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Related Concept Videos

Bulk Density of Aggregate01:22

Bulk Density of Aggregate

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Bulk density refers to the mass of aggregate particles that would fill a unit volume. The concept of bulk density originates from the inability to pack aggregate particles in a manner that completely eliminates void spaces. Hence, the term bulk refers to the volume that encompasses both the aggregates and the voids. This measurement is crucial when aggregates are batched by volume and is used to convert quantities by mass to volume.
Most natural mineral aggregates, like sand and gravel,...
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Moisture Content and Bulking of Aggregate01:10

Moisture Content and Bulking of Aggregate

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The moisture content of aggregates is a crucial factor in construction, particularly in concrete mixing, as it influences the total water required in the mix. Moisture content represents the water coated on the exterior surface of the aggregate existing in a saturated and surface-dry condition. The total water content of a moist aggregate is the sum of its moisture content and water absorption.
When aggregates are exposed to rain or sit in stockpiles, they absorb moisture, which must be...
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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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Estimating Population Mean with Known Standard Deviation01:16

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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
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Specific Gravity of Aggregate01:19

Specific Gravity of Aggregate

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Aggregates typically contain pores, which can be either permeable or impermeable. Considering the pores in the aggregates, the specific gravity of aggregates is defined in three different forms, namely, bulk or gross specific gravity, apparent specific gravity, and absolute specific gravity.
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Estimation of construction waste composition based on bulk density: A big data-probability (BD-P) model.

Liang Yuan1, Weisheng Lu1, Fan Xue1

  • 1Department of Real Estate and Construction, Faculty of Architecture, The University of Hong Kong, Hong Kong, China.

Journal of Environmental Management
|May 24, 2021
PubMed
Summary

This study introduces a big data-probability (BD-P) model to accurately estimate construction waste composition using bulk density. The novel approach combines big data analytics with probability theories for efficient waste management.

Keywords:
Big dataBig data enabled probabilistic analysisComposition estimationConstruction wasteProbability

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Area of Science:

  • Environmental Science
  • Data Science
  • Civil Engineering

Background:

  • Accurate construction waste composition estimation is vital for waste management facilities.
  • Real-world scenarios demand both speed and precision in waste analysis.
  • Existing methods face challenges in balancing efficiency and accuracy.

Purpose of the Study:

  • To develop a novel model for estimating construction waste composition.
  • To address the need for quick and accurate waste analysis in practical settings.
  • To integrate big data analytics with probability theories for predictive modeling.

Main Methods:

  • Development of a big data-probability (BD-P) model using a large dataset of 4.27 million truckloads.
  • Derivation of construction waste bulk density probability distribution.
  • Application of the Law of Joint Probability for model construction.

Main Results:

  • The BD-P model achieved 90.2% accuracy and an AUC of 0.8775.
  • Estimation speed was approximately 52 seconds per truckload.
  • An informed linear model demonstrated 88.8% accuracy in 0.4 seconds per case.

Conclusions:

  • The BD-P model effectively estimates construction waste composition, solving a key challenge for waste management operators.
  • This research harmonizes big data and probability theory, demonstrating their synergistic potential.
  • The findings offer a practical and academically significant advancement in predictive waste analysis.