Detailed Elicitation Analysis — Technical Use Cases of IoT with Construction Machines
Detailed Elicitation Analysis — Technical Use Cases
DATA COLLECTION
Minimizing the Costs
A Construction Site project with a considerable duration, such as 6 month generates an enormous amount of information in the form of text, images and other data formats. Integrating Detection and Recognition based Algorithms with the help of Models will produce data that is forensic ready.
Manage Risks through a Risk Register and Record Delays.
READINESS TOWARDS DATA MANAGEMENT.
- Automated Error Checking and Validation
- Minimizing Material Wastage
MACHINERY METADATA
- Piling Rig Data from Specifications
- Importing Machinery into the Construction Site
CYBER INVESTIGATIONS
- Minimize the costs of investigations
- Promote Discoverability of Equipment Metadata
TRACEABILITY
- Trace-Back Specified Duration
- Connect Identified Risks to Detected
IoT METADATA
- EPC (Electronic Purchase Code) Number
- Maintenance Lifecycle Details
EVENT RECORD
- IoT Time-Series Data
- IoT and Machinery Metadata
- Machine Learning Activity Record Data
NETWORK TRAFFIC.
TRAFFIC ANALYZER
- Network Traffic Analysis
- Ensuring Accountability for Cost-Codes
PACKET DATA
- Database-codes and Device-codes
- Vision-based Productivity Improvement
SNMP MONITORING
- Supporting SNMP POLL Processes
- Improving Authentication Processes in Transfer of IoT Metadata from Machinery
EquipAny — MOBILE APPLICATION.
IDENTIFY RISK
- Traceability in Issue/Risk Register
- Reporting risks, issues and incidents; and Developing Risk/Issue Register
- Visualization of Incidents and Risks
SAS HACKATHON 2023
- Top 3 Finalist in Manufacturing Sector
- Finalist in IoT Sector
- 4 Team Members
HEALTH & SAFETY
- HSE Approval, Reporting and Team Building
- Workflow of HSE, Workforce and Project Management
MOTIVATION
- WSIS Forum 2023, DGA Hackathon — ITU
- Govstack.Github.io Framework
- Drift Detection on IoT Devices
DATABASE AND DEVICES
- Database-codes and Device-codes
- Machine Learning Driven, Construction Foundation Dataset Driven and Computer-Vision based
ORIGIN OF BRAND
- KlinterAI: From Clinker, meaning residue in concrete manufacturing.
- Multi-Factor Productivity vs Market output
- Construction Foundation Dataset with 14k+ images
Use Case: Code-based Representation
Observability by Parts
Integrating Safety at Sites
Verifiable Data Collected at the Client side
Supporting Insurances Related to Any Productivity Losses Detected
Enabling Equal Opportunities to the Client and Claimant
Handshake — Poll — Storage — Expose.
THE PROCESS OF DATA COLLECTION.
Risk Register and Issues Recorded.
THE PROCESS OF IENTIFYING RISKS.
Designed Logos
Research into Business Cases
GovStack Github Collaboration from ITU Framework
Architecture Developed from Digital Government Authority, GovStack Framework as per Dec 2022
Demonstration of a Sample Information Exchange within IoT Devices inside Machinery
For Developing the Issue Register towards Reporting Risks and Incidents at Construction Site, we developed a table of Issues with Case Names connecting at least 15 Activities or Objects Recognized for the purposes of Measuring Productivity Losses.
Improving Insights into Risks and Incidents
RISK COVERAGE THROUGHOUT SITE WORK
- We developed Synthetic Dataset using mockaroo.com and uploaded that to our Github Repository into Reported_Issues.csv file
At SAS Hackathon we were able to demonstrate the usage of such a Dataset
Certain Delays caused could be concentrated on for further improvement
Our Vision camera will have timestamp-based recordings of Computer Vision detected events
THE COMPETITIONS PHASE AND PROCEEDING FUNDING PHASE.
Click on the link below to keep a Meeting with me:
