Conversational LCA: Using AI to Build a Learning Community for Sustainable Design

Main Presenter:    Ming Hu 

Co-Authors:                                                  

Background and Relevance to Life Cycle Innovation:
The building sector is a major contributor to the global polycrisis of climate change and resource depletion. While Life Cycle Assessment (LCA) is a vital tool for quantifying environmental impacts, its complexity and inaccessibility prevent integration into early-stage design, where the most consequential decisions are made. Current BIM-LCA integrations often rely on rigid, expert-centric tools that offer limited interpretability and fail to facilitate collaborative, multi-stakeholder decision-making.

Objective or Research Question:
This research asks: How can we democratize LCA by creating an accessible, interpretable, and interactive tool that empowers diverse stakeholders—architects, engineers, and policymakers—to collaboratively navigate sustainability trade-offs during early building design?

Approach and Methods:
We developed a novel AI-augmented framework that seamlessly integrates with Autodesk Revit. The system automates material quantity takeoff and inventory modeling via the OpenLCA IPC server. A key innovation is a dynamic endpoint assessment module that allows users to adjust the time horizon, reflecting different cultural perspectives on long-term risk. An integrated conversational AI (GPT-4o) serves as a natural language interface, enabling users to query results, explore “what-if” scenarios, and receive plain-language interpretations and visualizations of complex LCA data.

Key Findings or Expected Results:
In a case study of a residential building, the framework reproduced expert-level SimaPro LCA results with high accuracy, showing less than 2.1% variation across impact indicators. The AI interface successfully interpreted user queries, generated dynamic graphs (e.g., GWP trends over 20-1000 years), and explained endpoint damages (Human Health, Ecosystem quality) in accessible terms. This demonstrates a functional pipeline from BIM data to stakeholder-relevant insights without requiring LCA expertise.

Novelty or Significance of the Work:
This work is significant for its threefold contribution: 1) full automation of the BIM-to-LCA workflow, 2) introduction of a customizable, dynamic endpoint assessment to reflect value-based perspectives, and 3) deployment of a conversational AI to translate complex LCA results into actionable intelligence. It transforms LCA from a static, post-design audit into a dynamic, participatory process for co-creating sustainable solutions.

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