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Machine learning predicts asphalt concrete strength using SHAP analysis

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a machine learning framework to predict the splitting strength of asphalt concrete, utilizing 296 samples and 14 input variables. Six models were compared, with TabPFN demonstrating the best performance, achieving an R^2 of 0.88 and a composite score of 0.91. SHAP analysis identified key variables influencing the strength, and optimal parameter ranges were quantified to improve the material's performance. A graphical user interface was also created to make the framework more accessible. AI

IMPACT This research demonstrates the application of advanced machine learning techniques for material science, potentially improving construction material design and performance.

RANK_REASON The item is an academic paper detailing a machine learning model for a specific engineering application. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Machine learning predicts asphalt concrete strength using SHAP analysis

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The item is an academic paper detailing a machine learning model for a specific engineering application. [lever_c_demoted from research: ic=1 ai=0.7]
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53 days old
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jianglei Xing, Xiao Tan, Dongzhao Jin, Pengwei Guo, Yuhuan Wang, Huiya Niu ·

    Interpretable machine learning for predicting splitting strength of asphalt concrete: insights from SHAP analysis

    arXiv:2608.00956v1 Announce Type: new Abstract: This paper presents an interpretable machine-learning framework for predicting the splitting strength (ST) of asphalt concrete and supporting data-driven mixture design. A database consisting of 296 samples was established, and 14 i…

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