Consultancy Services for the Development and Implementation of a GIS - - PowerPoint PPT Presentation

Consultancy Services for the Development and Implementation of a GIS based Road Maintenance Management System for Saint Lucia

Contents

• 1. Road maintenance planning and RAMS (KS)
• 2. Systems, data and data flows (KS)
• 3. Data collection methodology (MK)
• 4. Road sector processes (ES)
• 5. Capacity building (KS)

• 1. Road maintenance planning

Ad-hoc planning

Maintenance is needed

Preventative maintenance Instead of Emergency maintenance

Maintenance is needed

Service level & quality problems

Reactive vs. preventive maintenance

Preventive maintenance

Optimisation of road maintenance

Scenarios for the Maintenance Budget

* NOTE: planned/ongoing 654 MUSD investment for the road retwork has been assumed to bring 623km of roads from bad condition into good condition (2011-2020).

17% 4% 25% 8% 57% 88%

2016 2036

SCENARIO - 7.7 MUSD/YEAR (0.09% GDP)

Good Fair Poor

17% 10% 25% 25% 57% 65%

2016 2036

SCENARIO - 25 MUSD/YEAR (0.31% GDP)

Good Fair Poor

17% 29% 25% 34% 57% 37%

2016 2036

SCENARIO - 55 MUSD/YEAR (0.7% GDP)

Good Fair Poor

Optimized Maintenance Programme

Overlay 50mm AC Rehabilitation Upgrade gravel road to bitumen

Project Objectives

1. Collection of relevant data with procured equipment for sustainable road management and maintenance decision-making 2. Implemented Road Asset Management System modules to support decisio n-making and optimisation 3. Institutionalised business processes to support use of RAMS and data for o ptimal decisions 4. Fully trained users to operate the software and hardware according to the i nstitutionalised processes 5. Increased road life for the same budget through systematic approach

1 3

• 2. Systems and data flows

RMMS

Annual Reports

Key Performance Indicators

GIS

Multi-year Maintenance plan, Expenditures Strategic Analysis Budgeting

Properties of the Road Functional Condition Structural Condition Hilliness and Geometry Traffic Data Bridges Culverts & Drainage Condition Vulnerability for Natural Hazards Road Videos and Photographs Black Spots and Safety Hazards

Road Database

Complete RAMS Model

RAMS architecture

1 6

Central Road Asset Database

Application Programming Interfaces Road and GIS data pre- processing

Cloud Server Client Devices

RAMS Server code

RAMS Road Information Subsystem RAMS Strategy and Policy Subsystem RAMS Periodic Maintenance planning Subsystem RAMS Routine Maintenance planning Subsystem RAMS Bridge Management Subsystem RAMS Capacity Expansion Subsystem

Web-based User Interface

System data flows: GIS & RDB

GIS Database Relevant tables for RAMS RAMS Database Roadroid IRI Survey Database Possible manual and automatic validation Quantum Geographic Information System (QGIS) Match using Linear Referencing System GIS data processing: 1. reduction of unnecessary road alignment points 2. selection of the correct tables and fields

System data flows: survey data

1 9 Manual traffic counts RAMS Database Mobile application and intermediate server used RMMS Portable traffic counts Permanent traffic counts FWD data Videos and photos Inventory & PDI Bridge inspections BMS Automatic data transfers from the tablet Manual transfer of the data and import may be required Intermediate server in the data transfer Manual transfer

• f the data and

import is required Intermediate server in the data transfer may be required

RAMS IT hardware

Public Cloud Private Server

Data requirements and dictionary

Road inventory

– Location – Characteristics (width, length, road class) – Geometry: curvature, rises and falls, gradient

Pavements

– Structural condition (bearing capacities by Falling Weight Deflectometer) – Functional condition (International Roughness Index, Pavement Distress Index) – Gravel thickness and subgrade soil classification – Drainage condition – Pavement ages – Work history

Structures

– Bridge and culvert inventory – Characteristics – Condition

Traffic

– Composition and volumes

Finance and operations

– Maintenance standards for routine and periodic maintenance & reconstruction – Past works – Unit costs – Maintenance funding sources and budget constraints – Maintenance strategies and contracts – National Vehicle Fleet with prices

Status of the current data

In the GIS database Number of classified paved roads 1725 Number of classified non-paved roads Number of non-classified paved roads 1093 Length of the paved roads 820.4 km Total length of all the roads 1892.6 km Maximum road length 11.8 km Average paved road length 499 m Number of bridges, large culverts and tunnels 229 Number of culverts 18138 Number of retaining walls 8430

