Neural Network Based Pavement Condition Assessment with Hyperspectral Images

Hyperspectral image processing techniques, with their ability to provide information about the chemical compositions of materials, have great potential for pavement condition assessment. This study introduces a novel age-based pavement assessment method, employing an integrated algorithm with artificial neural network (ANN) and spectral angle mapping (SAM) on hyperspectral images. In the proposed method, the resulting ANN prediction outputs are used to make a new prediction along with the results from SAM scores. Tests are performed on hyperspectral images that have 360 spectral bands between 400 and 900 nm, collected by a specifically designed vehicular system for proximal image acquisition. The acquired images have eight classes, including three different pavement classes (good (5-year), medium (10-year), and poor (25-year)), yellow dye, white dye, soil, paving stone, and shadow. Several experiments are performed to evaluate the robustness of the followed methodology with limited learning data that include 5, 10, 25, and 50 samples per class, selected randomly from our independent spectral database. For a fair comparison, the individual ANN, SAM, support vector machine (SVM), and stacked auto-encoders (SAE) algorithms are also evaluated. The classification performances of individual ANN and SAM are significantly increased with their joint use, demonstrating a 1.2% to 21% classification accuracy improvement in relation to the training sample size. The study proves that the proposed approach is quite robust in cases wherein few training data are available, while SAE and standard ANN algorithms are more successful in cases wherein more learning data are present.

Rating and Condition Assessment of Urban Roads Based on Functional Distresses

The pavement management system deals with a pavement condition assessment. Rating of pavement can be done on the pavement condition assessment. Structural and functional distress is responsible for the failure of pavements. In this work, significant functional distresses which occur in flexible pavements are considered for the rating and assessment of road sections. The functional distress considered are Raveling, Potholes, Shoving, Patching, Depression, and Rutting as these are common and frequently occurred in the flexible pavements. The study of these distresses is done by authors. The measurement of distresses is done as per guideline given by the Indian Road Congress 1982 is used. For the condition, assessment guideline provided in Maintenance Management of Primary, Secondary, and Urban Roads, IRC, 2004, is used. Total five road section in the Pune region is considered for the study. All are flexible pavements. As Per assessment, it is observed that out of five segments, segment I and II are in fair to good condition, segment III is in very good condition. Segment IV and V are in very good and good condition, respectively.