Machine learning-based soft-sensor development for road quality classification

Nagy Roland, Alex Kummer, János Abonyi, István Szalai

Journal of Vibration and Control 2023 article cited by 14

Source Open-access PDF More in Engineering & Materials

importance 4/10 ai assisted Structural Health Monitoring Techniques road infrastructure monitoring stage: research

Problem

Road defects cause health, safety, and vehicle-damage costs, so a low-cost onboard system was built to detect and classify road quality.

Why it matters

Early detection of road defects reduces passenger health problems and mechanical damage to vehicles.

What the AI did

A multi-sensor system (accelerometers, gyroscopes) collected vibration data, which was noise-filtered, resampled by spatial frequency, and classified by a trained decision-tree model into four road-quality classes.

Results

System classifies road sections into four quality classes; accuracy metrics not stated in source (abstract truncated)

Impact

A working ML-based soft-sensor for road monitoring is described, but quantitative performance is not shown in the abstract.

Record

Venue	Journal of Vibration and Control
Published	2023
Work type	article
Authors	Nagy Roland, Alex Kummer, János Abonyi, István Szalai
Citations	14 (OpenAlex, at import time)
References	28
Model or method	Decision tree-based classification model
Organization	not stated in source
Primary source	https://doi.org/10.1177/10775463231183307
Open access	bronze
Keywords	road quality, soft sensor, machine learning, decision tree, vibration monitoring

Importance rating

4 / 10

real but narrower advance

Road defects cause stated health, safety, and financial harms motivating monitoring, but the abstract gives no scale and the system is a low-cost sensing advance of fairly narrow scope.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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