Status of the current data

Primary (A) Secondary (B) Tertiary (C) Streets (D) Number of classified paved roads 24 34 231 342 Length of classified paved roads (m) 129 566 63 785 221 979 81 045 Number of non-classified paved roads 1 425 Length of non-classified paved roads (m) 391 575 Number of non-classified unpaved roads ? Length of non-classified unpaved roads (m) 1 067 220

• 3. Data collection methodology

Road Surveys

Device Manufacturer Data to collect FlexPak6 GPS Novatel Centerline coordinates Roadroid Smartphone Roug hnessApplication Roadroid Roughness (IRI), road videos/images. Roadroid Inventory Tablet Roadroid Road and condition Inventories, manual t raffic counting EyeVi videologging and lase rscanning ReachU 360° road images and 3D-model of road surroundings, inventories Falling Weight Deflectomete r Dynatest Bearing Capacity (Deflection, E-modules, stresses and strains) IRImeter Englo Roughness (IRI), crossfall* Tube traffic counters Metrocount AADT, vehicle composition Magnetic traffic counters Sensebit AADT, vehicle composition, speed, seasonal factors Dynamic Cone Penetrometer Controls group Gravel thickness, subgrade soil classificati

• n

Road Inventory - GPS

u Location of the centerlines u Algorithm creation for curvature, hilliness and rises and falls from the GPS tra ck With Egnos correction accuracy is 0.6-0.7m

Basic Video Logging

u Road videos and interval images act as “eyes” to the road network u From videos is possible to make road inventories: Pavement type, Shoulder type, Drainage condition class, Pavement condition class

Advanced Video Logging

u 360° Panoramic images with 3D Lidar laserpoints u Orthophotos u Road Inventory: road width, pavement width, Shoulder width u Height of road side objects

Roughness (IRI)

u IRI by IQL3 u Year 1: Accelerometer-based survey using Samsung S7 phones (Roadroid)

Roughness - IRI

u Year 2 onwards: Roadroid is left for lower network surveys and more accurate equipment is procured: (IRImeter2) u Central block with a display and USB interface u Wireless IRI sensor(s) u Roof-mounted GPS antenna u IRImeter1 has sensor for crossfall surveys, if needed. IRImeter1 is not wireless.

Calibration of IRI

u Calibration road sections will be identified and measured with Dipstick to get “true value” against which the roughness surveys devices are calibrated u Three test road segments (in good, fair and poor condition) of 500 m are selected and manually measured by Dipstick u Each of them are surveyed with Roadroid by different speeds (20 km/h, 40 km/h, 60 km/h) and calibration factors are calculated u Average of all the calibration factors is used in the surveys

Bearing Capacity

u The Dynatest Falling Weight Deflectometer (FWD) applies a dynamic load that simulates the loading of a moving wheel. u The pavement response is analyzed with Dynatest's ELMOD (Evaluation of Layer Moduli and Overlay Design) software to determine the elastic moduli, stresses and strains of each modeled layer. u ELMOD reports the weakest layer of failure, residual life and determines the

• ptimum rehabilitation alternatives.

Pavement Surface & Drainage Condition

u Using the same videos / photos as for road inventory survey – Visual assessment in the office u Creation of condition rating for visual assessment: 1-Very good, 2-Good, 3-Fair, 4-Poor, 5-Very poor

– lick on “Inventory” button “Click to add inventory category” “Start inventory”.

• 6. Finish the survey by clicking on “Stop and save inventory” button.

be available on the “import history” where 1 2 3 4 5 6

Traffic Surveys

u AADT, Traffic Composition, average speed u Hourly, weekly and seasonal factors u Permanent and portable counters u Manual counting on minor roads

Bridge Condition Inventory

u Bridge and culvert inventory u Characteristics & Condition u Tablet for data collection u DISTO D510 Laser Distance measurer u Crack gauge (simple card with different lines with thicknesses starting from 0.1 mm to 2 mm) u Helmet u Safety rope or simple climbing equipment (harness, rope, stopper) u Safety vest u Small hammer (for delamination detection) u DJI Mavick Pro drone for inspections and for making 3D models u Screwdriver or knife (for rust and timber testing) u Hand torch or headlight u 30 cm ruler or distance measure tape (for small measurements and pictures) u Camera (DSLR) u Ladder (preferably folding ladder with minimum height of 5 meters) u The coating thickness measurer Sauter TF 1250

