DOCSLIB.ORG

Pavement Data Collection, Rating History & Pavement Management Program in the City of Houston

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Pavement Data Collection, Rating History & Pavement Management Program in the City of Houston Pavement Data Collection, Rating History & Pavement Management Program in the City of Houston Presenter: Steve Loo P.E., Managing Engineer Transportation and Drainage Operations Texas Municipal League Conference 2019 October 9, 2019 Pavement Data Collection and Rating History – In the past ~2001-2007, COH evaluated pavement conditions using “Windshield” method. – Then in ~2008-2014, COH collected pavement data with a pavement data collection vehicle (city-owned). – Currently since 2015, COH collects pavement data (nation-wide vendor) to generate the PCI: 1) PCI is adopted by APWA (American Public Works Association). 2) PCI is based on ASTM D6433 published standard (American Society for Testing and Materials). 3) PCI is a “Numerical Indicator” that rates surface condition of the pavement. Inertial Measuring Unit (IMU) Right-of-Way Global Cameras Positioning (typically four) System (GPS) 5-Laser Profiler Roughness Laser- Rutting IlluminatedLine scan Camera (Pavement) Mobile Asset Collection Van • Videologger for data validation • ROW and pavement view • Google street-view integration Roadway Photo Imagery Log COH Major Roads PCI Map COH Major Roads PCI Results COH Local Roads PCI Map COH Local Roads PCI Results PCI Segment Distribution vs. # of Miles Pavement Surface Type Pavement Surface Type Report Distress Types and Distribution Pavement Distresses/Deterioration Conditions Faulting Linear Cracking Divided Slab & Patching Corner Breaks, Divided Slab, Faulting & Patches Pavement Distresses/Deterioration Alligator Cracking Pothole s Edge Cracking Patching & Joint Reflection Cracking Pavement Condition Index Summary – 2015-2016 RUN COH Right-of-Way Imagery “Asset Tagging” Curb & Gutter, Sidewalks, Curb Ramps, Bridge Guardrails, Etc. Basic Pavement Management Steps – 1) Setup “Street Network Inventory Database” properly on any Enterprise Database System (i.e. Oracle, SQL, etc.) – 2) Collect “Pavement Condition Data” & Perform “Rating on Pavement Surface Condition (i.e. PCI)” – 3) Analyze Data, Assemble Plan & Perform Work (i.e. Preservation, Repair, Rehabilitation & Reconstruction) – 4) Feed-Back Process (Update System) Pavement Survey Cycle Street Network Distress Survey Inventory update STEP 2 STEP 4 - Feedback Collect & Generate Physical(PCI) Upload treatment plans, costs, resurvey strategy. Functional(IRI) Set priority based on needs, Demand(ADT) funds including cost Analyze Data, Assemble Plan & Perform Work (i.e. STEP 3 Preservation, Repair, Rehabilitation & Reconstruction) Additional PCI Information • Repeatable methodology • Pavement distresses are objectively scored based on type, severity, and extent • Every street is rated with the same standard • PCI score range is 0 (worst condition) to 100 (best possible condition) • PCI can be used as a tool/aid to rank streets relative to each other Know PCI, IRI & ADT DISTINCTIONS Functional Assessment: IRI (International Roughness Index) •Ride Quality Measurement •Lagging Indicator (generally) •Citizen/Driver’s notice •Important dimension to assess road conditions. It can be used as a tie- breaker in case PCI is same for two segments Know PCI, IRI & ADT DISTINCTIONS Demand Assessment: ADT (Average Daily Traffic) •Maximum return on funds invested •Maximize impact based on road usage. •Try to minimize VOC (Vehicle Operating Costs – damage, trip, down time, increased fuel costs etc.) for the most Citizens. Average Daily Traffic (ADT): PMP: Why data collection & analysis? • PCI provides