Syed Mahdi Rehan

Ai Engineer @Engineering Consultants International Pvt Ltd

Designed and implemented an AI-based pavement inspection system integrating computer vision, machine learning, OCR (Optical Character Recognition), and GIS mapping to automate road condition assessments. Built and trained a AI model on annotated distress instances across 17 distress classes (alligator cracks, potholes, raveling, block cracking, patching, etc.) with severity classification (low, medium, high) and accurate distress dimensions (length, width, area and distress count), achieving strong detection accuracy of 81.5%(mAP50: 0.815). Automated PCI (Pavement Condition Index) computation using ASTM D6433 standard to analyze pavement conditions, prioritize maintenance needs, and support the creation of data-driven maintenance management plans, with outputs seamlessly integrated into a comprehensive web application UI. Implemented geospatial mapping outputs (Excel, shapefile, KML, and interactive GIS maps) for visualization of distress and PCI values, supporting data-driven maintenance planning. Designed and delivered a unified we-based Pavement Evaluation System application that integrates automated pavement distress detection, distress dimension analysis, PCI evaluation and geospatial visualization into a single workflow; enabling engineers to interactively view, analyze, and manage pavement condition data through dynamic maps, tables, and dashboards, resulting in streamlined infrastructure asset management.