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Water-reducers, or plasticizers, are chemical admixtures used in concrete to improve strength and workability. These additives reduce the water-cement ratio without compromising workability, lower the cement content while maintaining the same workability, or increase workability to assist concrete placement in inaccessible areas.
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Related Experiment Video

Updated: Jan 26, 2026

Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris
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Identifying floating plastic marine debris using a deep learning approach.

Kyriaki Kylili1, Ioannis Kyriakides1, Alessandro Artusi2

  • 1Marine & Carbon Lab, Department of Engineering, University of Nicosia, 46 Makedonitissas Avenue, CY-2417, Nicosia, Cyprus.

Environmental Science and Pollution Research International
|April 20, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning method to automatically detect floating marine plastics like bottles, buckets, and straws. This fast and scalable approach offers a more efficient way to estimate ocean plastic pollution.

Keywords:
Convolutional Neural NetworksData processingDeep learningImage classificationMarine debrisMonitoringPlastics

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Area of Science:

  • Environmental Science
  • Computer Science
  • Marine Biology

Background:

  • Oceanic macro-plastic pollution is a significant global environmental issue.
  • Current methods for quantifying floating plastic debris are manual, time-consuming, and limited in scope.
  • There is a critical need for efficient and scalable solutions to monitor marine plastic pollution.

Purpose of the Study:

  • To develop a fast, scalable, and cost-effective automated method for identifying floating marine plastics using deep learning.
  • To assess the accuracy and efficiency of a machine learning classifier trained on common plastic litter categories.

Main Methods:

  • A deep learning model was developed and trained to classify images of floating marine plastics.
  • The classifier was trained on three categories: bottles, buckets, and straws.
  • Performance was evaluated based on recognition accuracy and efficiency.

Main Results:

  • The deep learning classifier achieved a success rate of approximately 86% in recognizing floating plastic objects.
  • The developed method demonstrates high accuracy and efficiency in identifying marine plastic litter.
  • The tool can significantly reduce the time and resources required for plastic debris estimation.

Conclusions:

  • The proposed machine learning tool represents a significant advancement in the automated detection of floating marine plastics.
  • This technology offers a scalable and potentially cost-effective solution for monitoring ocean plastic pollution.
  • The findings pave the way for a more comprehensive understanding of the true scale of marine plastic debris.