present condition of pavement surface • PCI helps determine what treatments and where (right treatment on the right pavement at the right time) • PCI allows for feedback on maintenance treatments (validation of current practices) Pavement Decay/ Management Philosophy TREATMENT PLANS for Asphalt: (Draft-copy) Third Party Software Pavement Management Program desired Outcomes: • Improve QA/QC procedures • See Effects of Preventive Maintenance Strategies • Help Understand Impact of Pavement Treatments • Justify the Need for Dedicated Funds Key points to remember: Automate parts of the PMP system, but Can’t Replace the Human Brain! (i.e. PW Judgement) Data-Driven Decision Making Process! Right Treatment Right Pavement Right Time! Pavement Mgmt. Info. System (PMIS) – Street Section/Segment Data Information Draft 5 Year Plan from Pavement Management Information System to Maintain 70 PCI 2020 2021 2022 2023 2024 Total SINGLE CHIP SEAL 1,358.34 122.28 126.75 58.68 91.36 1,757.41 SEAL CRACKS 2,340.66 2,340.66 MaC Major Collector (5) 262.02 262.02 R Residential/Local 2,078.65 2,078.65 MaC Major Collector (5) 477.66 122.28 126.75 58.68 91.36 876.72 R Residential/Local 880.68 880.68 MILL AND THIN OVERLAY 1,229.96 192.31 180.3 0.07 1,602.64 MaC Major Collector (5) 31.99 4.83 2.44 0.07 39.34 R Residential/Local 1,197.96 187.48 177.86 1,563.30 RECONSTRUCT STRUCTURE (AC) 0.01 0.01 104.95 104.97 MaC Major Collector (5) 0.01 104.95 104.97 R Residential/Local 0.01 0.01 RECONSTRUCT STRUCTURE (PCC) 17.98 161.43 156.43 172.3 78.57 586.7 MaC Major Collector (5) 17.95 7.86 9.76 7.76 4.99 48.31 R Residential/Local 0.03 153.57 146.67 164.54 73.58 538.39 Total 2,606.28 476.02 463.48 230.99 2,615.61 6,392.38 Estimated work in lane miles Importance of Pavement Preservation, Repairs and Preventative Maintenance (example scenario) State of Pavement Infrastructure(Draft/Concept) HOUSTON PUBLIC WORKS-PAVEMENT TREATMENT: ESTIMATED TOTAL NEED BASED ON 2015/2016 PCI Cost per Paveme Treatment Lane Est. Treatment Road Class nt Type PCI Range Category Lane Miles Planned Treatments Mile Total $ Need MAJORS PCC 86-100 Preserve 1587.5 Do Nothing (regular maintenance $0 activities) LOCALS PCC 86-100 Preserve 1088.3 Do Nothing (regular maintenance $0 activities) SUMMARY BY PCI RANGES/TREATMENT CATEGORIES PCI Lane Miles % Estimated Total $ Needs 86- Preserve 100 4,473.6 31% $7,531,300 71-85 Repair $927,375,930 4,551.4 31% $131,728,070 (56%) Repair & 41-70 Rehabilitation 4,264.7 29% $788,116,560 Rehabilitation 0-40 Reconstruction 1,366.3 9% $721,406,400 $721,406,400 (44%) TOTALS 14,656.0 $1,648,782,330 Reconstruction State of Pavement Infrastructure (Draft/Concept) PLANNED FOR ANNUAL PERFORMANCE Maintenance Budget Needed General overall pothole/skin patching as needed $12,491,600 Subtotal for Preventive, Repair, Rehabilitation $108,608,140 Maintenance Support $8,115,800 Bridge Maint. & Replacement @ 8 per year $17,851,800 Grand Total for one year $134,575,740 State of Pavement Infrastructure (Draft/Concept) HOUSTON PUBLIC WORKS-HISTORY OF MAINTENANCE AND REPAIR: STREET NETWORK PAVEMENT FY2015 FY2016 FY2017 PRESERVATION/REPAIRS TOTALS $43,666,704 $42,426,013 $34,274,823 Averaged COH per lane mile maintenance spend $2,737.62 Steady-State COH per lane mile maintenance spend (as proposed) $7,410.49 ICMA Center for Performance Measure (2012) reported nation average per lane mile $3,867.00 State of Pavement Infrastructure (Draft/Concept) LANE MILES BY COUNCIL DISTRICTS HPW-PAVEMENT CONDITIONS (PCI 2015-2016): A B C D E F G H I J K Totals Majors 450 590 556 503 300 289 292 410 338 213 406 4346.52 766 1237 1058 1112 1212 406 726 984 939 328 833 9599.65 Totals Locals 1217 1826 1614 1616 1512 694 1018 1394 1276 540 1239 13946.2 Grand Totals .
