Reconstruction of 3D Pavement Texture on Handling Dropouts and Spikes Using Multiple Data Processing Methods

Area of Science:

• Civil Engineering
• Materials Science
• Transportation Engineering

Background:

• Pavement micro-texture significantly impacts tire-pavement interactions, affecting safety, comfort, and sustainability.
• Accurate micro-texture measurement is crucial for pavement performance evaluation.
• Current laser devices often produce data with dropouts and spikes, leading to inaccurate characterization.

Purpose of the Study:

• To develop and validate a method for reconstructing accurate 3D pavement micro-texture data.
• To address data quality issues caused by dropouts and spikes in laser measurements.
• To improve the reliability of pavement texture analysis for engineering applications.

Main Methods:

• Utilized an advanced laser sensor for 3D pavement micro-texture measurement.
• Applied an interpolation method to fill data dropouts.
• Employed Butterworth filters to separate texture components.
• Developed a three-step method to identify and remove spikes based on statistical analysis.
• Reconstructed 3D pavement profiles free from dropouts and spikes.

Main Results:

• Successfully reconstructed 3D pavement profiles by filling dropouts and removing spikes.
• Demonstrated the effectiveness of the proposed interpolation and spike-removal techniques.
• Investigated the impact of spikes on Mean Profile Depth (MPD) calculations.
• Found that spikes inflate MPD values and that MPD is sensitive to baseline length.

Conclusions:

• The proposed data processing method enhances the accuracy of 3D pavement micro-texture characterization.
• A shorter baseline is recommended for MPD calculation to minimize the influence of remaining data imperfections.
• Accurate pavement texture data is essential for optimizing pavement design and maintenance.