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Related Concept Videos

Microcracking in Concrete01:20

Microcracking in Concrete

162
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...
162
Types of Non-structural Cracks in Concrete01:28

Types of Non-structural Cracks in Concrete

207
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.
207
Design Example: Joints in Concrete Pavements01:28

Design Example: Joints in Concrete Pavements

226
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...
226
Non-destructive Tests for Concrete Strength01:12

Non-destructive Tests for Concrete Strength

155
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...
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Updated: Jul 30, 2025

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
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Machine-Aided Bridge Deck Crack Condition State Assessment Using Artificial Intelligence.

Xin Zhang1, Benjamin E Wogen1, Xiaoyu Liu2

  • 1Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA.

Sensors (Basel, Switzerland)
|May 13, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-powered method to assess bridge deck crack conditions, improving accuracy and efficiency in bridge inspections mandated by the Federal Highway Administration (FHWA). The approach aids inspectors in managing resources and ensuring infrastructure safety.

Keywords:
deep learningimage classificationmachine-aided bridge inspectionrisk managementsemantic segmentation

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Area of Science:

  • Civil Engineering
  • Artificial Intelligence
  • Computer Vision

Background:

  • Federal Highway Administration (FHWA) mandates biannual bridge inspections, recorded in the National Bridge Inventory (NBI).
  • Increasing complexity of inspection specifications, including element-level assessments, demands more inspector training and field time.
  • Current methods face challenges in efficiently assessing detailed bridge element conditions.

Purpose of the Study:

  • To develop and evaluate a machine-aided bridge inspection method using artificial intelligence (AI).
  • To automate the condition state assessment of cracking in reinforced concrete bridge deck elements.
  • To assist bridge inspectors in meeting new FHWA requirements for element-level inspections.

Main Methods:

  • Utilized a deep learning-based workflow integrating image classification and semantic segmentation.
  • Employed a deep neural network to extract critical information from bridge images.
  • Developed a system to evaluate crack condition states according to FHWA specifications.

Main Results:

  • Demonstrated the effectiveness of the AI workflow for assessing crack conditions in bridge decks.
  • The AI method accurately extracts information required by bridge inspection manuals.
  • The system enables objective condition state determination for cracks.

Conclusions:

  • The AI-based method enhances the efficiency and accuracy of bridge deck crack inspections.
  • This approach helps balance costs and risks associated with AI in infrastructure management.
  • Departments of Transportation can implement this AI tool to improve bridge asset management and community service.