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Domain

Pavement Condition & Deterioration

PCI scoring (ASTM D6433), automated survey integration with AI crack classification, MTO-aligned deterioration curves, remaining service life calculation, and network-level condition forecasting — the analytical engine for treatment selection and capital planning.

≥ 90%

AI Crack Accuracy

< 24 hrs

Survey Processing

< 30 sec

Scenario Modeling

< 5 sec

PCI Map

Purpose-Built for Canadian Municipalities

Ontario Compliant
MFIPPA Ready
AODA Accessible
Bilingual Support
Canadian Hosted
SOC 2 Aligned

Purpose & Scope

What this module owns

Clear ownership boundaries prevent duplication and ensure every capability has exactly one authoritative home.

Owns

10

Delegated to

5

Segment attributes, traffic data

road-network-inventory

Colour-coded PCI maps

geospatial-engine

Automated crack classification, curve calibration

ai-ml-engine

Field survey data capture

mobile-field

Condition reports, trend dashboards

reporting-analytics

These capabilities are handled by dedicated modules and consumed via stable API contracts — keeping boundaries clean and ownership unambiguous.

Core Capabilities

What it does

3 capability groups comprising 9 discrete capabilities — each with API surface, business rules, and data ownership.

ASTM D6433: all distress types (alligator, block, edge, longitudinal, transverse cracking, patching, potholes, rutting, shoving, weathering, bleeding, corrugation, depression), severity L/M/H, extent, deduct values, composite 0–100.

Distress Types

All ASTM D6433 distress types including cracking, deformation, patching, and surface defects.

Severity/Extent

Low/Medium/High severity with percentage-based extent measurement.

Deduct Values

Standard ASTM deduct value curves for composite PCI calculation.

Composite Score

0–100 composite PCI with maximum corrected deduct value methodology.

Vehicle-mounted data: imagery, GPS, laser rutting, IRI, surface texture. AI crack type/severity auto-classification with reconciliation against manual PCI for calibration.

Vehicle Data

Automated collection: pavement imagery, GPS track, laser rutting, IRI, and surface texture.

AI Classification

Automated crack type and severity classification from pavement imagery (≥ 90% accuracy).

Calibration

Reconciliation between automated and manual PCI for continuous calibration.

Processing

< 24 hours from vehicle collection run to PCI score availability.

Vehicle-mounted accelerometers or smartphone roughness measurement. IRI per segment converted to 1–5 scale. Captures subgrade settlement, frost heave, and base failure.

IRI Measurement

International Roughness Index from vehicle-mounted sensors or smartphone accelerometers.

1–5 Scale

IRI values converted to 1–5 ride comfort scale for public reporting.

Structural Indicators

Roughness patterns indicating subgrade settlement, frost heave, or base failure.

Trend Analysis

IRI trend tracking for early detection of structural distress.

FWD testing: deflection basin, backcalculated layer moduli, Structural Number/SAI. Distinguishes surface-only vs. full-depth reconstruction needs.

FWD Testing

Falling Weight Deflectometer deflection basin analysis at multiple offsets.

Backcalculation

Layer moduli backcalculated from deflection data for structural evaluation.

Structural Number

SN/SAI calculation for pavement structural adequacy assessment.

Treatment Guidance

Distinguishes surface-only rehabilitation from full-depth reconstruction needs.

Every module owns a single bounded context, exposes stable APIs, and can be composed into any Civic product — that's the architecture that scales.

Krutik Parikh

Creator of Civic

Data Model

Entity Architecture

4 entities with 4 relationships — the authoritative schema for this bounded context.

Entities

Select an entity to explore its fields and relationships

API Surface

Integration Endpoints

8 RESTful endpoints across 6 resource groups — plus 3 domain events for async integration.

|
GET

/api/v1/pavement/surveys

Survey records

POST

/api/v1/pavement/surveys

Record survey with distresses

Ecosystem

Products that depend on this module

2 Civic products consume Pavement Condition & Deterioration — making it one of the most critical platform services in the ecosystem.

Technical Specifications

Performance, Compliance & Configuration

AI Crack Classification Accuracy

Target≥ 90%

Survey Processing

Target< 24 hours from vehicle run to PCI

Scenario Modeling

Target< 30 seconds for 50,000 segments

PCI Map Rendering

Target< 5 seconds

FAQ

Frequently Asked Questions

Ready to Integrate

Build on Pavement Condition & Deterioration

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