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Chapter 6: Inventory and Condition

Introduction

Different types of data are required for managing transportation infrastructure. Data collection technologies and data needs vary depending on which infrastructure element is evaluated. For the purpose of this guide, emphasis is placed on the paved road infrastructure. There are many technologies for the collection of data, but the data that is beneficial to the transportation agency in question as well as the appropriate equipment to be used to collect these data need to be decided.

The data collected should support the following objectives:

Provide the data required to support the approach to asset management Describe the asset and its performance Provide the basis for informed decision making Facilitate communications with stakeholders Inform the assessment and management of risk Support the management of statutory requirements Support continuous improvement

What data to collect?

The data needed by any transportation agency are those that influence

The cost to replace or maintain road assets, Maintenance or rehabilitation treatment options, Influences on management decisions, and Service life.

In principle, asset management data falls into two main types and can be grouped as follows:

Inventory data: Data that are mostly static in nature and describe the physical element of the road system and road assets

Condition data: Data that describe the condition of the assets; these data markedly change over time

Other data used for transportation asset management can be grouped as follows:

Traffic data Financial data Asset management activities data Resource allocation data

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Depending on the maturity level of a specific transportation agency, it is imperative that a data management strategy be defined. A data management strategy may comprise the following:

Identification of the business need: This should be based on an assessment of the data requirements and should demonstrate how they meet the asset management strategy and include the risk associated with the data.

Identification of the data owner: An “owner” for the data is required to be responsible for managing the collected information.

Accessibility and date stamping: Access rights to the data should be considered, and all data should be date stamped.

Data collection: When determining the method of collection, the most cost-effective method should be used. Requirements for the accuracy, reliability, and repeatability of data should also be considered. Collaboration (e.g., in procurement) between authorities should also be considered as appropriate with the objective of delivering cost savings.

Frequency of collection and updating: A risk-based approach may be suitable, particularly where assets pose a low risk to the performance of the network and are unlikely to require capital investment. Decisions about the life expectancy of all data types will need to be made.

Data management: Data storage and management processes should be considered to ensure that these are appropriate for the purpose, especially because the quantity and quality of data are likely to increase. Information technology specialists may need to contribute to this discussion to ensure that the proposed approach complies with the transportation agency’s information technology requirements.

Disposing of data: The data management strategy should include considerations of the archiving or disposing of out-of-date data. Agencies should consider whether the data will be required at a later date or whether they may be disposed of completely. In determining the performance of individual assets, historical information and trends may be invaluable to support decisions regarding future performance.

The following questions should be considered when deciding what data to collect:

1. What decisions are to be made to manage the network? 2. What data are needed for the decisions to be made? 3. Can the agency afford to collect the required data? 4. Can the agency afford to keep the data current over a long period?

Process

The following sections focus on the processes involved in data collection for asset management systems.

What data to collect?

Road data can broadly be categorized into various data groups, as shown in Table 6.1.

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Table 6.1 Data groups

Data Group Data Description Data Examples

Inventory Network data, furniture, environment, drainage

Road ID (road name, location, nodes etc.), geometry (length, width etc.), furniture (location, type, condition), environment (terrain, rainfall), drainage (type, location, condition)

Pavement condition

Pavement conditions, pavement structures

Pavement (surface defects, layer strength, ride quality), layer materials

Road structures Bridges, tunnels, pedestrian bridges, retaining walls

Locations, types, conditions

Traffic Volume, loading accidents

Vehicles per day (VPD), annual average daily traffic (AADT), equivalent single-axle loads (ESAL), rates of accidents

Financial Costs, budgets, income/revenue

Unit rates, total costs, allocation and ceiling, toll collection/road tax

Road activities Projects, policy, strategy

Project progress, project duration, project costs, work standards, intervention policy, maintenance strategy

Resources Staffing, materials, equipment and machinery

Salary, quantity and stockpile, cost of machinery

When collecting data, the transportation agency needs to consider the level of analysis for which it will be used, as summarized in Table 6.2.