Load more

Recommended publications
  • Town of Superior Pavement Assessment Study Final Report January 2017
    TOWN OF SUPERIOR PAVEMENT ASSESSMENT STUDY FINAL REPORT JANUARY 2017 This report was funded in part through grants from the Federal Highway Administration, U.S. Department of Transportation. The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the data, and for the use or adaptation of previously published material, presented herein. The contents do not necessarily reflect the official views or policies of the Arizona Department of Transportation or the Federal Highway Administration, U.S. Department of Transportation. This report does not constitute a standard, specification, or regulation. Trade or manufacturers’ names that may appear herein are cited only because they are considered essential to the objectives of the report. The U.S. government and the State of Arizona do not endorse products or manufacturers. The Town of Superior’s Council approved and adopted the study on January 12th, 2017. TABLE OF CONTENTS 1. Study Overview ............................................................................................................................... 1 Community Overview ...................................................................................................... 1 Purpose and Need .......................................................................................................... 3 Technical Advisory Committee (TAC) ................................................................................ 3 2. Review of Relevant Studies and Policies .......................................................................................
    [Show full text]
  • Development of Regression Models for Predicting Pavement Condition Index from the International Roughness Index
    Journal of Engineering journal homepage: www.joe.uobaghdad.edu.iq Number 12 Volume 26 December 2020 Civil and Architectural Engineering Development of Regression Models for Predicting Pavement Condition Index from the International Roughness Index Muataz Safaa Abed* MSc of Highways and Transportation Engineering Department of Engineering in Al-Diwaniyah Governorate Al-Diwaniyah, Iraq Email: [email protected] ABSTRACT Flexible pavements are considered an essential element of transportation infrastructure. So, evaluations of flexible pavement performance are necessary for the proper management of transportation infrastructure. Pavement condition index (PCI) and international roughness index (IRI) are common indices applied to evaluate pavement surface conditions. However, the pavement condition surveys to calculate PCI are costly and time-consuming as compared to IRI. This article focuses on developing regression models that predict PCI from IRI. Eighty-three flexible pavement sections, with section length equal to 250 m, were selected in Al-Diwaniyah, Iraq, to develop PCI-IRI relationships. In terms of the quantity and severity of each observed distress, the pavement condition surveys were conducted by actually walking through all the sections. Using these data, PCI was calculated utilizing Micro PAVER software. Dynatest Road Surface Profiler (RSP) was used to collect IRI data of all the sections. Using the SPSS software, linear and nonlinear regressions have been used for developing two models between PCI and IRI based on the collected data. These models have the coefficients of determination (R2) equal to 0.715 and 0.722 for linear and quadratic models. Finally, the results indicate the linear and quadratic models are acceptable to predict PCI from IRI directly.
