AI BIM Cost Estimation: Revit & IFC to Excel with n8n — n8n ワークフロー

複雑度 トリガー47個のノード AI👁 1回閲覧作成者:Artem Boiko

概要

Transform your pre-construction phase with this advanced AI-driven BIM cost estimation workflow. By bridging the gap between architectural models and financial forecasting, this automation eliminates hours of manual data entry and price research. The process begins by ingesting architectural data from Revit or IFC files, utilizing a local exporter when necessary. It then leverages the analytical power of GPT-4 and Claude to interpret complex element groups, applying real-time material pricing and quantity takeoff logic.

Unlike traditional spreadsheets, this workflow evaluates data confidence and enriches each line item with context-aware financial insights. The final output is a professional-grade suite of deliverables: a comprehensive multi-sheet Excel workbook for granular analysis and a polished HTML executive summary for stakeholder presentations. This is an essential tool for AEC firms looking to minimize human error in quantity surveying, accelerate the bidding process, and maintain high-accuracy cost projections using state-of-the-art LLM orchestration.

💡

Common Use Cases

  • Automated Quantity Surveying for rapid pre-construction bidding and tender preparation.
  • Real-time material price auditing to identify budget variances in large-scale Revit projects.
  • Generating executive-level financial feasibility reports directly from IFC architectural models.

使用ノード

CodeAI AgentAnthropic Chat ModelOpenAI Chat ModelOpenAIxAI Grok Chat Model

ワークフロープレビュー

⭐ If you find our tools helpful, please consi
Block 4: Price Calculation & Reporting
This block:
- Estimates costs using material types, volumes/areas,
- Creates charts: pie for material distribution, bar fo
Block 2: Element Classification
This block:
- Detects category fields (e.g., Category, IFC Type)
- Uses AI to classify as building elements (e.g., walls
or non-building (e.g., annotations,
Block 3: Material Analysis
This block:
- Processes building elements in batches
- Classifies materials by EU/DE/US standards
- Determines units (m³, m², etc.) and densities
- Uses AI (Anthropic) fo
Conversion Block
This block:
- Checks if Excel file exists from Revit project
- If not, runs converter to extract data
- If yes, skips to save time
Simply: Converts Revit file to Excel for analysi
Block 1: Data Loading & Grouping
This block:
- Loads Excel data
- Cleans headers
- Uses AI to decide aggregation (sum for quantities, me
- Groups data by parameter (e.
⚠️ Important Information
- Pipeline uses AI (OpenAI, Grok, Anthropic) - check cr
- Revit converter requires downloaded DDC_Converter_Rev
- Data is aggregated by groups, volumes
Project Cost Calculation for Revit and IFC wi
DataDrivenConstruction [GitHub](https://github.com/data
📝 Setup Instructions
1. In the 'Setup - Define file paths' node, specify:
- Path to converter (RvtExporter.exe)
- Path to project file (.rvt)
- Grouping parameter (group_by, e.g. 'Type Na
⬇️ Only modify the variables here
— everything else works automatically
model
W
When clicking ‘Execute w…
Find Category Fields1
Apply Classification to …
N
Non-Building Elements Ou…
AI Classify Categories1
I
Is Building Element1
C
Check If All Batches Done
Collect All Results
P
Process in Batches1
Clean Empty Values1
AI Agent Enhanced
Accumulate Results
Anthropic Chat Model1
Calculate Project Totals1
Enhance Excel Output
Prepare Excel Data
C
Create Excel File
Prepare Enhanced Prompts
Parse Enhanced Response
Generate HTML Report
Convert to Binary
Group Data with AI Rules1
Extract Headers and Data
AI Analyze All Headers
R
Read Excel File1
P
Parse Excel1
C
Create - Excel filename1
C
Check - Does Excel file …
I
If - File exists?1
E
Extract - Run converter1
I
Info - Skip conversion1
C
Check - Did extraction s…
E
Error - Show what went w…
S
Set xlsx_filename after …
M
Merge - Continue workflow1
S
Set Parameters1
Process AI Response1
Prepare HTML Path
W
Write HTML to Project Fo…
O
Open HTML in Browser
Prepare Excel Path1
W
Write Excel to Project F…
S
Setup - Define file paths
OpenAI Chat Model
xAI Grok Chat Model
O
On the standard 3D View
N
Non-3D View Elements Out…
47 nodes47 edges

仕組み

  1. 1

    トリガー

    このワークフローは トリガー トリガーで開始します。

  2. 2

    処理

    データは 47 個のノードを流れます, connecting agent, code, executecommand。

  3. 3

    出力

    ワークフローは自動化を完了し、設定された宛先に結果を配信します。

ノード詳細 (47)

CO

Code

code

#1
AI

AI Agent

n8n-nodes-langchain.agent

#2
AN

Anthropic Chat Model

n8n-nodes-langchain.lmChatAnthropic

#3
OP

OpenAI Chat Model

n8n-nodes-langchain.lmChatOpenAi

#4
OP

OpenAI

n8n-nodes-langchain.openAi

#5
XA

xAI Grok Chat Model

n8n-nodes-langchain.lmChatXAiGrok

#6

このワークフローのインポート方法

  1. 1右側の JSONをダウンロード ボタンをクリックしてワークフローファイルを保存します。
  2. 2n8nインスタンスを開き、ワークフロー → 新規 → ファイルからインポート に進みます。
  3. 3ダウンロードした estimate-construction-costs-from-revitifc-models-with-gpt-4-and-claude ファイルを選択し、インポートをクリックします。
  4. 4各サービスノードの 認証情報(APIキー、OAuthなど)を設定します。
  5. 5ワークフローをテスト をクリックして動作確認し、有効化します。

またはn8nの JSONからインポート に直接貼り付け:

{ "name": "AI BIM Cost Estimation: Revit & IFC to Excel with n8n", "nodes": [...], ...}

インテグレーション

agentcodeexecutecommandiflmchatanthropiclmchatopenailmchatxaigrokmanualtriggermergeopenaireadbinaryfilesetsplitinbatchesspreadsheetfilewritebinaryfile

このワークフローを取得

ワンクリックでダウンロード&インポート

JSONをダウンロードn8n.ioで見る
ノード47
複雑度high
トリガーtrigger
閲覧数1
カテゴリAI

作成者

Artem Boiko

Artem Boiko

@datadrivenconstruction

タグ

agentcodeexecutecommandiflmchatanthropiclmchatopenailmchatxaigrokmanualtriggermergeopenai

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