Evidence map›Paper›PMID 42828132›Full record

ArticleiScience2026

Assessing machine learning prediction of recycled aggregate concrete strength with a tabular foundation model.

Jin Fan, Roshani Bharati, GangYong Zhang, Hui Zhang, Ning Zhang, YanLing Zhang, AiXia Liang, Mandeep Pokhrel

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2026. 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

8 authors.

Jin FanSchool of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China.
Roshani BharatiOffice of Institutional Research and Effectiveness, Montclair State University, Montclair, NJ 07043, USA.
GangYong ZhangSchool of Chemistry and Environmental Engineering, Wuhan Institute of Technology, Wuhan 430205, China.
Hui ZhangSchool of Chemistry and Environmental Engineering, Wuhan Institute of Technology, Wuhan 430205, China.
Ning ZhangSchool of Mechanics and Construction Engineering, Jinan University, Guangzhou 510632, China.
YanLing ZhangSchool of Mechanics and Construction Engineering, Jinan University, Guangzhou 510632, China.
AiXia LiangDepartment of Architecture and Civil Engineering, Southwest Jiaotong University Hope College, Chengdu, Sichuan 610400, China.
Mandeep PokhrelForensics & Renewal Practice, Thornton Tomasetti, Newark, NJ 07102, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recycled aggregate concrete (RAC) supports more sustainable construction, but recycled concrete aggregate (RCA)'s heterogeneity complicates compressive-strength prediction. This study evaluates the Tabular Prior-data Fitted Network (TabPFN) against nine conventional machine-learning models using a standardized literature-derived database of 833 RAC mixtures. Cube strengths were converted to equivalent cylinder strengths, with conversion sensitivity also assessed. Among conventional models, CatBoost achieved the highest accuracy (R

Indexed as

machine learningrecycled concrete aggregateShapley additive explanationTabPFN regressortabular foundation model

Identifiers

PMID42828132
PMCPMC13631657

What OpenQuestion holds

Textmetadata
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.