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Paper

A Comparative Analysis of Machine Learning Models in Prediction of Mortar Compressive Strength

by Independent / Community 003b97f76e7bf1ee9c95acd289633060654a39e0

Free2AITools Nexus Index

69.4

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 85

P: Popularity 61

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

Predicting the mechanical properties of cement-based mortars is essential in understanding the life and functioning of structures. Machine learning (ML) algorithms in this regard can be especially useful in prediction scenarios. In this paper, a comprehensive comparison of nine ML algorithms, i.e., linear regression (LR), random forest regression (RFR), support vector regression (SVR), AdaBoost regression (ABR), multi-layer perceptron (MLP), gradient boosting regression (GBR), decision tree r...

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Semantic Scholar 69 Citations

Paper Information Summary
Entity Passport
Registry ID	003b97f76e7bf1ee9c95acd289633060654a39e0
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{003b97f76e7bf1ee9c95acd289633060654a39e0,
  author = {Unknown},
  title = {A Comparative Analysis of Machine Learning Models in Prediction of Mortar Compressive Strength Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/003b97f76e7bf1ee9c95acd289633060654a39e0}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). A Comparative Analysis of Machine Learning Models in Prediction of Mortar Compressive Strength [Paper]. Free2AITools. https://api.semanticscholar.org/003b97f76e7bf1ee9c95acd289633060654a39e0

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⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 85

Popularity (P) 61

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for A Comparative Analysis of Machine Learning Models in Prediction of Mortar Compressive Strength: Authority (A:85), Popularity (P:61), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"Predicting the mechanical properties of cement-based mortars is essential in understanding the life and functioning of structures. Machine learning (ML) algorithms in this regard can be especially useful in prediction scenarios. In this paper, a comprehensive comparison of nine ML algorithms, i.e., linear regression (LR), random forest regression (RFR), support vector regression (SVR), AdaBoost regression (ABR), multi-layer perceptron (MLP), gradient boosting regression (GBR), decision tree r..."

❝ Cite Node

@article{Unknown2026A,
  title={A Comparative Analysis of Machine Learning Models in Prediction of Mortar Compressive Strength},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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📊 Research Signals

📈69CitationsSemantic Scholar

🏛️85AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

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🏷️ Research Topics

vector databases

📦Data Source: semantic_scholar

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🆔 Identity & Source

source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
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downloads: 0
stars: null
forks: null
citations: 69

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