AI-Based Bridge Sensor Data System
Budget: $250 – $750 USD
Phase 1) Data Ingestion framework & bridge classification
Goal of phase 1: Establish the core backend architecture for importing sensor data and preprocessing.
In this step, prime focus will be on creating a flexible data ingestion pipeline capable of pulling and parsing historical as well as real-time data from varying sensor vendors. Bridge type classification logic will be added to assign structure types (suspension, arch, beam) to every bridge in the system.
Phase 2) AI modeling engine & anomaly detection
Goal of phase 2: Build the predictive AI core to correlate bridge types and sensor behavior.
This phase involves training and creating deep learning models using the historical sensor data for each bridge type. The models will be trained to identify normal behavior and tag deviations as structural anomalies. All models will support multi-sensor inputs (tilts, vibrations, strains, temperatures), with results saved in structured insights. Dynamic comparison with week-long data streams will also be performed here.
Phase 3) Multi-client system design and access control
Goal of phase 3: Enable system scalability to multiple clients and bridge portfolios.
In this phase, multi-tenancy logic will be incorporated. Every customer will contain their own bridge data, sensors, AI outcomes, and reports. High-performing role-based access control (RBAC) will be implemented for user role handling (admin, engineer, viewer) for each company.
Phase 4) Web GUI development & Interactive reporting
Goal of phase 4: Create an end-to-end working, client-presenting frontend to render insights.
The platform's frontend will be constructed using React (or an equivalent up-to-date framework) along with the backend APIs. The UI will incorporate dashboards for sensor status, anomaly detection, model confidence, and bridge-level aggregations.
Phase 5) Import sensor modules & manufacturer Integration
Goal of phase 5: To standardize sensor data from various manufacturers into one uniform schema.
In this phase, sensor-agnostic importers will be implemented. These importers will convert multiple formats (JSON, CSV, API) to the system's standardized time-series format, handling preprocessing rules, calibration parameters, and time sync.
Phase 6: usage-based billing & payment system
Goal of phase 6: To monetize the platform with automated billing based on client usage.
The final phase will encompass metering API request support, storage usage, sensor data volume, and AI processing per user. There will be a billing backend to calculate usage and generate invoices, and third-party payment gateway integration (e.g., Stripe) for subscription or pay-per-use billing.
Goal of phase 1: Establish the core backend architecture for importing sensor data and preprocessing.
In this step, prime focus will be on creating a flexible data ingestion pipeline capable of pulling and parsing historical as well as real-time data from varying sensor vendors. Bridge type classification logic will be added to assign structure types (suspension, arch, beam) to every bridge in the system.
Phase 2) AI modeling engine & anomaly detection
Goal of phase 2: Build the predictive AI core to correlate bridge types and sensor behavior.
This phase involves training and creating deep learning models using the historical sensor data for each bridge type. The models will be trained to identify normal behavior and tag deviations as structural anomalies. All models will support multi-sensor inputs (tilts, vibrations, strains, temperatures), with results saved in structured insights. Dynamic comparison with week-long data streams will also be performed here.
Phase 3) Multi-client system design and access control
Goal of phase 3: Enable system scalability to multiple clients and bridge portfolios.
In this phase, multi-tenancy logic will be incorporated. Every customer will contain their own bridge data, sensors, AI outcomes, and reports. High-performing role-based access control (RBAC) will be implemented for user role handling (admin, engineer, viewer) for each company.
Phase 4) Web GUI development & Interactive reporting
Goal of phase 4: Create an end-to-end working, client-presenting frontend to render insights.
The platform's frontend will be constructed using React (or an equivalent up-to-date framework) along with the backend APIs. The UI will incorporate dashboards for sensor status, anomaly detection, model confidence, and bridge-level aggregations.
Phase 5) Import sensor modules & manufacturer Integration
Goal of phase 5: To standardize sensor data from various manufacturers into one uniform schema.
In this phase, sensor-agnostic importers will be implemented. These importers will convert multiple formats (JSON, CSV, API) to the system's standardized time-series format, handling preprocessing rules, calibration parameters, and time sync.
Phase 6: usage-based billing & payment system
Goal of phase 6: To monetize the platform with automated billing based on client usage.
The final phase will encompass metering API request support, storage usage, sensor data volume, and AI processing per user. There will be a billing backend to calculate usage and generate invoices, and third-party payment gateway integration (e.g., Stripe) for subscription or pay-per-use billing.