A signal processing algorithm based on the principle of support vector machines as well as the analysis to the characteristics of road surface diseases is proposed to detect pavement disease. Measurements from vehicle-mounted sensors (e.g., accelerometers and Global Positioning System (GPS) receivers) are properly combined to produce higher quality road roughness data for road surface condition...

Modeling pavement performance by combining field and experimental data The accurate prediction of pavement performance is important for efficient management of the surface transportation infrastructure. By reducing the error of the pavement deterioration prediction, agencies can obtain significant budget savings through timely intervention and accurate planning. The goal of this research was to...

Accelerometers embedded in smartphones have become an alternative means of measuring the roughness roads. However, differences their sensitivity and sampling rates between could produce measurement inconsistencies that challenge wide spread smartphone approach for road measurements. In this study, inconsistency was investigated from three different brands. Using same vehicle, device mount metho...

Pavement condition largely determines its long-term behavior and is of paramount importance for rehabilitation maintenance management. The use non-destructive testing (NDT) systems to assess pavement has gained much popularity. Often, well-known NDT are combined take full advantage the capabilities each system. Combining independent optimize assessment process a scientific challenge. With this ...