Optimization of pavement texture depth measurement using machine learning algorithms

Mohammad Fahad

doi:10.1007/s42452-026-08392-9

February 23, 2026Discover Applied SciencesOpen Access

Optimization of pavement texture depth measurement using machine learning algorithms

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Key Points

The research aims to enhance the accuracy of pavement texture depth measurements through machine learning techniques.
Used 3D laser scanning for measurement
Applied paired t-tests and ANOVA for data analysis
Employed machine learning algorithms for depth prediction
Separated macrotexture and microtexture using Power Spectral Density
Improved measurement accuracy of pavement texture depth
Successful separation of macrotexture and microtexture studies
Demonstrated the effectiveness of machine learning in predicting pavement depth

Implication

Optimization research predicts pavement texture depth, suggesting improved measurement methods using technology.

Abstract

Optimization of 3D laser scanning method using paired t-tests and ANOVA tests. Prediction of pavement mean texture depth using machine learning algorithms. Separation of macrotexture and microtexture using Power Spectral Density method.

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Cite This Study

Mohammad Fahad (2026) studied this question.

synapsesocial.com/papers/699fe24b95ddcd3a253e62b8 https://doi.org/10.1007/s42452-026-08392-9

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Optimization of pavement texture depth measurement using machine learning algorithms

Key Points

Implication

Abstract

Discussion

Cite This Study

Optimization of pavement texture depth measurement using machine learning algorithms

Key Points

Implication

Abstract

Discussion

Cite This Study

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