SERIAL 03
24205106
FOUNDATION X GROUP CIVIL ENGINEERING · PYTHON PROJECT
TRANSPORTATION ENGINEERING / DATA-DRIVEN DECISION SUPPORT
Python-Based Road Pavement Condition Analysis & Maintenance Priority System
A practical engineering decision-support platform that transforms road-survey observations into pavement condition scores, condition categories and maintenance priorities.
Explore live dashboard ↗THE ENGINEERING PROBLEM
Why a structured pavement assessment is needed
Road survey observations can be fragmented across sections and distress types. Without a consistent assessment workflow, comparing road condition and explaining maintenance priorities can become difficult.
Project responseConvert field observations into a transparent weighted condition score, a consistent condition category and a ranked maintenance list for preliminary decision support.
THE PEOPLE BEHIND THE PROJECT
Project team
Foundation X · Group Leader: Rakibul Hasan
04 STUDENT MEMBERS
SERIAL 02
24205108
Niloy
REGULAR MEMBER
SERIAL 04
24205123
Rafiul Islam
REGULAR MEMBER
SERIAL 01
24205135
Rakibul Hasan
GROUP LEADERWHY THIS PROJECT MATTERS
Engineering importance
From scattered survey observations to a structured maintenance plan.
Safer, better roads
Identifies pavement sections with poor or critical conditions so that engineers can recognize locations that may require timely inspection and intervention.
Smarter use of budgets
Ranks road sections by relative maintenance need, helping decision-makers plan limited maintenance resources more transparently.
Consistent assessment
Applies a repeatable scoring and classification workflow instead of relying only on separate manual observations.
Data-driven planning
Uses tables, charts and downloadable reports to communicate pavement condition patterns and support preliminary maintenance planning.
PROJECT OBJECTIVES
What the system is designed to do
- Analyze pavement condition data for multiple road sections.
- Process distress indicators such as cracking, potholes, rutting, roughness and drainage condition.
- Calculate a transparent weighted condition score.
- Classify each road section and generate maintenance priorities.
- Visualize condition scores and priority rankings through charts and summary tables.
METHODOLOGY
How the analysis works
01CollectRoad inventory and pavement distress survey data
02ValidateCheck required fields, numeric values and data consistency
03CalculateApply weighted distress indicators and condition scoring
04Classify & rankAssign condition categories and maintenance priorities
05ReportPresent charts, tables and downloadable outputs
DATA & TECHNOLOGY
Tools used in the project
Python 3Lists & TuplesDictionariesFunctions & ClassesFile HandlingNumPyPandasMatplotlibHTML · CSS · JavaScript
EXPECTED OUTCOME
A clearer picture of pavement health.
The system produces a condition score, condition category and recommended maintenance action for each analyzed road section. It is intended as a preliminary decision-support tool for pavement management—not a replacement for detailed field investigation, engineering judgement or formal pavement design.
RV+
WEBSITE DESIGNED & DEVELOPED BY RAKIBUL HASANFOUNDATION X · ROADVISION PRO
Network overview
Monitor pavement health. Make smarter maintenance decisions.
Files stay on your device
ROAD NETWORK INTELLIGENCE
Every road tells a story.
Make the next move count.
Turn pavement survey data into clear condition insights and actionable maintenance priorities.
01 / CONDITION MONITORINGENGINEERED FOR BETTER ROADS
Import your road survey file to start the analysis. CSV, XLSX and XLS are supported.
NETWORK AT A GLANCE
Key performance indicators
NO DATASET LOADED
ROAD SECTIONS▤
—
Surveyed sections ↗
AVERAGE CONDITION◉
—/100
Network health score
POOR + CRITICAL!
—
Sections needing intervention
Awaiting survey data
TOP PRIORITY↑
—
Most urgent section
ACTION REQUIRED
CONDITION & DISTRIBUTION
Performance analytics
Good Fair Poor Critical
01
Condition score by section
Higher scores indicate better pavement condition.
Your condition chart will appear here.
02
Network condition mix
Road sections by condition category.
—SECTIONS
No data yet.
Import a dataset to see the condition mix.
03
Maintenance priority ladder
Rank 1 represents the most urgent intervention.
Priority ranking will appear here.
04
Engineering insights
Highlights from the current road network.
BEST CONDITION—Awaiting data
NEEDS ATTENTION—Awaiting data
Good0
Fair0
Poor0
Critical0
ADVANCED ANALYSIS
Traffic & distress analytics
Traffic insights appear only when the uploaded dataset contains the required fields.
05
Distress parameter comparison
Normalized severity indicators (0–100).
Upload a dataset to compare distress indicators.
06
Traffic vs pavement condition
Observed traffic count against condition score.
Traffic_Count_15min field not found.
07
Heavy vehicle impact analysis
Heavy vehicle count against condition score.
Heavy_Vehicle_Count_15min field not found.
08
Network statistics
Summary of the active dataset.
Import a dataset to calculate summary statistics.
DECISION SUPPORT
Maintenance action plan
Ranked road sections with recommended engineering intervention.
| RANK | SECTION / ROAD | CONDITION SCORE | STATUS | RECOMMENDED ACTION |
|---|---|---|---|---|
| Import a CSV or Excel survey file to populate the action plan. | ||||
ROAD ASSET REGISTER
Road inventory
Search, filter and inspect individual analyzed road sections.
| SECTION ID | ROAD NAME | LENGTH (m) | WIDTH (m) | CONDITION SCORE | STATUS | DETAILS |
|---|---|---|---|---|---|---|
| Import a survey dataset to populate the road inventory. | ||||||
ACADEMIC PROJECT DOWNLOAD CENTER
Submission center
Project resources and deliverables. Google Drive links can be added in the centralized configuration.
Resource links are placeholders until the project owner adds the verified Google Drive URLs. No download link is fabricated.
PROJECT DOCUMENTATION
About the project & methodology
Project information
- Project
- Python-Based Road Pavement Condition Analysis and Maintenance Priority System
- Group
- Foundation X
- Group Leader
- Rakibul Hasan · 24205135
- Developer
- Designed & Developed by Rakibul Hasan
- Platform
- Cloudflare Pages · Browser-side processing
Weighted condition model
The existing Python notebook defines the following indicator weights. These values are preserved without modification.
Cracking25%
Potholes25%
Rutting20%
Roughness20%
Drainage10%
Condition Score = 100 − Weighted Distress Score
Classification: Good ≥ 85; Fair ≥ 70; Poor ≥ 40; Critical < 40. These are the project's implemented thresholds, not a claim of official PCI classification.
Methodology, assumptions & limitations
- Crack severity uses Crack_Percent directly on a 0–100 scale.
- Pothole severity is pothole area divided by section area, expressed as a percentage and capped at 100.
- Rutting severity uses Rut_Depth_mm / 25 × 100, capped at 100.
- Roughness and drainage ratings are normalized from 1–5 to 0–100 using (rating − 1) / 4 × 100.
- Traffic and heavy-vehicle fields are used for descriptive analytics only; they do not alter the condition score.
- This academic decision-support model does not replace field investigation, engineering judgement or formal pavement design.
Technical note: The uploaded notebook is the supplied Python reference. A separate standalone .py source was not present in the available project files.
ANALYTICAL MODEL
Weighted pavement condition index
Crack 25% · Pothole 25% · Rutting 20% · Roughness 20% · Drainage 10%. Condition score = 100 − weighted distress score.
MODEL V1.0