Vibration comfort is a critical factor in assessing the overall performance of
engineering machinery, with significant implications for operator health and
safety. However, current evaluation methods lack specificity for construction
machinery, impeding accurate prediction of vibration comfort and hindering the
optimization of noise, vibration, and harshness (NVH) performance. To address
this challenge, this article proposes a model that combines a random forest with
a genetic algorithm (GA-RF) to enable rapid and accurate prediction of vibration
comfort in construction machinery cabins. The approach begins with an improved
objective evaluation methodology for extracting key features from vibration
signals at five measurement points: seat, floor, back, and left and right
armrests. Additionally, subjective evaluation technology, combining semantic
differential and rating scales, is employed to capture operators’ personal
comfort perceptions. The implementation of the GA-RF model constructs a
nonlinear mapping between vibration characteristics and perceived comfort,
significantly enhancing the precision and efficiency of the vibration comfort
evaluation process. Testing indicates that the objective evaluation method
effectively refines vibration data features relevant to practical engineering
applications. The proposed GA-RF model demonstrates robust predictive
capabilities. These results provide valuable insights for the evaluation and
enhancement of vibration comfort in the engineering machinery sector, laying a
substantial foundation for future research and application.