    [Show full text]
  • Arterial and Bridge Needs Assessment (ABNA) Pavement Rating Crosswalk Methodology
    Arterial and Bridge Needs Assessment (ABNA) Pavement Rating Crosswalk Methodology October 14, 2019 Background Maricopa Association of Governments (MAG) is in the process of developing an interactive tool to inform future decision making and project investment. As part of the tool development process, the consultant team has collected pavement performance data from Maricopa Association of Government (MAG)’s Member Agencies. Because the data is collected in various formats depending on the region (See Table 1), it needs to be translated to one consistent measure in order to develop consistent shapefiles of each region’s pavement condition. The purpose of the following memo is to provide documentation of the crosswalk with methodology for transforming all Member Agency data. Table 1: Member Agency Pavement Ratings Rating System / Approach Agency PCI Apache Junction, Avondale, Fountain Hills, Gilbert, Glendale, Goodyear, Maricopa, Mesa, Paradise Valley, Peoria, Phoenix, Queen Creek, SRPMIC, Scottsdale, Tempe Pavement Quality Index (PQI) Chandler Overall Condition Index (OCI) Surprise, Wickenburg Pavement Surface Evaluation and Rating (PASER) El Mirage, Buckeye Excellent (5) to Failed (1) Guadalupe, Litchfield Park, Florence PCR Maricopa County Page 1 of 7 The following table provides definitions of the rating systems used based on interviews with the member agencies and outside research. Crosswalks were developed based on these definitions. Table 2: Pavement Index Definitions Rating System / Definition Rating Approach PCI PCI is typically measured using a visual survey in 0-100 accordance with ASTM D6433 or ASTM D5340. Pavement Quality City surveys pavement, such as the smoothness of 0-100 Index (PQI) roadways and any signs of distress in the pavement surface.
    [Show full text]
  • Developing International Roughness Index (IRI) Model from Visible Pavement Distresses
    IOP Conference Series: Materials Science and Engineering PAPER • OPEN ACCESS Recent citations Developing International Roughness Index (IRI) - Road Handling Using International Roughness Index and Surface Distress Model from visible pavement distresses Index Method Andrew Ghea Mahardika et al To cite this article: Hasan H Joni et al 2020 IOP Conf. Ser.: Mater. Sci. Eng. 737 012119 View the article online for updates and enhancements. This content was downloaded from IP address 170.106.33.14 on 27/09/2021 at 03:41 BCEE4 IOP Publishing IOP Conf. Series: Materials Science and Engineering 737 (2020) 012119 doi:10.1088/1757-899X/737/1/012119 Developing International Roughness Index (IRI) Model from visible pavement distresses Hasan H Joni1, Miami M Hilal2 and Muataz S Abed3 1 2 Assistant Prof. Dr., Department of Civil Engineering, University of Technology, Baghdad, Iraq. 3Engineer in Governorate of AL-Diwaniyah, AL-Diwaniyah, Iraq. Email: [email protected] Abstract. The pavement maintenance, rehabilitation, and design methodologies need an appropriate evaluation of the pavement functional condition. The roughness is generally recognized as a measure of the pavement functional capacity. The roughness of pavement is measured using IRI, which depends on the quantity of distresses existing on the pavement surface. This study focuses on developing a model to predict the roughness (IRI) from the flexible pavement distresses. Accordingly, 83 flexible pavement sections were selected in AL-Diwaniyah city roadways, Iraq. The length of the section is equal to 250 meters. Distress data were collected manually, in terms of amount and severity. The IRI data were collected from all sections using the Dynatest Road Surface Profiler (RSP) test system.
    [Show full text]
  • Roads and Parking Lots Pavement Condition Index Surveys1
    Designation: D 6433 – 07 Standard Practice for Roads and Parking Lots Pavement Condition Index Surveys1 This standard is issued under the fixed designation D 6433; the number immediately following the designation indicates the year of original adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. A superscript epsilon (e) indicates an editorial change since the last revision or reapproval. 1. Scope 2.1.3 pavement branch—a branch is an identifiable part of 1.1 This practice covers the determination of roads and the pavement network that is a single entity and has a distinct parking lots pavement condition through visual surveys using function. For example, each roadway or parking area is a the Pavement Condition Index (PCI) method of quantifying separate branch. pavement condition. 2.1.4 pavement condition index (PCI)—a numerical rating 1.2 The PCI for roads and parking lots was developed by the of the pavement condition that ranges from 0 to 100 with 0 U.S. Army Corps of Engineers (1, 2).2 It is further verified and being the worst possible condition and 100 being the best adopted by DOD and APWA. possible condition. 1.3 The values stated in inch-pound units are to be regarded 2.1.5 pavement condition rating—a verbal description of as the standard. The SI units given in parentheses are for pavement condition as a function of the PCI value that varies information only. from “failed” to “excellent” as shown in Fig. 1.
