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AI-Driven Construction Site Management and Risk Detection Platform
  1. case
  2. AI-Driven Construction Site Management and Risk Detection Platform

AI-Driven Construction Site Management and Risk Detection Platform

leobit.com
Construction
Real estate
Infrastructure Development

Manual Construction Progress Tracking and Delayed Issue Detection

Construction teams face inefficiencies due to manual site inspections requiring physical visits by foremen, leading to delayed progress reporting, overlooked quality issues, and resource allocation delays. Existing processes lack proactive risk detection capabilities and decentralized reporting mechanisms for field workers.

About the Client

Leading Nordic construction firm specializing in residential, commercial, and public infrastructure projects with dedicated R&D focus on construction process innovation

Automate Progress Tracking and Proactive Risk Management

  • Implement automated AI-powered progress tracking across multiple construction sites
  • Enable real-time decentralized reporting for field workers
  • Develop predictive AI models for structural defect detection
  • Reduce manual site visit requirements by 70%
  • Improve resource planning accuracy through proactive material request system

Core System Functionalities and Key Features

  • Flutter-based cross-platform mobile application with BLoC architecture
  • Real-time progress reporting with photo documentation
  • AI-powered defect detection (cracks, alignment issues, curvature)
  • AR-assisted photo capture guidance using ARCore/ARKit
  • Offline data caching with SQLite (Drift library) and background sync
  • Azure-integrated material request workflow
  • Predictive risk alerts with manual verification override

Technology Stack Requirements

Flutter SDK
TensorFlow
PyTorch
Microsoft Azure Cloud
.NET Core
Azure Blob Storage
AR Flutter Plugin

System Integration Requirements

  • Azure Active Directory Authentication
  • TensorFlow/PyTorch AI models on Azure GPU servers
  • ARCore/ARKit for mobile AR functionality
  • Azure DevOps for CI/CD pipeline
  • Firebase Cloud Messaging for push notifications

Non-Functional Requirements

  • Cloud scalability for concurrent site monitoring
  • Offline-first design for low-connectivity environments
  • Role-based access control with Azure AD
  • GPU-accelerated AI processing performance
  • Construction worker-friendly UI/UX design

Enhanced Operational Efficiency and Risk Mitigation in Construction Projects

Implementation of AI-powered progress tracking is expected to reduce manual site visits by foremen by 70%, enable early detection of 92.7% of structural defects, and improve resource allocation efficiency through real-time material request workflows. The AR-assisted photo system will ensure 98%+ compliance with AI analysis requirements while maintaining seamless operations in low-connectivity environments.

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