Bumps and sags cracking Durability (“D”) Corrugation Faulting Depression Joint seal damage Edge cracking Lane/shoulder drop-off Joint reflection cracking Linear cracking Lane/shoulder drop-off Large patching and utility cuts Longitudinal and transverse cracking Small patching Patching and utility cut patching Polished aggregate Polished aggregate Popouts Potholes Pumping Railroad crossing Punchout Rutting Railroad crossing Shoving Shrinkage cracks Slippage cracking Scaling, map cracking, and crazing Swell Corner spalling Weathering and raveling Joint spalling Pavement Condition Surveys Pavement condition data is typically collected using two primary methods: manual and automated. In addition, with automated data collection, data is analyzed using either or semi-automated procedures (Zimmerman et al. 2011). Manual surveys—include personnel either walking or driving at slow speeds to visually assess the pavement condition. Manual surveys do not require specialized equipment and are capable of providing very detailed distress type, severity, and quantity information; however, data collection is slow and fairly labor intensive. Manual surveys, due to the slow speed of data collection, can increase the potential for safety hazards to both the data collection team and the traveling public. Raters for manual surveys need to be trained to assure adequate data quality. Some agencies require certification. automated equipment that uses imaging and/or noncontact Automated surveys—include sensors for quantifying pavement distress (i.e., surface cracking, surface texture characteristics, roughness, rutting, and faulting). Automated surveys can be conducted at or near traffic speeds reducing the exposure of the data collection team and minimizing the impact to the traveling public. Fully automated processing—distress identification (type, severity, and extent) that requires no human intervention and uses software to interpret differences in grayscale for identifying cracks and other surface irregularities. Semi-automated processing—distress identification (type and severity) that requires human intervention for quantifying distress type and severity. Analysis is conducted using specialized software and computer workstations. Factors Influencing Pavement Condition Surveys The following provides a brief summary of current and/or upcoming changes that may influence the way agencies are conducting or analyzing pavement condition surveys (Zimmerman et al. 2011). Preventive maintenance treatments—most pavement condition surveys do not incorporate the types of distress that normally trigger preventive maintenance treatments,
Bumps and sags Corrugation Depression Edge cracking Joint reflection cracking Lane/shoulder drop-off Longitudinal and transverse cracking Patching and utility cut patching Polished aggregate Potholes Railroad crossing Rutting Shoving Slippage cracking Swell Weathering and raveling Durability (“D”) cracking Faulting Joint seal damage Lane/shoulder drop-off Linear cracking Large patching and utility cuts Small patching Polished aggregate Popouts Pumping Punchout Railroad crossing Shrinkage cracks Scaling, map cracking, and crazing Corner spalling Joint spalling Pavement Condition Surveys Pavement condition data is typically collected using two primary methods: manual and automated. In addition, with automated data collection, data is analyzed using either or semi-automated procedures (Zimmerman et al. 2011). Manual surveys—include personnel either walking or driving at slow speeds to visually assess the pavement condition. Manual surveys do not require specialized equipment and are capable of providing very detailed distress type, severity, and quantity information; however, data collection is slow and fairly labor intensive. Manual surveys, due to the slow speed of data collection, can increase the potential for safety hazards to both the data collection team and the traveling public. Raters for manual surveys need to be trained to assure adequate data quality. Some agencies require certification. Automated surveys—include automated equipment that uses imaging and/or noncontact sensors for quantifying pavement distress (i.e., surface cracking, surface texture characteristics, roughness, rutting, and faulting). Automated surveys can be conducted at or near traffic speeds reducing the exposure of the data collection team and minimizing the impact to the traveling public. Fully automated processing—distress identification (type, severity, and extent) that requires no human intervention and uses software to interpret differences in grayscale for identifying cracks and other surface irregularities. Semi-automated processing—distress identification (type and severity) that requires human intervention for quantifying distress type and severity. Analysis is conducted using specialized software and computer workstations. Factors Influencing Pavement Condition Surveys The following provides a brief summary of current and/or upcoming changes that may influence the way agencies are conducting or analyzing pavement condition surveys (Zimmerman et al. 2011). Preventive maintenance treatments—most pavement condition surveys do not incorporate the types of distress that normally trigger preventive maintenance treatments,
12%20Pavement%20Management.docx