Predictive Change Event Generation for Construction Project Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing construction project management software places the burden on users to determine when to create change events, leading to potential delays and inefficiencies due to untimely documentation and lack of guidance on necessary information inclusion.
Innovation Solution
Implementing predictive analytics software engines to automatically predict the need for and assist in creating change events by analyzing project data, recommending necessary information, and facilitating the creation of change orders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually determine and document change events, then flexibility and control are maintained, but delays and inefficiencies occur due to untimely documentation
Solution Approach 1:
The system performs preliminary analysis of project data to predict potential change events before they formally occur. By analyzing current project status, historical data, and contextual information, the system proactively identifies and documents change events in advance, eliminating delays associated with manual user recognition and documentation.
Solution Approach 2:
The system serves itself by automatically monitoring project data, predicting change events, and generating documentation without requiring user intervention. The predictive analytics engine continuously processes project information and autonomously creates change event records, freeing users from the burden of manual determination while ensuring timely documentation.
2Productivity
If users are responsible for identifying all necessary information for change events, then accuracy can be maintained, but efficiency decreases due to lack of guidance
Solution Approach 1:
The system provides feedback to users by analyzing project data and recommending specific information that should be included in change event documentation. The predictive analytics engine processes current project status and historical data to generate targeted suggestions, ensuring users have guidance on necessary information while maintaining accuracy through system-validated recommendations.
Solution Approach 2:
The system acts as an intermediary between raw project data and final change event documentation. It processes and analyzes project information, then presents synthesized recommendations to users that bridge the gap between available data and required documentation elements, improving both efficiency and information completeness.
3Reliability
If comprehensive analysis is performed to predict change events accurately, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The predictive analytics system is segmented into modular components that handle different aspects of analysis separately. Each module processes specific types of project data and contributes to the overall prediction, allowing comprehensive analysis to be performed through coordinated simple operations rather than a single complex process, thereby maintaining reliability while managing system complexity.
Data Source
AI summary
Based on receiving data defining a new data item for a construction project corresponding to a particular category of data items, a computing system (1) automatically: (i) predicts that a change event for the construction project is needed by inputting the new data item into a first machine learning model trained to predict a need for a change event from data items corresponding to certain categories of data items, including the particular category of the new data item, (ii) determines initial recommended data for the predicted change event, and (iii) determines additional data for the predicted change event corresponding to a particular class of additional data by inputting the initial recommended data for the predicted change event into a second machine learning model trained to predict one or more classes of additional data for a change event, and (2) automatically create a data item representing the predicted change event.