    [Show full text]
  • Receive and Discuss Updated Street Pavement Condition Index Report
    City of Sonoma Agenda Item Summary Meeting: City Council - Feb 19 2020 Department Staff Contact Public Works Colleen Ferguson, Public Works Director/City Engineer Agenda Item Title Receive and Discuss Updated Street Pavement Condition Index Report Summary In January 2020, City staff received a Budget Options Report (attached) prepared by a consultant, Fugro. The report provided updated pavement condition information for the City’s streets, documented a degradation in the condition of the City’s streets, and made funding recommendations for maintaining the existing street pavement condition. Fugro was contracted by the Metropolitan Transportation Commission (MTC) of the San Francisco Bay Area for their Pavement Management Technical Assistant Program (P-TAP), Round 20 to provide an update to the City of Sonoma’s Pavement Management Program. The Budget Options Report provides information with regards to network level pavement repair options, budget analysis with annual and multi-year programming, impact of varying levels of budgets, and cost-effective pavement repair recommendations to assist jurisdictions to develop maintenance and rehabilitation programs. of consisting system management StreetSaver pavement has Sonoma of City The a approximately 33.79 centerline miles of paved roadways. In July 2019, Fugro conducted distress surveys for the network. The distress data was entered into StreetSaver to calculate the Pavement Condition Index (PCI) for each section as well as the overall network. The PCI is an index ranging from 0 (very poor pavement) to 100 (new pavement). The overall network PCI from the last P•TAP inspection was 74 (as of October 14, 2017). The overall network PCI after Fugro's recent inspection was 72 (as of July 25, 2019).
    [Show full text]
  • Journal VOL 6.Indd
    Jacem 177 Journal of Advanced College of Engineering and Management, Vol. 6, 2021 ASSESSMENT OF RELATIONSHIP BETWEEN ROAD ROUGHNESS AND PAVEMENT SURFACE CONDITION Satkar Shrestha 1, Rajesh Khadka 2 1 Fusion Engineering Ser vices, Lalitpur, Nepal; [email protected] 2Assistant Professor, Department of Transportation Engineering, Nepal Engineering College, PU. __________________________________________________________________________________ Abstract Pavement evaluation is the most significant procedure to minimize the degradation of the pavement both functionally and structurally. Proper evaluation of pavement is hence required to prolong the life year of the pavement, which thus needs to be addressed in the policy level. By this, the development of genuine indices are to be formulated and used for the evaluation. In context of evaluating the pavement indices for measuring the pavement roughness, International Roughness Index (IRI) is used, whereas for calculating the surface distress, indices as such Surface Distress Index (SDI) and Pavement Condition Index (PCI) are used. Past evaluating schemes used by Department of Roads (DOR) were limited to IRI for evaluating the pavement roughness and SDI for measuring the surface distress, which has least variability in categorizing the pavement according to the deformation. Apart from these, PCI which has wide range of categories for evaluating pavement, is not seen in practice in Nepal due to its cumbersome field work and calculations. In this paper the relationship is developed relating PCI with IRI and SDI using regression analysis by using Microsoft excel. In the other words, the pavement roughness index is compared with the surface distress indices. In 2017, 23.6Km of feeder roads in various locations of Kathmandu and Lalitpur districts were taken for this study which comprised of 236 sample data, each segmented to 100m.