Data Collection Planning

u In RAMS, data collections should have a network-level approach u The data should be adequate to make annual maintenance planning and long-term strategy analysis - No point to collect data which is not supporting the decision making u Data collection is expensive and time consuming exercise u Survey accuracy, interval and coverage should be carefully planned, keeping in mind that we have to keep the data up-do-date in coming years u Surveys are launched by carrying out a pilot survey to get better understanding of the possible problems and survey speed. Weekly achievement in different surveys is based on experiences, but will be adjusted to circumstances in Saint Lucia uSurvey season should not be during rainy periods. Duration 3-4 months.

Quality Assurance

u Quality Assurance (QA) procedures for data and data management as well as use

• f the RAMS will be prepared by consultants

u Errors in data can occur in all stages of data collection. Wrong location for the measurement, insufficient calibration of equipment, systematic errors in work methods, errors in data processing and input are examples of quality problems. u Quality assurance is an on-going approach throughout all processes. For data collection quality assurance refers to activities before, during and after data collection. u Selecting the appropriate data collection frequencies is the first quality assurance activity undertaken. Only if the database holds appropriately updated data, will the models and analysis tool output produce reliable maintenance needs and programs.

Quality Assurance

u Before data collection the survey teams ensures, through training and calibration tests, that all surveyors are qualified. In addition all equipment to be applied shall be properly calibrated and be checked for reliability as per instructions of the equipment provider. u During data collection, the RAMS team leader should follow the survey teams closely; sometimes spending 1-2 days with the teams to observe their work. u Of the data received, the survey team should undertake random quality assurance tests of 3-5% of the surveyed road network. This could be done by comparing the received data to records made by the team itself. u Each survey equipment has its own calibration methodology. Approach for calibration is provided by the service provider and it should be trained for the personnel after delivery of the device. u Safety plan is part of the quality assurance

Data Collection - General

• 4. Road sector management

and processes

Road Asset Management

Process model

Road users’ needs Road users’ satisfaction Core processes Management services and Supporting processes Organization units

Road sector core and supporting processes

Human resource management Financing and accounting Management information Steering and Monitoring Legal matters, Standards Vision (10-20 years) Road Policy Long-term Planning (5-10 years) Mid-term Planning (3-5 years) Annual Planning and budgeting (Investments and Maintenance) Project and routine maintenance planning Procurement In-house implementation Quality control Contracting Supervision

Great Impact from RAMS 1.

5. 4.

3. 2.

7b. 7a. 6. 8b. 8a.

Example of long-term planning process

2

Stakeholders Minister Planning unit Consultants RAMS unit Initiative & TOR Analysing needs of road users and society Needed data and analyzis Use of consultants if needed Cost- Benefit Analysis Long-term program

• New roads
• Rehabilitation
• Maintenance

Publicity

• f

Program Road Policy Financial frames Combinin g different

• pinions

Approval

3-6 months 4-6 months

Analysing condition

• f road

network Tentative distributing of roads to categories:

• Maintainable
• Rehabilitation
• New construction

* *

Tentative distributing

• f roads to

different categories Discussions with Stakeholders

*

How to develop processes

• Describe the current processes for each main task
• Recognize the shortcomings and problems
• Propose improvements
• Describe new processes
• Put new processes into action
• Follow up and improve when needed

Interview model for process mapping

Interview model for process mapping

Annual maintenance planning cycle

Data collection planning Routine Maintenance Data collection Data validation and import Maintenance planning Periodic Maintenance Stakeholder consultations & maintenance decisions Procurement & contract management Process and system updates

• 5. Capacity building

Capacity building for sustainable RAMS

• On-the-job training
• Classroom workshops once a month. Topics could be:

1. Road asset data, importance, collection methodology and equipment 2. Principles in maintenance planning 3. Vision, strategy and processes in road management 4. RMMS, BMS, GIS 5. Road Database and Data Analysis 6. Road survey planning 7. Information management

• Information sharing, dialogues

Proposed RAMS team

Sustainable RAMS operations

Thank You!

• Dr. Konsta Mikael Sirvio

konsta.sirvio@sirway.fi +358 40 8233890 Sirway Ltd. Helsinki Finland

5 3 Javaid Iqbal Konsta Sirvio Zaur Izzatdust Esko Sirvio Markku Knuuti Sander Sein