ArticleScientific reports2025
A convolutional neural network-based deep learning approach for predicting surface chloride concentration of concrete in marine tidal zones.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Machine learning prediction of compressive strength in 3d printed fiber reinforced concrete using support vector regression and artificial neural networks with shapley additive explanations.Scientific reports · 2026Article
- Prediction of Chloride Penetration Depth in Concrete Using a Combined Ensemble-Neural Network Architecture: Facing Data Saturation.Materials (Basel, Switzerland) · 2026Article
- Toward sustainable energy production: a comparative machine learning framework for predicting green hydrogen cost across the african continent.Scientific reports · 2026Article
- Predicting unconfined compressive strength of geopolymer-stabilized clays using a sector fruit fly-based extreme learning machine.Scientific reports · 2026Article
- Application of swarm-based deep neural networks and ensemble models for reconstruction of specific conductance data.Scientific reports · 2026Article
- Sustainable grout for structural repairs incorporating waste master board and fiber: experimental study and RSM-based evaluation.Scientific reports · 2026Article
- Machine learning based optimization of fly ash content for improving geopolymer concrete compressive strength.Scientific reports · 2025Article
- Microstructural, mechanical characterization and ANN prediction of concrete with glass fiber roving waste.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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chloride-induced corrosion is a major threat to the durability of reinforced concrete (RC) structures. This is especially critical in marine tidal zones, where surface chloride concentration (Cs) plays a key role in predicting chloride ingress using Fick's second law. However, traditional assessment methods are time-consuming and impractical, necessitating advanced predictive models. This study developed a deep learning-based framework utilizing a convolutional neural network (CNN) trained on 284 samples with 11 critical features related to material composition and environmental conditions. The CNN's performance was benchmarked against four machine learning (ML) models: stepwise linear regression (SLR), support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF). Results demonstrated CNN's superiority, achieving a coefficient of determination (R
Indexed as
Identifiers
What OpenQuestion holds
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.