Intent-Based Construction Simulation for Dynamic Project Planning
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Solution Overview
Problem
Current AEC software solutions struggle to adapt to dynamic construction project inputs, failing to comprehend user intent and provide real-time insights due to reliance on manual and rule-based approaches, which limits their ability to handle diverse and unpredictable factors impacting construction schedules and designs.
Innovation Solution
An AI-based system that determines user intent from various input formats, generates scenarios, evaluates outcomes based on project objectives, and provides model recommendations through a controller that processes multiple input streams and employs ensemble learning to optimize construction processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual and rule-based approaches are used in AEC software, then the system structure remains simple and easy to implement, but the system cannot adapt to dynamic construction project inputs and fails to comprehend user intent
Solution Approach 1:
The patent replaces manual and rule-based mechanical processing systems with an AI-based cognitive system that can comprehend user intent and dynamically adapt to construction project inputs. The controller uses machine learning models to process natural language queries and generate scenario-based recommendations, substituting rigid rule-based logic with flexible intelligent processing.
Solution Approach 2:
The system transitions from static rule-based processing to dynamic adaptive processing by continuously learning from user inputs and project data. The AI model adjusts its behavior based on user intent and generates different scenarios dynamically, allowing the system to adapt to changing construction project requirements in real-time.
2Productivity
If AI-based intent processing is implemented, then the system can comprehend user intent and provide real-time insights, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and understanding user intent before generating full scenario analyses. The controller identifies key parameters and constraints from user queries in advance, allowing faster generation of scenario comparisons and recommendations without requiring complete re-analysis of all project data.
Solution Approach 2:
The AI processing is segmented into distinct stages: intent determination, parameter extraction, scenario generation, and recommendation formulation. This segmentation allows parallel processing of different aspects and optimizes computational resource allocation, reducing overall processing time while maintaining comprehensive analysis capabilities.
3Measurement precision
If multiple input streams are processed to determine user intent, then the system accuracy improves, but the device complexity and processing requirements increase
Solution Approach 1:
The controller is designed as a universal processing system that handles multiple input stream types (natural language queries, project parameters, constraints) through a single AI-based intent determination mechanism. This multi-functional approach consolidates complexity into a unified processing architecture rather than requiring separate systems for each input type.
Data Source
AI summary
A method for generating a model recommendation in a computing environment is disclosed. The method comprises determining a user intent based on a user input for executing at least one intended task by the user, converting the determined user intent to one or more machine executable instructions, generating a plurality of scenarios based on the one or more machine executable instructions, evaluating an outcome of each of the plurality of scenarios by mapping it to one or more project objectives associated with the at least one intended task, and generating at least one model recommendation associated with the user intent based on the evaluation.