Prediction of Compressive Strength of Concrete Using Explainable Machine Learning Models.

Hainan Fu, Xiong Zhou, Pengfei Xu, Dandan Sun

Abstract read

In one paragraph

Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Hainan FuShield and Underground Engineering Research Institute, China Railway 15th Bureau Group Co., Ltd., Shanghai 200070, China.

Xiong ZhouSchool of Engineering and Technology, China University of Geosciences, Beijing 100083, China.

Pengfei XuShield and Underground Engineering Research Institute, China Railway 15th Bureau Group Co., Ltd., Shanghai 200070, China.

Dandan SunShield and Underground Engineering Research Institute, China Railway 15th Bureau Group Co., Ltd., Shanghai 200070, China.

Funding

National Key R & D Program of China 2023YFC3707804Shanghai Municipal Science and Technology Commission 24QB2708000

6 · The paper itself

Abstract

Predicting the compressive strength of concrete is essential for engineering design and quality assurance. Traditional empirical formulas often fall short in capturing complex multi-factor interactions and nonlinear relationships. This study employs an interpretable machine learning framework using Gradient Boosting Trees, Random Forest, and Backpropagation Neural Networks to predict concrete compressive strength. Bayesian optimization was employed for hyperparameter tuning, and SHAP analysis was used to quantify feature contributions. Based on 223 sets of compression test data, this study systematically compared the predictive performance of the five models. Results demonstrate that the CatBoost model achieved the best results, R

Indexed as

Bayesian optimizationconcrete compressive strengthmachine learningmix proportion optimizationSHAP analysis

Identifiers

PMID41227967

DOI10.3390/ma18215009

PMCPMC12608167

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

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.