ArticleMaterials (Basel, Switzerland)2022
New SHapley Additive ExPlanations (SHAP) Approach to Evaluate the Raw Materials Interactions of Steel-Fiber-Reinforced Concrete.
Article in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Autonomous sampling and SHAP interpretation of deposition-rates in bipolar HiPIMS.Digital discovery · 2026Article
- Combined radiomics, PI-RADS, and clinical model improve significant prostate cancer prediction and guide biopsy decision.Insights into imaging · 2026Article
- Exploring the influencing factors of abdominal aortic calcification events in chronic kidney disease (CKD) and non-CKD patients based on interpretable machine learning methods.International urology and nephrology · 2025Article
- A machine learning model exploring the relationship between chronic medication and COVID-19 clinical outcomes.International journal of clinical pharmacy · 2025Article
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- Computational Modeling of Properties of Quantum Dots and Nanostructures: From First Principles to Artificial Intelligence (A Review).Nanomaterials (Basel, Switzerland) · 2025Review
- Construction and interpretation of tobacco leaf position discrimination model based on interpretable machine learning.Frontiers in plant science · 2025Article
- Design rules applied to silver nanoparticles synthesis: A practical example of machine learning application.Computational and structural biotechnology journal · 2024Article
- Understanding predictions of drug profiles using explainable machine learning models.BioData mining · 2024Article
- Investigating the Effect of Processing and Material Parameters of Alginate Dialdehyde-Gelatin (ADA-GEL)-Based Hydrogels on Stiffness by XGB Machine Learning Model.Bioengineering (Basel, Switzerland) · 2024Article
- Prediction of in-hospital mortality risk for patients with acute ST-elevation myocardial infarction after primary PCI based on predictors selected by GRACE score and two feature selection methods.Frontiers in cardiovascular medicine · 2024Article
- Explainable Artificial Intelligence to Investigate the Contribution of Design Variables to the Static Characteristics of Bistable Composite Laminates.Materials (Basel, Switzerland) · 2023Article
- Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations.PloS one · 2023Article
- Deep neural network for discovering metabolism-related biomarkers for lung adenocarcinoma.Frontiers in endocrinology · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Recently, artificial intelligence (AI) approaches have gained the attention of researchers in the civil engineering field for estimating the mechanical characteristics of concrete to save the effort, time, and cost of researchers. Consequently, the current research focuses on assessing steel-fiber-reinforced concrete (SFRC) in terms of flexural strength (FS) prediction by employing delicate AI techniques as well as to predict the raw material interaction that is still a research gap. In this study, the FS of SFRC is estimated by deploying supervised machine learning (ML) techniques, such as DT-Gradient Boosting, DT-XG Boost, DT-AdaBoost, and DT-Bagging. In addition to that, the performance model is also evaluated by using R
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