    [Show full text]
  • Research on the International Roughness Index Threshold of Road Rehabilitation in Metropolitan Areas: a Case Study in Taipei City
    sustainability Article Research on the International Roughness Index Threshold of Road Rehabilitation in Metropolitan Areas: A Case Study in Taipei City Shong-Loong Chen 1 , Chih-Hsien Lin 1, Chao-Wei Tang 2,3,4,*, Liang-Pin Chu 5 and Chiu-Kuei Cheng 6 1 Department of Civil Engineering, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd., Taipei 10608, Taiwan; [email protected] (S.-L.C.); [email protected] (C.-H.L.) 2 Department of Civil Engineering and Geomatics, Cheng Shiu University, No. 840, Chengching Rd., Niaosong District, Kaohsiung 83347, Taiwan 3 Center for Environmental Toxin and Emerging-Contaminant Research, Cheng Shiu University, No. 840, Chengching Rd., Niaosong District, Kaohsiung 83347, Taiwan 4 Super Micro Mass Research and Technology Center, Cheng Shiu University, No. 840, Chengching Rd., Niaosong District, Kaohsiung 83347, Taiwan 5 Taipei City Government, No. 1, City Hall Rd., Xinyi District, Taipei City 110204, Taiwan; [email protected] 6 Department of Agribusiness Management, National Pingtung University of Science and Technology, No. 1, Shuefu Rd., Neipu, Pingtung 91201, Taiwan; [email protected] * Correspondence: [email protected]; Tel.: +886-7-735-8800 Received: 24 October 2020; Accepted: 14 December 2020; Published: 16 December 2020 Abstract: The International Roughness Index (IRI) is the standard scale for evaluating road roughness in many countries in the world. The Taipei City government actively promotes a Road Smoothing Project and plans to complete the rehabilitation of the main and minor roads within its jurisdiction. This study aims to detect the road surface roughness in Taipei City and recommend appropriate IRI thresholds for road rehabilitation.
    [Show full text]
  • 2019 Pavement Management Report (PDF)
    2019 PAVEMENT MANAGEMENT REPORT CITY OF VADNAIS HEIGHTS 800 EAST COUNTY ROAD E | VADNAIS HEIGHTS, MN 55127 OCTOBER 2019 WSB PROJECT NO. 013883-000 2019 PAVEMENT MANAGEMENT REPORT Table of Contents I. Executive Summary ............................................................................................................................... 1 II. Introduction ............................................................................................................................................ 2 III. Pavement Condition Summary ......................................................................................................... 3 A. Introduction ........................................................................................................................................ 3 B. Existing Pavement Conditions .......................................................................................................... 4 C. Bituminous Pavement Rating Examples ........................................................................................... 5 IV. Maintenance and Rehabilitation Activities ........................................................................................ 9 A. Preventative Maintenance ................................................................................................................. 9 Crack Seal ............................................................................................................................................. 9 Fog Seal ................................................................................................................................................
    [Show full text]
  • Pavement Management Analysis of Hamilton County Using HDM-4 and HPMA
    PAVEMENT MANAGEMENT ANALYSIS OF HAMILTON COUNTY USING HDM-4 AND HPMA Trisha Sen Approved: Mbakisya Onyango Joseph Owino Assistant Professor of Civil Engineering UC Foundation Associate Professor of (Chair) Civil Engineering (Committee Member) Ignatius Fomunung Benjamin Byard Associate Professor of Civil Engineering Visiting Professor of Civil Engineering (Committee Member) (Committee Member) William H. Sutton A. Jerald Ainsworth Dean, College of Engineering Dean, The Graduate School and Computer Science PAVEMENT MANAGEMENT ANALYSIS OF HAMILTON COUNTY USING HDM-4 & HPMA By Trisha Sen A Thesis Submitted to the Faculty of the University of Tennessee at Chattanooga in Partial Fulfillment of the Requirements of the Degree of Master’s Science of Engineering The University of Tennessee at Chattanooga Chattanooga, Tennessee May 2013 ii ABSTRACT According to the 2009 ASCE Infrastructure Report Card, Tennessee roadways have received a B- due to lack of funding. This thesis focuses on the optimal utilization of available funds for Hamilton County using PMS. HDM-4 and HPMA are used to assess the existing pavement conditions and predict future conditions; estimate fuel consumption; calculate road agency costs and user costs; determine cost-effective maintenance treatment; and suggest optimum utilization of funds. From this study, fuel consumption is higher at peak hours due to reduced traffic speeds. User cost is higher on the state route than the interstate due to stop-and-go motion. Road agency cost for the state route is four times higher than the interstate. Micro-surfacing treatment may cost less, but does not improve rut depth, while an overlay improves the roadway including structural cracking and rut depth.
