Chronological Knowledge Retrieval: A Retrieval-Augmented Generation Approach to Construction Project Documentation

TL;DR

This paper introduces a retrieval-augmented generation method enabling professionals to conversationally access and explore the chronological history of decisions in construction project documentation.

Contribution

It presents a novel RAG framework tailored for time-annotated construction records, with an open-source implementation and a specialized dataset for evaluation.

Findings

Effective retrieval of decision history through natural language queries

Semantic search combined with large language models improves response relevance

Open-source tools and dataset facilitate further research in this domain

Abstract

In large-scale construction projects, the continuous evolution of decisions generates extensive records, most often captured in meeting minutes. Since decisions may override previous ones, professionals often need to reconstruct the history of specific choices. Retrieving such information manually from raw archives is both labor-intensive and error-prone. From a user perspective, we address this challenge by enabling conversational access to the whole set of project meeting minutes. Professionals can pose natural-language questions and receive answers that are both semantically relevant and explicitly time-annotated, allowing them to follow the chronology of decisions. From a technical perspective, our solution employs a Retrieval-Augmented Generation (RAG) framework that integrates semantic search with large language models to ensure accurate and context-aware responses. We demonstrate the approach using an anonymized, industry-sourced dataset of meeting minutes from a completed construction project by a large company in Belgium. The dataset is annotated and enriched with expert-defined queries to support systematic evaluation. Both the dataset and the open-source implementation are made available to the community to foster further research on conversational access to time-annotated project documentation.