ArticleMaterials (Basel, Switzerland)2024
Bond Strength Assessment of Normal Strength Concrete-Ultra-High-Performance Fiber Reinforced Concrete Using Repeated Drop-Weight Impact Test: Experimental and Machine Learning Technique.
Article in Materials (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Static and Dynamic Performance of Steel-Fiber-Reinforced Polymer-Modified Concrete: Strength, Toughness and Crack Resistance.Materials (Basel, Switzerland) · 2026Article
- Special Issue: Innovative Material Design and Nondestructive Testing Applications for Infrastructure Materials.Materials (Basel, Switzerland) · 2025Article
- The Application of Machine Learning Algorithms to Bond Strength between Steel Rebars and Concrete Using Bayesian Optimization.Materials (Basel, Switzerland) · 2024Article
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Authors and funding
5 authors.
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Abstract
Ultra-high-performance concrete (UHPC) has been used in building joints due to its increased strength, crack resistance, and durability, serving as a repair material. However, efficient repair depends on whether the interfacial substrate can provide adequate bond strength under various loading scenarios. The objective of this study is to investigate the bonding behavior of composite U-shaped normal strength concrete-ultra-high-performance fiber reinforced concrete (NSC-UHPFRC) specimens using multiple drop-weight impact testing techniques. The composite interface was treated using grooving (Gst), natural fracture (Nst), and smoothing (Sst) techniques. Ensemble machine learning (ML) algorithms comprising XGBoost and CatBoost, support vector machine (SVM), and generalized linear machine (GLM) were employed to train and test the simulation dataset to forecast the impact failure strength (
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