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Papers
61,005 resultsShowing papers similar to A data-driven approach for the assessment of the thermal stratification of reservoirs based on readily available data
ClearDrinking water potability prediction using machine learning approaches: a case study of Indian rivers
Researchers applied machine learning techniques to predict drinking water quality in Indian rivers based on key parameters like pH, dissolved oxygen, and bacterial counts. Their models achieved high accuracy in classifying water as potable or non-potable. The study demonstrates how data-driven approaches could help developing countries monitor water safety more efficiently, especially in regions where traditional testing infrastructure is limited.
Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges
This review covers machine learning methods applied to predicting and understanding mechanical properties of materials from large datasets. It is an engineering informatics paper and is not related to microplastics or environmental health.
Thermo‐based fatigue life prediction: A review
Not relevant to microplastics — this review covers thermography-based methods for predicting the fatigue life of metals under cyclic stress, with no connection to plastic pollution or environmental health.
Water Quality Monitoring And Ground Water Level Prediction Using Machine Learning
Researchers applied machine learning techniques to water quality monitoring and groundwater level prediction, demonstrating the potential of data-driven approaches for environmental sensing and resource management.
Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials
Researchers used machine learning and Bayesian network analysis on 4D microscopy data from cracking metal samples to identify which microstructural features best predict how small fatigue cracks grow and in which direction. The resulting analytical model outperformed existing fatigue metrics, offering a more accurate tool for predicting when and how structural metal components will fail under repeated stress.
Fatigue Failure Assessment in Ultrasonic Test Based on Temperature Evolution and Crack Initiation Mechanisms
This study examined how temperature changes and crack formation can be used to detect fatigue failure in materials during ultrasonic testing. Researchers found that thermal imaging can identify fatigue damage earlier than conventional methods. The work advances non-destructive testing techniques for structural materials.
Rapid estimation of fatigue limit for C45 steel by thermography and digital image correlation
This materials engineering study used thermography and digital image correlation to rapidly estimate the fatigue limit of steel, linking temperature and mechanical changes to the onset of microplastic deformation in metal. It is a mechanical engineering paper not related to environmental microplastics.
Energy Dissipation Measurement in Improved Spatial Resolution Under Fatigue Loading
This engineering study used infrared thermography to measure energy dissipation in materials under fatigue loading to quickly predict a material's failure threshold. It is a materials science paper unrelated to environmental microplastics.
Loading Frequency Classification in Shape Memory Alloys: A Machine Learning Approach
Researchers applied machine learning methods to predict the loading frequency of nickel-titanium shape memory alloys based on experimental data from cyclic tensile tests. They tested multiple algorithms across different loading frequencies and found that machine learning could effectively classify the frequency conditions. The study demonstrates the potential of data-driven approaches for characterizing the behavior of these materials used in engineering applications.
Fatigue-Limit Assessment via Infrared Thermography for a High-Strength Steel
Despite its title referencing infrared thermography, this paper tests whether thermal imaging techniques can accurately assess the fatigue limits of high-strength steel under cyclic stress loading — not microplastic pollution. It examines materials engineering for metal fatigue testing and is not relevant to microplastics or human health.
Prediction of pore-scale flow in heterogeneous porous media from periodic structures using deep learning
Researchers developed a method to predict pore-scale fluid flow in heterogeneous porous media from periodic structural data, improving computational modeling of transport through complex materials. The approach has applications in understanding how microplastics and other particles move through soils, sediments, and filtration systems.
Suggesting a Stochastic Fractal Search Paradigm in Combination With Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings
Researchers developed a machine learning method combining neural networks and stochastic optimization to predict cooling energy loads in residential buildings. This engineering modeling paper is unrelated to microplastic research.
Dissipative aspects in thermographic methods
This engineering paper developed improved thermographic methods to detect the fatigue limit of steel by measuring tiny temperature changes during cyclic loading, correlating these signals with microplastic deformation at the crystal level. This is a materials engineering study with no relevance to environmental microplastics.
Coupling life prediction of bending very high cycle fatigue of completion strings made of different materials using deep wise separable convolution
Not relevant to microplastics — this study uses deep learning to predict the fatigue life of nickel-based alloy completion strings used in oil well engineering, with no connection to microplastic pollution.
Dam Sustainability’s Interdependency with Climate Change and Dam Failure Drivers
Researchers examined the interplay between dam failure drivers and climate change factors, analysing how variations in temperature and precipitation patterns affect dam sustainability and failure risk. The study found that changing climate conditions interact with structural, hydrological, and operational failure drivers in ways that require updated risk assessment frameworks for dam management.
Machine Learning‐Driven Discovery of Thermoset Shape Memory Polymers With High Glass Transition Temperature Using Variational Autoencoders
A machine learning framework was developed to discover thermoset shape memory polymers with high glass transition temperatures, enabling their use in extreme high-temperature applications such as geothermal energy and aerospace. The approach rapidly identified candidate polymer formulations that outperformed traditionally synthesized materials.
Fatigue limit estimation of metals based on the thermographic methods: A comprehensive review
This review covers 30 years of research on using infrared thermography to rapidly estimate the fatigue limits of metals. Researchers found that thermal imaging can detect the heat signatures produced during mechanical fatigue, offering a faster alternative to traditional fatigue testing. The study provides a comprehensive comparison of different thermographic approaches, outlining their strengths, limitations, and open questions for future research.
Rapid Fatigue Limit Estimation of Metallic Materials Using Thermography-Based Approach
This paper is not about environmental microplastics; it uses the term "microplastic" in a materials science context to describe microscopic plastic deformation in metals during fatigue testing.
A Strategy for Dimensionality Reduction and Data Analysis Applied to Microstructure–Property Relationships of Nanoporous Metals
This materials science study applied machine learning to predict the mechanical properties of nanoporous metals from their microstructural features, offering an efficient way to optimize material design. While focused on metals rather than plastics, similar data-driven approaches are being developed for predicting the environmental behavior of microplastics.
Modeling of daily groundwater level using deep learning neural networks
Researchers applied a CNN-biLSTM deep learning model to predict daily groundwater levels, finding it outperformed conventional modeling approaches by capturing both spatial patterns and temporal dependencies in the data. The method offers improved accuracy for groundwater monitoring, which is critical for managing increasingly stressed freshwater resources.
Machine Learning to Access and Ensure Safe Drinking Water Supply: A Systematic Review
This systematic review examines machine learning applications for monitoring, predicting, and controlling drinking water quality, covering contaminants from disinfection byproducts to biofilms and antimicrobial resistance genes. While not specifically about microplastics, the ML approaches described are directly applicable to detecting and predicting microplastic contamination in engineered water systems.