Table 6.2 Data levels and purpose

Data Level Purpose Description

1. Network-level data (information quality level [IQL] 3, 4, and 5)

For general planning, programming, and policy decisions. These data are supported by the network-level asset management strategy.

2. Project-level data (IQL 2)

For application to the selected section where the data should support decisions to made for the section. The data can be stored for more complete database over time. A method must be establish to keep the data current

3. Research-level data (IQL 1)

These data are collected to respond to specific questions.

The amount of data to be collected and the frequency at which data will be collected will depend on the following:

Relevancy of data to the road management process

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Appropriateness Reliability Affordability

The road data collected should be programmed or strategized to match their function in the asset management process and the data IQL. A data collection program that can be adapted by other transportation agencies is shown in Table 6.3.

Table 6.3 Data collection plan

Strategy Frequency Data Coverage Data Level (IQL) Data Output

1 Every year • Whole network • Uniform section • Selected section

where design is produced

• High-level data (IQL IV) • Detailed data (IQL III) • More detailed data (IQL

II)

Selection with different level of data

2 Cyclic: Three- to five-year intervals

Portion of the network

Detailed data (IQL II /III) Yearly work program using the current data and projected data from the previous year for the following year’s prediction 3 Every year Main roads in the

network High-level data (IQL IV)

Cyclic: Three- to five-year intervals

Roads lower in the hierarchy of the network

Detailed data (IQL II/ III)

4 Cyclic: Three- to five-year intervals

Whole network Low-level data (IQL III) Yearly work program

5 Every year Whole network Detailed data (IQL II) Yearly program (not a cost-effective data collection strategy)

Technology options for data collection

The following factors determine the selection of technologies and equipment for data collection:

Reliability of the equipment Efficiency Ability of the system and the equipment to secure the data collected

It will be advantageous if the equipment or methods employed in data collection can collect road data in a single operation so that the collection will be cost-effective and consistent in referencing. The equipment for data collection can be divided into two main categories:

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Portable system: This is a modular and portable type system that is easily installed and uninstalled from any vehicle. This system is in-expensive and suffices for a particular application.

Dedicated system: This is a system that is permanently installed in a dedicated vehicle. This system has a high cost and is relevant for data collection requiring sophistication and data-intensive surveys, such as that used for crack surface crack mapping or detailed image logging.

Various other types of technology for data collection are widely available, e.g., drones to inspect bridges, surface profilers, or deflection beams.

Figures 6.1 shows an examples of data collection equipment and systems.

Figure 6.1 Video/image logging with processed asset details

Quality control/quality assurance

It is important to ensure data quality during the collection of data. Quality control and assurance is a consideration in all stages and processes during the survey, including calibration, validation, daily checks, continuous monitoring of equipment and results, correct processing, and the storing and securing of data.

A detailed quality management plan (QMP) should be prepared for the data collection. The QMP is expected to ensure that the acquisition of the required data meets the agency’s needs and specifications. The QMP has to address, among other considerations, the following issues:

1. Equipment: This involves equipment/system design, specification, and configuration and compliance monitoring, i.e., ensuring that the equipment is capable of delivering the outcome according to standard requirements (including calibration and validation processes).

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2. Survey procedures: This involves the handling of work zones and narrow roads, weather or traffic conditions, speed conditions, etc.

3. Data processing and data management: This refers to the data processing procedures and algorithms.

4. Health and safety aspects: This refers to safety compliance and traffic management procedures.

5. Data display: This involves visual monitoring of the system outputs. 6. Data backup: This involves ensuring that data is backed up regularly. 7. Calibration and validation manuals: This involves summarizing the calibration and

validation procedures.

Condition indices

Condition indices are a set of criteria that describe a set of discrete, ordered states describing the rating of particular road condition parameters. A professional observer judges the state and assigns a rating that usually achieves the most cost-effective system. However, the rating can vary by rater.

