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Related Concept Videos

Abrasion Resistance of Concrete01:23

Abrasion Resistance of Concrete

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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.
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Aggregate Cement Ratio01:21

Aggregate Cement Ratio

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The Aggregate Cement ratio refers to the weight of aggregate divided by the weight of cement in a concrete mix. Altering this ratio has profound effects on the concrete's properties. This ratio plays a pivotal role in determining the strength, workability, and durability of concrete. When the Aggregate Cement ratio is higher, the mix is leaner, meaning it has less cement paste to lubricate the aggregate, potentially making the concrete less workable. Such mixes, known as lean, enhance the...
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Workability of Concrete01:25

Workability of Concrete

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The workability of concrete is a crucial property that affects its handling, placing, and finishing during construction. It describes the ease with which concrete can be mixed, placed, compacted, and finished. Workability is primarily concerned with the concrete's movement and its ability to resist internal friction and external resistance from molds and reinforcements during the application process.
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Deleterious Substances in Aggregate01:25

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Deleterious substances in aggregates can be detrimental to the quality and durability of concrete. These substances include organic impurities like loam, which interfere with cement hydration and are usually present in the sand. These prevent a good bond between aggregate and cement paste. Organic impurities can be detected using the colorimetric test, where the darkness of a solution after agitation indicates the level of organic content.
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Bonding and Strength of Aggregate01:12

Bonding and Strength of Aggregate

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The bond between aggregate particles and the cement matrix is significantly influenced by the shape and surface texture of the aggregates. High-strength concretes benefit from a rougher texture, which leads to stronger bonding due to greater adhesion. Angular aggregates with larger surface areas also enhance this bond. The bonding quality, however, is complex to assess as no universally accepted test exists. Good bonding is indicated when a crushed concrete specimen shows some aggregate...
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Fatigue Strength of Concrete01:22

Fatigue Strength of Concrete

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Fatigue, in the context of materials science and engineering, refers to the weakening or failure of a material caused by repeatedly applied loads, even if these loads are below the strength limit of the material. Fatigue strength in concrete is a critical property that influences its durability and longevity. Concrete can fail in two ways due to fatigue. Static fatigue or creep rupture occurs under a constant load or one that increases slowly. The other failure mode is due to cyclical or...
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Predicting self-healing efficiency in recycled aggregate concrete using optimized machine learning models.

Kunpeng Cao1,2, Dunwen Liu3, Kian Hau Kong2

  • 1School of Resources and Safety Engineering, Central South University, Changsha, 410083, China.

Scientific Reports
|October 22, 2025
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Summary

This study uses machine learning to predict self-healing concrete performance with recycled coarse aggregate (RCA). An optimized NRBO-XGBoost model accurately predicted healing rates, showing crack width is key, not RCA amount.

Keywords:
Crack repairMachine learningPredictionRecycled aggregateSelf-healing concrete

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Area of Science:

  • Materials Science
  • Civil Engineering
  • Sustainable Construction

Background:

  • Self-healing concrete offers enhanced durability but faces cost and complexity challenges.
  • Recycled coarse aggregate (RCA) presents a sustainable alternative, potentially reducing costs and improving concrete properties.
  • Predicting the self-healing performance of concrete incorporating RCA is crucial for its practical application.

Purpose of the Study:

  • To develop and validate an optimized machine learning (ML) model for predicting the self-healing performance of concrete containing RCA.
  • To assess the influence of various factors, including RCA content, on the self-healing rate of concrete.
  • To provide a cost-effective and efficient method for evaluating sustainable concrete materials.

Main Methods:

  • A database of 173 datasets was compiled, with eight input variables and the self-healing rate as the output.
  • An optimized NRBO-XGBoost model was developed and compared against four other ML models and two optimization techniques.
  • Shapley method was employed for sensitivity analysis to identify key influencing factors.

Main Results:

  • The optimized NRBO-XGBoost model demonstrated superior performance, achieving high accuracy (R² = 0.9569, RMSE = 7.1800, MAE = 4.9575).
  • Sensitivity analysis revealed crack width as the most significant factor affecting self-healing, with RCA showing minimal impact within the tested range.
  • The model's predictive capability offers a reliable tool for assessing self-healing concrete performance.

Conclusions:

  • The study successfully introduced an optimized ML approach for predicting the self-healing performance of concrete with RCA.
  • While RCA showed minimal impact on healing within the studied range, its economic and environmental advantages are significant for sustainable construction.
  • The findings provide valuable theoretical insights and practical guidance for utilizing recycled materials in self-healing concrete applications.