ArticleScientific reports2024
Machine learning-based corrosion rate prediction of steel embedded in soil.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Machine Learning for Structural Steels: Materials Design, Property Prediction, Durability, and Future Directions.Materials (Basel, Switzerland) · 2026Review
- A Large-Scale Analysis of Corrosion Data in High-Sulfur Natural Gas Purification Systems.ACS omega · 2026Article
- Prediction and Analysis of Creep Rupture Life of 9Cr Martensitic-Ferritic Heat-Resistant Steel by Neural Networks.Materials (Basel, Switzerland) · 2026Article
- Crossing scales and eras: Correlative multimodal microscopy heritage studies.Journal of microscopy · 2026Article
- Data-driven assessment of corrosion in reinforced concrete structures embedded in clay dominated soils.Scientific reports · 2025Article
- A machine learning approach for corrosion rate modeling in Patna water distribution network of Bihar.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Predicting the corrosion rate for soil-buried steel is significant for assessing the service-life performance of structures in soil environments. However, due to the large amount of variables involved, existing corrosion prediction models have limited accuracy for complex soil environment. The present study employs three machine learning (ML) algorithms, i.e., random forest, support vector regression, and multilayer perception, to predict the corrosion current density of soil-buried steel. Steel specimens were embedded in soil samples collected from different regions of the Wisconsin state. Variables including exposure time, moisture content, pH, electrical resistivity, chloride, sulfate content, and mean total organic carbon were measured through laboratory tests and were used as input variables for the model. The current density of steel was measured through polarization technique, and was employed as the output of the model. Of the various ML algorithms, the random forest (RF) model demonstrates the highest predictability (with an RMSE value of 0.01095 A/m
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.