The pavement conditions normally evaluated for asset management are as follows:

Roughness Texture Skid resistance Mechanical and structural properties, e.g., falling weight deflectometer (FWD) data to

measure pavement rigidity Surface distress, e.g., crack frequency or rutting

The above characteristics are measured in the field by acceptable equipment and methodologies and are quantified by means of indicators or condition indices. A set of typical pavement indicators and indices is shown in Table 6.5.

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Table 6.2 Typical pavement indices/indicators

Evaluation Pavement Functions

Characteristics Measured

Indicator/ Indices

Functional

Servicablity Roughness

International roughness index (IRI)

Present serviceability index (PSI)

Quarter-car index (QI)

Safety

Texture Macrotexture

Microtecture

Skid resistance Skid resistance coefficent

International friction index (IFI)

Structural evaluation

Structural capacity

Mechanical properties

Deflections

Pavement distress

Cracking

Surface defects

Profile deformation, rutting

Case study: Implementation of road asset management and inventory systems for the state roads of Pahang, Malaysia – data collection

The state government of Pahang, Malaysia, decided to implement a road asset management system for its state road network as part of a long-term road maintenance contract of seven years with a private company appointed in April 2013. The company was to maintain 2,300 km of the state road network and implement a road asset management system. The scope of road maintenance work included routine maintenance, periodic maintenance, and emergency maintenance. The contract also included the provision of an asset management system (YP-RAMS) and a road asset inventory management system (YP-RIMS)

The data collection for the asset management system involved the collection of road asset inventory data and a pavement condition survey to be carried out for the entire network in the first year. The collection of road asset inventory data and a pavement condition survey for 30% of the network was to be carried out from the second year until the end of the contract period. The use of GPS coordinates and digital recording for all asset points and referencing was compulsory. The road asset inventory management system needed to be updated and inventory reports produced every year. The new road asset management module needed to be inter-phased or integrated into a pavement management system/HDM-4, geographic information system (GIS), road asset inventory management system, and road inventory and road condition database.

The asset management system is called YP-RAMS. It makes use of a common inventory and database on the ArcGIS platform. The network condition data are measured in terms of roughness (IRI), cracks, and pavement strength. Digital recordings of the right-of-way were

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taken for the entire network. Traffic data were collected from selected traffic count stations in terms of AADT and axle loading.

The project managers opted to outsource the data collection equipment rather than purchase new equipment. This proved to be cost-effective, considering the idle time between data collection intervals. The techniques and equipment used included a multi-laser profiler and automatic crack detection (ACD) system, FWD, asphalt coring, and a dynamic cone penetrometer. The techniques and equipment used are shown in Figures 6.4 to 6.7. The following data were collected:

Location referencing Inventory data Traffic data Road condition data

Figure 6.4 Auto crack detection system and digital camera

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Figure 6.5 Time data acquisition output

Figure 6.6 Point-and-click on-screen operation to give GPS coordinates of an asset

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Figure 6.7 Real-time data acquisition of digital right-of-way recordings

The criteria used for data collection and analysis are shown in Tables 6.4 to 6.6.

Table 6.4 Pavement layer stiffness criteria

Pavement Layer Criteria Layer Strength, E Value

Ranges (MPa)

Bituminous surfacing

POOR <1500

SATISFACTORY 1500-2500

SOUND >2500

Granular base

POOR <200

SATISFACTORY 200-300

SOUND >300

Subbase

POOR <75

SATISFACTORY 75-150

SOUND >150

Subgrade

POOR <50

SATISFACTORY 50-100

SOUND >100 Table 6.5 Functional criteria for pavement failure

Criteria Roughness

(IRI)

Rutting

(mm)

Texture Depth

(mm)

All Cracks

(% area)

