CIVIL ENGINEERING · DECISION SUPPORT

Home Dashboard

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
DATA PIPELINE

Upload your road data

CSV · Schema validation · Python calculations
⇧

Upload pavement survey data

Select a CSV file first. Then click the Analyze Dataset button to start the analysis.

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
ROAD SECTIONS ▤
—Sections in latest analysis
AVERAGE CONDITION ◉
—/100Network condition index
HIGHEST PRIORITY ⚑
—Lowest condition score
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.
ROAD INTELLIGENCE

Road Analysis

Analyze pavement condition data

Import a road-condition CSV dataset, validate its structure and run the Python-based pavement analysis. Results update the Home Dashboard, condition charts and maintenance ranking.

ROAD SECTIONS
—Sections in latest analysis
AVERAGE CONDITION
—Network condition index
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 · FREE PUBLIC MODEL

AI pavement distress detection

EXPERIMENTAL
◎

Road Hazard Detection — YOLO11n

Use the public browser-based detector below. It runs the model on your device with ONNX and WebAssembly; no RoadVision Pro AI server, Hugging Face subscription or API key is required.

PotholeLongitudinal crackTransverse crackAlligator crack
Open detector in a new tab ↗Public demo by Vivek Vagale

If the detector does not appear inside this panel, use “Open detector in a new tab”. The public demo is hosted and maintained by its author; availability and model downloads depend on that service.

ENGINEERING SAFEGUARDS & MODEL LIMITATIONS

AI output is a preliminary screening result, not an engineering measurement.

  • Detected damage must be checked by a human engineer.
  • The model was trained on Indian RDD2022 road images, not specifically on Bangladeshi roads.
  • Published held-out test mAP@0.5 is approximately 0.386; transverse-crack detection is particularly weak.
  • Do not convert image pixels into crack area, pothole depth or maintenance quantities without field measurement and calibration.
  • AI results do not automatically change RoadVision Pro's original pavement condition score.
Model source and evaluation: VivekVagale/road-hazard on GitHub.
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.
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.