    [Show full text]
  • Pavement Condition Assessment V2.0W
    NYSDOT Network Level Pavement Condition Assessment V2.0w Surface Distress Rating Ride Quality (IRI) Pavement Condition Index IRI Deduct 35 30 IRI >170 in/mi y = 1.08 (9E-06x3 - 0.0075x2 + 2.0532x - 154.37) 25 20 IRI <170 in/mi y = 0.90 (2E-06x3 + 0.0004x2 + 0.0596x - 5.2585) 15 DeductValue 10 5 0 0 50 100 150 200 250 300 IRI (in/mi) New York State Department of Transportation i Network Level Pavement Condition Assessment Preface This document describes the procedures used by the New York State Department of Transportation (NYSDOT) to assess and quantify network-level pavement condition. There are four general facets of pavement condition: surface distress, ride quality, structural capacity and friction. Because the data collection tools currently available to measure structural capacity and friction are not efficient for network-level data collection, the NYSDOT condition assessment procedures are limited to certain surface distresses and to ride quality. Network-level data for these condition factors is readily available through ongoing data collection programs. Information about pavement condition and performance is critical to the decision making that occurs to successfully manage highway pavements. Accurate and timely data is used to assess system performance and deterioration, identify maintenance and reconstruction needs, and determine financing requirements. The annual assessment and reporting of pavement conditions is required by the New York State Highway Law (§ 2 Section 10). NYSDOT has assessed pavement surface distress information annually on 100% of the state highway system using a visual windshield rating system since 1981. The original paper-based collection procedure was replaced in 2004 with the E-Score electronic recording system.
    [Show full text]
  • Seattle Pavement Types and Condition
    PAVEMENT MANAGEMENT: SEATTLE PAVEMENT TYPES AND CONDITION As of 2010, Seattle has an inventory of 3,952 lane-miles (12-ft) of street pavements. The busiest streets, arterials, account for approximately 1,540 lane-miles of the system. Arterials are the city’s most critical connectors and are the principal means by which people and goods move about the city. The remaining 2,412 lane-miles are non- arterials, which carry lower volumes, but nonetheless serve a variety of users. Most non-arterials are residential, but some also support commerce and industry in areas such as the Greater Duwamish, Ballard, the International District, and First Hill. These totals do not include alleys or parking lots, where no inventory information is available. SDOT manages its pavements by regularly assessing condition, analyzing budget needs, performing routine and preventive maintenance, and undertaking major paving projects. INVENTORY OF SEATTLE STREET SURFACES SDOT’s pavement engineers use lane-miles (area) and centerline miles (length) to report pavement inventory figures. SDOT uses a twelve-foot wide lane to calculate lane-mile area. A two-lane, 24-foot wide street that is one mile long is two lane-miles; and a four-lane, 48-foot street that is one mile long is four lane-miles. Centerline miles are simply the length measured along the roadway centerline. The following tables show Seattle’s street surface inventory measured in lane-miles and centerline miles. Pavement Type - Arterial and Non-arterial, by Area Non- Non- Pavement Type Arterial Arterial arterial
    [Show full text]