Pavement-management systems (PMS) can work effectively only when they are constructed by organically combining all activities concerned with road pavement (planning, design, construction, maintenance, rehabilitation, evaluation, economic analysis, and research) and the data bank . Then, the most important items are the establishment of a serviceability index, which represents pavement quality, and a prediction of performance, which is represented by the relation between time (and/or traffic) and the index. Pavement quality consists of two primary factors: riding quality and skid resistance. The factors influencing riding quality are pavement distress and/or roughness. Three major factors ...view middle of the document...
The better the macro texture is, the smaller the slope of the friction coefficient-speed function . Macro texture is also a predominant contribution to wet-pavement safety and a coarse macro texture is desirable for safe wet-weather travel as the speed increases. Therefore, measuring the macro texture is one of the essential components in pavement management applications.
The requirements for acquiring these factors are the following: (1) That data-acquisition cost is as cheap as possible; (2) that data analysis can be done in a short time; and (3) that data acquisition doesn't influence the speed of other traveling vehicles, in particular on roads with heavy traffic .
Pavement surface distress measurement is an essential part of a pavement management system (PMS) for determining cost-effective maintenance and rehabilitation strategies. Visual surveys conducted by engineers in the field are still the most widely used means to inspect and evaluate pavements, although such evaluations involve high degrees of subjectivity, hazardous exposure, and low production rates. Consequently, automated distress identification is gaining wide popularity among transportation agencies . With the advancement of 3D sensor and information technology, a high-resolution, high-speed 3D line laser imaging system has become available for pavement surface condition data collection. With the advances in sensor technology, a 3D line-laser-imaging-based pavement surface data acquisition system has become available. The Laser Crack Measurement System (LCMS)  can collect high-resolution 3D continuous pavement profiles for constructing pavement surfaces. The objective of this paper is to validate the capability of 3D laser pavement data to detect cracks in support of subsequent crack classification . The paper is organized as follows. This section reviews related research on automated pavement crack surveying and identifies the objective of this study. Methods and materials briefly introduce the 3D line-laser-imaging system for pavement data collection.
The LCMS is composed of two high performance 3D laser profilers that are able to measure complete transverse road profiles with 1mm resolution at highway speeds. The high resolution 2D and 3D data acquired by the LCMS is then processed using algorithms that were developed to automatically extract crack data including crack type (transverse, longitudinal, alligator) and severity. Also detected automatically are ruts (depth, type), macro-texture (digital sand patch) and raveling (loss of aggregates).
The sensors used with the LCMS system are 3D laser profilers that use high power laser line projectors, custom filters and a camera as the detector. The light stripe is projected onto the pavement and its image is captured by the camera. The shape of the pavement is acquired as the inspection vehicle travels along the road using a signal from an odometer to synchronize the sensor acquisition. All...