utility networks.
  • Innovative solutions are needed for future road design and maintenance.

Purpose of the Study:

  • To introduce Removable Urban Pavements (RUP) for improved access to underground utilities.
  • To develop a Non-Destructive Testing (NDT) methodology for RUP condition monitoring.
  • To create an automated system for classifying pavement components and anomalies.

Main Methods:

  • Utilizing Unmanned Aerial Vehicle (UAV) for structural identification and pavement classification.
  • Employing smartphone imagery for surface anomaly detection.
  • Implementing Stepped-Frequency 3D Ground Penetrating Radar (GPR) for subsurface visualization.
  • Developing a deep learning model (YOLO-type) for automatic classification.

Main Results:

  • The integrated NDT system enables element-by-element detection and classification of RUP and standard pavements.
  • Surface and subsurface anomalies can be effectively identified and visualized.
  • The foundation for an evolving database of structural components and anomalies is established.

Conclusions:

  • The developed NDT methodology provides reliable monitoring of Removable Urban Pavements.
  • Automated classification using deep learning enhances the efficiency of infrastructure management.
  • This system supports the design and maintenance of resilient smart city infrastructure.