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Related Concept Videos

Preplaced Aggregate Concrete01:29

Preplaced Aggregate Concrete

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Preplaced aggregate concrete is ideal for construction environments that are not easily accessible. The process begins by properly wetting the gap-graded coarse aggregates to remove the dirt, then placing it in the form and compacting it. Voids are filled with a mortar mix pumped under pressure through slotted pipes. This mortar typically consists of Portland cement, pozzolan, fine aggregates, water, and a fluidizing aid. The pozzolan helps reduce bleeding and segregation while improving the...
95
Bonding and Strength of Aggregate01:12

Bonding and Strength of Aggregate

152
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...
152
Impact Strength of Concrete01:21

Impact Strength of Concrete

199
Impact strength in concrete is a critical measure that reflects the material's capability to endure the forces applied during pile driving and when supporting machinery foundations that experience impulsive loads. It is also essential when handling precast concrete components to prevent accidental damage. The impact strength is assessed by observing the concrete's resistance to repeated impacts and energy absorption capacity. A key indicator of significant damage to concrete is when it...
199
Design Example: Aggregate Gradation01:24

Design Example: Aggregate Gradation

93
The right type and quality of aggregates are crucial for concrete as they significantly influence its properties, mix proportions, and cost-effectiveness. If different sources are available for sand, the commonly used fine aggregate in concrete, the selection of sand is primarily based on its gradation.
The grading, or particle-size distribution, of sand is determined using sieve analysis, with standard sizes ranging from 150 μm to 10 mm (ASTM No. 100 sieve to 3⁄8 in. sieve). Sand is...
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Toughness and Hardness of Aggregate01:22

Toughness and Hardness of Aggregate

254
Toughness and hardness are critical properties of aggregate materials used in concrete, particularly on pavement surfaces and industrial flooring subjected to heavy loads. Toughness is defined as the aggregate's resistance to failure by impact and is measured by the aggregate impact value (AIV). For this, the aggregate impact value test is performed, wherein the impact is delivered by a standard hammer, which falls freely under its own weight onto the aggregates. The aggregates fragment in...
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Aggregate Cement Ratio01:21

Aggregate Cement Ratio

239
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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Forecasting the strength of preplaced aggregate concrete using interpretable machine learning approaches.

Muhammad Faisal Javed1, Muhammad Fawad2,3, Rida Lodhi4

  • 1Department of Civil Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Swabi, 23640, Pakistan. arbabfaisal@cuiatd.edu.pk.

Scientific Reports
|April 10, 2024
PubMed
Summary

This study enhances preplaced aggregate concrete (PAC) compressive strength prediction using machine learning. The XGBoost model achieved high accuracy, identifying gravel, water-to-binder ratio, and superplasticizer as key influencing factors.

Keywords:
Compressive strength predictionConstruction engineeringMachine learning modelsPreplaced aggregate concreteTwo-stage concrete

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Area of Science:

  • Civil Engineering
  • Materials Science
  • Data Science

Background:

  • Preplaced aggregate concrete (PAC), also known as two-stage concrete (TSC), is a widely used construction material.
  • The complex production process of PAC complicates accurate prediction of its compressive strength (CS).
  • Reliable CS prediction is crucial for ensuring the structural integrity and performance of PAC in various applications.

Purpose of the Study:

  • To enhance the comprehension and prediction accuracy of PAC compressive strength (CS) utilizing machine learning (ML) models.
  • To evaluate the performance of thirteen different ML models for predicting PAC CS.
  • To identify the most significant input parameters influencing PAC CS through sensitivity analysis.

Main Methods:

  • Evaluation of thirteen machine learning models using a dataset of 261 data points and eleven input variables.
  • Application of XGBoost as the best-fit model for further analysis using SHAP (SHapley Additive exPlanations) analysis.
  • Development of a graphical user interface (GUI) for practical application of the predictive model.

Main Results:

  • The XGBoost model demonstrated exceptional accuracy in predicting PAC compressive strength, achieving a correlation coefficient of 0.9791 and R² of 0.9583.
  • Gradient Boosting (GB) and CatBoost (CB) also exhibited robust performance.
  • Sensitivity analysis identified gravel (44.7%), sand (19.5%), and cement (15.6%) as the most impactful input parameters, while SHAP analysis highlighted the water-to-binder ratio, superplasticizer, and gravel as most significant.

Conclusions:

  • Machine learning models, particularly XGBoost, offer a highly accurate and reliable method for predicting the compressive strength of preplaced aggregate concrete.
  • The developed GUI provides a practical tool for civil engineers to leverage ML models for informed decision-making in construction projects.
  • This study significantly contributes to improving the reliability and applicability of predictive models in the field of preplaced aggregate concrete strength prediction.