GOOD <2.5 <5 >0.5 <5

FAIR 2.5-3.5 5-10 0.3-0.5 5-10

POOR 3.5-4.5 10-20 <0.3 10-25

BAD >4.5 >20 - >25

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Table 6.6 Typical maintenance standards

Maintenance Work Item Roughness (KPI)All Crack (% area)

Partial Recon @ 3.8 and ACA > 40 ≥ 3.5 ≥ 40%

OL 50 @ > 3.8 ACA < 20 ≥ 3.5 <= 20%

Mill 110/Rep 110 @ 3.8 IRI < 40 ACA ≥ 3.5 <= 40%

Mill 110/Rep 110 @ 2.5 ACA > 40 ≥ 2.5, ≤ 3.5 >= 40%

Mill 50/Rep 50 @ 40 ACA < 2.5 <= 2.5 >= 40%

Mill 50/Rep 50 @ 2.5 ACA >20 ≥ 2.5, ≤ 3.5 ≥ 20%, ≤ 40%

Mill 110/Rep 110 refers to a 110 mm depth of milling, a 60 mm depth of replacing back with new material, and a 50 mm depth overlay. Mill 50/Rep 50 refers to a 50 mm depth of milling and a 50 mm depth of replacing back with new material. OL 50 refers to a 50 mm depth overlay. Partial Recon refers to a treatment applied until the pavement subbase layer.

The collected road data were processed using the respective software of the equipment used. A summary of the outcome of the surveys is shown in Table 6.7.

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Table 6.7 Summary of the results of the survey

Total length surveyed (km): 1900 km (GPS measured length)

Average carriageway (width):

> 6m 352.662 (18.5%)

4.25 – 6.0m 1143.242 (60.2%)

< 4.25m 403.944 (21.2%)

Pavement condition: Good Fair Poor Bad

Roughness, IRI (m/km) 30.9% 34.5% 19.5% 15.1%

Rutting (mm) 77.5% 19.0% 3.2% 0.2%

SMTD 59.6% 36.8% 3.6% -

FWD (9,422 FWD points) : Sound Satisfactory Poor

Bituminous layer , E1 32.2% 21.4% 46.4%

Granular layer / base, E2 29.5% 57.3% 13.1%

Subgrade , E3 37.6% 47.6% 14.8%

Average layer thickness (mm) : Bound layer (981 coring)

Base / unbound (981 DCP points)

115 277

Crack analysis:

Total area measured (m2) 9,342,549

Area with all cracks (m2) 2,035,053.9 (21.8%)

Average existing subgrade CBR (%):

7%

HDM-4 was used to evaluate the maintenance requirements based on the maintenance standard specified. With the base year 2014, the predicted cost by each district is shown in Figure 6.8.

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0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

Annual Cost by District (RM)

Year

HDM4 Output 2015 ‐ 2024(Base Year 2014) 

Bera

Maran

Temerloh

Kuantan

Pekan 

Lipis

Jerantut

Raub

Bentong

Cameron Highland

Figure 6.8 HDM-4 output (ten-year analysis)

In conclusion, the proper and systematic collection of data using the correct and appropriate data collection equipment allows an adequate maintenance strategy to be applied.

References

a. “Guidelines on Calibration and Adaptation”, HDM-4 Technical Reference Series, Volume 5 by Bennett, C.R. and Paterson, W.D.O. (1999)

b. “Data Collection Technologies for Road Management”, C Bennett, A Chamorro, C Chen, H Solminihac, G W Flintsch, Version 2 (2007), The World Bank

c. “Asset Management for Road Sector”, OECD, 2001 d. “Road Asset Management Principles”, University of Birmingham/Transit NZ, 10 June 2002 e. “Selecting Road Management Systems”, The World Bank, 1997 f. “Evaluating Asset Management Maturity in the Netherlands”, Telli Van der Lel et al g. “Highway Infrastructure Asset Management” , UK Roads Liason Group, 2013