Machine Learning-Driven Prediction and Design Guidance for Asphalt Concrete Using Marshall Stability and Indirect Tensile Strength.

Jianglei Xing, Xiao Tan, Mu Guo, Pengwei Guo, Yuhuan Wang, Dongzhao Jin

Abstract read

In one paragraph

Article in Materials (Basel, Switzerland), 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

6 authors.

Jianglei XingCollege of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210024, China.ORCID 0009-0005-4591-066X

Xiao TanCollege of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210024, China.ORCID 0000-0002-3552-6163

Mu GuoShanghai Research Institute of Building Science Co., Ltd., Shanghai 201108, China.

Pengwei GuoBartlett School of Sustainable Construction, University College London, London WC1E 6BT, UK.

Yuhuan WangDepartment of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder, Boulder, CO 80309, USA.

Dongzhao JinDepartment of Civil, Environmental, and Geospatial Engineering, Michigan Technological University, Houghton, MI 49931, USA.ORCID 0000-0003-0153-5501

Funding

Basic Research Program of Jiangsu province BK20251486Fundamental Research Funds for the Central Universities B250201004National Natural Science Foundation of China 52508343

6 · The paper itself

Abstract

This work addresses the simultaneous prediction of Marshall Stability (MS) and Indirect Tensile Strength (ITS) by integrating machine learning models with multi-objective optimization for the preliminary design of asphalt concrete. Based on 389 experimental samples, 15 variables were selected to describe asphalt properties, aggregate gradation, volumetric parameters and fiber characteristics, and four dual-output prediction models were developed. The models were evaluated using 50 Monte Carlo splits. TabICLv2 performed slightly better for MS prediction, with an RMSE of 1.49 ± 0.22 kN and an R

Indexed as

asphalt concreteindirect tensile strengthmachine learningMarshall stabilityPareto filteringTOPSIS ranking

Identifiers

PMID42654561

DOI10.3390/ma19163408

PMCPMC13514947

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.