Item 33 - Measure M2 Funding Eligibility 2024 Renewal (Revised)

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2.0 Methodology City of Santa Ana 2024 Pavement Management Program 2-2-2 2.2 PLANNING Preparation prior to performing pavement surveys involves three major components: inspector training, survey type selection, and identifying sample units. • Inspector Training - In order to be familiar with the inspection guidelines, inspectors are prompted to read the StreetSaver Asphalt & Concrete Distress Manuals, complete the web-based Pavement Management software training and learn the data input application for the StreetSaver program prior to inspecting. Additionally, the inspection routes are planned in a time-efficient manner to efficiently capture the pavement data. • Survey Types - There are two types of surveys allowable: “windshield” and “walking” surveys. A windshield survey consists of examining the pavement from a vehicle moving at no more than 10 miles per hour (MPH), while a walking survey allows an inspector to examine from the pavement surface itself. Although, for safety reasons, walking surveys on arterial roadways may be performed from a safe place adjacent to the pavement such as a sidewalk, center median, or shoulder. • Sample Units - Random areas, known as sample units, within a street section are inspected as a representative of the whole segment. A sample unit for an asphalt street section contains a total area of 2500 square feet (SF) ±1000 SF, and a sample unit for a PCC street section contains a total number of 20 slabs ±8 slabs. 2.3 DATA COLLECTION The pavement distresses found within a sample unit are identified by type, classified by severity, measured/counted, and recorded as they are encountered in the field. A number of sample units are randomly selected for re-inspection to assure consistent and accurate results among inspectors. Refer to Appendix D: Quality Assurance/Quality Control Plan for further details on quality control/quality assurance. 2.4 DATA ENTRY The collected pavement distress data is input into StreetSaver, creating an inspection record for each sample unit. 2.5 ANALYSIS Based on the latest inspection data and several other factors, including surface type, functional classifications, last construction date, and PCI, pavement deterioration curves (PDCs) or pavement condition prediction models are generated separately for the local and arterial networks. These curves are imperative for a complete PMP to predict future pavement performance and determine maintenance and rehabilitation (M&R) requirements. Figure 2-1 shows a sample PDC with the appropriate M&R.