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Related Concept Videos

Microcracking in Concrete01:20

Microcracking in Concrete

311
Microcracking in concrete refers to the tiny cracks that can form within the material even before any external load is applied. These microcracks typically occur at the interface between the coarse aggregate and the hydrated cement paste, often as a result of differential volume changes prompted by variations in stress-strain behavior, as well as thermal and moisture movement. Initially, these microcracks remain stable and do not grow substantially until the concrete is stressed to about 30...
311
Types of Non-structural Cracks in Concrete01:28

Types of Non-structural Cracks in Concrete

368
Non-structural cracks are primarily of three types: plastic, early-age thermal, and drying shrinkage cracks. Plastic cracks are further classified into plastic shrinkage cracks and plastic settlement cracks.
Plastic shrinkage cracks typically form within hours after the concrete is poured. The concrete's surface dries faster than the bottom, creating tensile stress that the still-plastic concrete cannot withstand, leading to diagonal or randomly patterned cracks on the concrete surface.
368
Non-destructive Tests for Concrete Strength01:12

Non-destructive Tests for Concrete Strength

317
The rebound hammer test, also known as the Schmidt hammer test, is a non-destructive technique for evaluating the hardness of concrete and, indirectly, the strength of concrete. It operates on the principle that the rebound of a spring-driven mass from a concrete surface correlates to the surface's hardness. The device comprises a mass within a tubular housing, a spring mechanism, and a plunger that strikes the concrete. Upon release, the energy imparted to the mass by the spring causes it...
317
Measurement of Air Content in Concrete01:23

Measurement of Air Content in Concrete

461
Air content measurement in concrete is critical for ensuring structural integrity and durability of concrete structures, especially in environments prone to severe weather conditions. Accurate air content analysis optimizes concrete's resistance to freeze-thaw cycles and enhances its workability and strength. Several methods are standardized under ASTM guidelines to measure the air content in fresh concrete, each suitable for different concrete types and conditions.
The pressure method,...
461
Total Voids in Concrete01:12

Total Voids in Concrete

317
Total voids in concrete encompass gel water volume, capillary pores, and entrapped air. Gel water (retained within the cement hydration products) and physically entrapped or adsorbed water are significant for the hydration process. For complete hydration, it's estimated that the space needed for the products of a cubic centimeter of cement doubles. Capillary pores constitute the unoccupied space within the hydrated cement paste, with their size largely influenced by the water-to-cement...
317
Abrasion Resistance of Concrete01:23

Abrasion Resistance of Concrete

363
Abrasion resistance is an essential characteristic of concrete that determines its durability and longevity under various wear conditions. Concrete surfaces are vulnerable to different types of abrasion. For instance, surfaces may wear down due to the constant movement of vehicles or be eroded by solids carried in water, as seen in concrete canal linings. Specific tests are conducted to measure the abrasion resistance of concrete.
One such test is the revolving disc test, where three plates...
363

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Vision and Deep Learning-Based Algorithms to Detect and Quantify Cracks on Concrete Surfaces from UAV Videos.

Sutanu Bhowmick1, Satish Nagarajaiah1,2, Ashok Veeraraghavan3

  • 1Department of Civil and Environmental Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA.

Sensors (Basel, Switzerland)
|November 10, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-powered drone system for rapid infrastructure inspection. The framework uses computer vision and deep learning to detect and quantify cracks, improving post-disaster structural assessments.

Keywords:
U-Netcomputer visionmorphological operationsunmanned aerial vehicle

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Area of Science:

  • Civil Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Manual inspection of civil infrastructure post-disaster is time-consuming and error-prone.
  • Accessing all areas of large structures for damage assessment is challenging.
  • Unmanned Aerial Vehicles (UAVs) offer a potential solution for timely infrastructure health assessment.

Purpose of the Study:

  • To propose an AI-driven framework for automated detection and quantification of cracks in civil infrastructure.
  • To enhance the speed and accuracy of structural integrity assessments after natural disasters.
  • To provide a robust method for evaluating the global stability of structures.

Main Methods:

  • Utilizing computer vision and deep learning algorithms for crack detection from images.
  • Implementing image segmentation with a U-Net deep neural network for pixel-level crack classification.
  • Applying morphological operations to quantify crack geometry (length, width, area, orientation).

Main Results:

  • Successful detection, quantification, and localization of concrete cracks using the proposed framework.
  • Validation through a laboratory experiment involving a UAV-mounted camera and a concrete beam.
  • Demonstrated efficacy in providing dense measurements of individual crack geometries.

Conclusions:

  • The proposed AI and UAV-based framework offers a viable and efficient alternative to manual infrastructure inspection.
  • Automated crack analysis improves the accuracy and timeliness of structural health prognosis.
  • This technology can significantly aid in assessing a structure's ability to withstand service loads post-disaster.