CIVIL ENGINEERING · DECISION SUPPORT

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SYSTEM WORKSPACE
ADVANCED PAVEMENT ANALYTICS

Understand your roads.
Prioritize what matters.

A data-driven workflow for pavement condition assessment, distress intelligence and maintenance investment planning.

Python-powered · GIS-enabled · AI-ready
01ROAD
INTELLIGENCE
23°48' N
90°24' E
ROAD SECTIONS ▤
—Sections in latest analysis
AVERAGE CONDITION ◉
—/100Network condition index
HIGHEST PRIORITY ⚑
—Lowest condition score
AI MODEL STATUS ◎
Not configuredCustom model required
ENGINEERING PERFORMANCE

Condition intelligence

Based on the original Colab weighted model
Condition score by sectionScale: 0–100 · Higher is better
● Condition score
Condition distributionRoad sections by category
0SECTIONS
DECISION SUPPORT

Maintenance priority ranking

Lowest condition score = highest priority
Road section registerRun an analysis to populate results
RANKSECTION / ROADCONDITIONCATEGORYRECOMMENDED ACTION
Upload your project CSV to view road rankings.
SPATIAL INTELLIGENCE

GIS road network

GeoJSON · Leaflet · OpenStreetMap
Road condition mapUpload verified road geometry; coordinates are never guessed.
● Good● Fair● Poor● CriticalNo road geometry loaded
COMPUTER VISION

AI pavement distress detection

MODEL-DEPENDENT
◎

Image-based distress screening

Upload a road image to run a custom YOLO pavement model. Inference remains disabled until trained weights are configured on the backend.

PotholeLongitudinal crackTransverse crackAlligator crackOther damage
AI model status will appear here.
ENGINEERING SAFEGUARDS

AI output is not an engineering measurement.

  • Every detection requires human confirmation.
  • Pixel area cannot be converted to m² without image calibration.
  • Rutting depth cannot be inferred reliably from an ordinary single image.
  • AI detections do not automatically alter the original condition score.
Model evaluation should include precision, recall, F1-score, mAP50 and a location-separated test set.
CAPITAL PLANNING

Maintenance budget optimization

Binary selection · Budget-constrained benefit maximization

Enter a documented cost and benefit value for each road. Benefit scores are supplied by the project team—not generated arbitrarily.

Optimization results will appear here after you provide road costs and benefit values.
DATA PIPELINE

Import & validate dataset

CSV · Schema validation · Python calculations
⇧

Upload pavement survey data

Use your original road_pavement_raw_data.csv file or another CSV with the required engineering columns.

Required fieldsSection_ID, Road_Name, Section_Length_m, Pavement_Width_m, Crack_Percent, Pothole_Area_m2, Rut_Depth_mm, Roughness_Rating, Drainage_Rating
Waiting for dataset
PROJECT DELIVERABLES

Reports & submission center

Google Drive submission links can be added in frontend/app.js after the team creates the final shared files.

THE PEOPLE BEHIND THE PROJECT

Project team

Foundation X · 4 student members
Rakibul Hasan
SERIAL 01 · 24205135

Rakibul Hasan

Group Leader · Developer

Md. Mahedi Morshed
SERIAL 03 · 24205106

Md. Mahedi Morshed

Regular Member

Niloy
SERIAL 02 · 24205108

Niloy

Regular Member

Rafiul Islam
SERIAL 04 · 24205123

Rafiul Islam

Regular Member

Designed & Developed by Rakibul Hasan · Foundation X

ENGINEERING TRANSPARENCY

Methodology & project information

Weighted pavement condition model

Cracking 25% · Potholes 25% · Rutting 20% · Roughness 20% · Drainage 10%

Condition Score = 100 − Weighted Distress Score. The original Python engineering engine remains the single source of truth.
Classification thresholds

Good ≥ 85 · Fair ≥ 70 · Poor ≥ 40 · Critical < 40

AI screening is experimental and does not automatically modify engineering scores.