Evidence map›Paper›PMID 42198190›Full record

ArticlePolymers2026

Experimental Evaluation and Prediction of the Dynamic Modulus of Crumb Rubber-Modified Stone Mastic Asphalt Mixtures.

Muhammad Irfan, Saif Ullah Khan Wazir, Muhammad Asif Khan, Sarfraz Ahmed, Zain Maqsood

Abstract read
In one paragraph

Article in Polymers, 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
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0citing papers in PubMed
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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

5 authors.

Muhammad IrfanSchool of Civil and Environmental Engineering (SCEE), National University of Sciences & Technology (NUST), Islamabad 44000, Pakistan.ORCID 0000-0002-6511-9892
Saif Ullah Khan WazirMilitary College of Engineering (MCE), National University of Sciences & Technology (NUST), NUST Campus, Risalpur 24080, Pakistan.
Muhammad Asif KhanSchool of Civil and Environmental Engineering (SCEE), National University of Sciences & Technology (NUST), Islamabad 44000, Pakistan.ORCID 0000-0003-2790-8003
Sarfraz AhmedMilitary College of Engineering (MCE), National University of Sciences & Technology (NUST), NUST Campus, Risalpur 24080, Pakistan.ORCID 0000-0002-4504-6171
Zain MaqsoodSchool of Civil and Environmental Engineering (SCEE), National University of Sciences & Technology (NUST), Islamabad 44000, Pakistan.ORCID 0000-0002-2901-0224

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Increased and excessive axle loads (exceeding design specifications) at high temperatures stimulate premature distresses in flexible pavements. This study utilizes the novelty of engineered bituminous composite-crumb rubber-modified (CRM) stone mastic asphalt (SMA) for pavement longevity and sustainable performance. Dynamic modulus testing was employed at four temperatures and six frequency sweeps. The experimental design included the preparation of SMA 19 specimens with six different percentages of crumb rubber (CR) mixed in bitumen. CR addition to the mix translated into an improved stiffness of the mix, as a 64% increase in dynamic modulus (on average) was reported at 10% CR as compared to a neat mixture. Master curves were produced using |E*| test results, which revealed that 10% modified SMA was relatively stiffer and more rut-resistant than the other mixtures. Performance prediction models were developed for |E*| using artificial neural networks (ANNs) and non-linear regression, wherein the former proved to be more robust. Sensitivity analysis revealed that a temperature rise (21.1 to 37.8 °C) translated into a 65% drop in |E*| (on average) and a rise in frequency (0.1 to 25 Hz) divulged a 72% upsurge in |E*| (on average). This research demonstrates the promise of deploying CR SMA mixtures, particularly for high-traffic and heavy-load scenarios.

Indexed as

artificial neural networkscrumb rubberdynamic modulusmaster curvesperformance modelingstone mastic asphalt

Identifiers

PMID42198190
PMCPMC13210916

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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.