From Processing to Performance: A Multi-Scale Machine Learning Framework for SBS-Modified Asphalt Gels with Multi-Target Correlation and Explainable AI.

Zongyuan Wu, Xiaoyu Ma, Mengxin Qiu, Chenze Fang, Weizhan Liu, Decai Wang

Abstract read

In one paragraph

Article in Gels (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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Zongyuan WuSchool of Civil Engineering and Communication, North China University of Water Resources and Electric Power, Zhengzhou 450045, China.

Xiaoyu MaSchool of Civil Engineering and Communication, North China University of Water Resources and Electric Power, Zhengzhou 450045, China.

Mengxin QiuSchool of Civil Engineering and Communication, North China University of Water Resources and Electric Power, Zhengzhou 450045, China.

Chenze FangSchool of Civil Engineering and Communication, North China University of Water Resources and Electric Power, Zhengzhou 450045, China.

Weizhan LiuSchool of Civil Engineering and Communication, North China University of Water Resources and Electric Power, Zhengzhou 450045, China.

Decai WangSchool of Civil Engineering and Communication, North China University of Water Resources and Electric Power, Zhengzhou 450045, China.

Funding

the Key Scientific Research Project Plan of Colleges and Universities in Henan Province 24A560010the Key Scientific Research Project Plan of Colleges and Universities in Henan Province 26B58002the Natural Science Foundation of Henan Province 242300421458the Natural Science Foundation of Henan Province 262300421967the Open Fund Project of the Provincial Engineering Research Center for New Energy Vehicle Intelligent Control and Simulation Test Technology of Sichuan XNYQ2025-002

6 · The paper itself

Abstract

Styrene-butadiene-styrene (SBS)-modified asphalt is a physical polymer gel system in which SBS forms a three-dimensional elastic network within the asphalt matrix. This network structure governs the rheological and mechanical properties of the material, yet the quantitative relationships among processing parameters, material composition, microstructure, and macroscopic performance remain insufficiently understood. This study proposes a multi-scale machine learning framework to establish processing-composition-structure-performance mappings for SBS-modified asphalt gels. A dataset of 1072 experimental samples was compiled from a gene database and supplementary laboratory tests. Ten input features were used to predict four performance indicators: penetration, softening point, ductility, and viscosity at 135 degrees Celsius. Four machine learning models were developed and compared. The support vector machine with radial basis function kernel achieved the highest accuracy for penetration with an R

Indexed as

machine learningmaterials genemulti-target correlationpolymer gelSBS-modified asphalt

Identifiers

PMID42794345

DOI10.3390/gels12090795

PMCPMC13606412

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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.