بنقرة واحدة
rvt-to-excel
Convert RVT/RFA files to Excel databases. Extract BIM element data, properties, and quantities.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
القائمة
Convert RVT/RFA files to Excel databases. Extract BIM element data, properties, and quantities.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Generate automated daily progress reports from site data. Track work completed, labor hours, equipment usage, and weather conditions.
Analyze labor productivity from site data. Compare planned vs actual, identify trends, benchmark against industry standards.
Create interactive KPI dashboards for construction projects. Track schedule, cost, quality, and safety metrics in real-time.
Detect and analyze geometric clashes in BIM models. Identify MEP, structural, and architectural conflicts before construction.
Classify BIM elements using AI and standard classification systems. Map elements to UniFormat, MasterFormat, OmniClass, and CWICR codes.
Generate comprehensive BIM model validation reports. Check data quality, completeness, and compliance with standards.
| name | rvt-to-excel |
| description | Convert RVT/RFA files to Excel databases. Extract BIM element data, properties, and quantities. |
| homepage | https://datadrivenconstruction.io |
| metadata | {"openclaw":{"emoji":"🏠","os":["win32"],"homepage":"https://datadrivenconstruction.io","requires":{"bins":["python3","RvtExporter"]}}} |
BIM data inside RVT files needs to be extracted for:
Convert RVT files to structured Excel databases for analysis and reporting.
RvtExporter.exe <input_path> [export_mode] [options]
| Mode | Categories | Description |
|---|---|---|
basic | 309 | Essential structural elements |
standard | 724 | Standard BIM categories |
complete | 1209 | All Revit categories |
custom | User-defined | Specific categories only |
| Option | Description |
|---|---|
bbox | Include bounding box coordinates |
rooms | Include room associations |
schedules | Export all schedules to sheets |
sheets | Export sheets to PDF |
# Basic export
RvtExporter.exe "C:\Projects\Building.rvt" basic
# Complete with bounding boxes
RvtExporter.exe "C:\Projects\Building.rvt" complete bbox
# Full export with all options
RvtExporter.exe "C:\Projects\Building.rvt" complete bbox rooms schedules sheets
# Batch processing
for /R "C:\Projects" %f in (*.rvt) do RvtExporter.exe "%f" standard bbox
import subprocess
import pandas as pd
from pathlib import Path
from typing import List, Optional
class RevitExporter:
def __init__(self, exporter_path: str = "RvtExporter.exe"):
self.exporter = Path(exporter_path)
if not self.exporter.exists():
raise FileNotFoundError(f"RvtExporter not found: {exporter_path}")
def convert(self, rvt_file: str, mode: str = "complete",
options: List[str] = None) -> Path:
"""Convert Revit file to Excel."""
rvt_path = Path(rvt_file)
if not rvt_path.exists():
raise FileNotFoundError(f"Revit file not found: {rvt_file}")
cmd = [str(self.exporter), str(rvt_path), mode]
if options:
cmd.extend(options)
result = subprocess.run(cmd, capture_output=True, text=True)
if result.returncode != 0:
raise RuntimeError(f"Export failed: {result.stderr}")
# Output file is same name with .xlsx extension
output_file = rvt_path.with_suffix('.xlsx')
return output_file
def batch_convert(self, folder: str, mode: str = "standard",
pattern: str = "*.rvt") -> List[Path]:
"""Convert all Revit files in folder."""
folder_path = Path(folder)
converted = []
for rvt_file in folder_path.glob(pattern):
try:
output = self.convert(str(rvt_file), mode)
converted.append(output)
print(f"Converted: {rvt_file.name}")
except Exception as e:
print(f"Failed: {rvt_file.name} - {e}")
return converted
def read_elements(self, xlsx_file: str) -> pd.DataFrame:
"""Read converted Excel as DataFrame."""
return pd.read_excel(xlsx_file, sheet_name="Elements")
def get_quantities(self, xlsx_file: str,
group_by: str = "Category") -> pd.DataFrame:
"""Get quantity summary grouped by category."""
df = self.read_elements(xlsx_file)
# Group and count
summary = df.groupby(group_by).agg({
'ElementId': 'count',
'Area': 'sum',
'Volume': 'sum'
}).reset_index()
summary.columns = [group_by, 'Count', 'Total_Area', 'Total_Volume']
return summary
| Sheet | Content |
|---|---|
| Elements | All BIM elements with properties |
| Categories | Element categories summary |
| Levels | Building levels |
| Materials | Material definitions |
| Parameters | Shared parameters |
| Column | Type | Description |
|---|---|---|
| ElementId | int | Unique Revit ID |
| Category | string | Element category |
| Family | string | Family name |
| Type | string | Type name |
| Level | string | Associated level |
| Area | float | Surface area (m²) |
| Volume | float | Volume (m³) |
| BBox_MinX/Y/Z | float | Bounding box min |
| BBox_MaxX/Y/Z | float | Bounding box max |
# Initialize exporter
exporter = RevitExporter("C:/Tools/RvtExporter.exe")
# Convert single file
xlsx = exporter.convert("C:/Projects/Office.rvt", "complete", ["bbox", "rooms"])
# Read and analyze
df = exporter.read_elements(str(xlsx))
print(f"Total elements: {len(df)}")
# Quantity summary
quantities = exporter.get_quantities(str(xlsx))
print(quantities)
# Export to CSV for further processing
df.to_csv("elements.csv", index=False)
# Full pipeline: Revit → Excel → Cost Estimate
from semantic_search import CWICRSemanticSearch
# 1. Convert Revit
exporter = RevitExporter()
xlsx = exporter.convert("project.rvt", "complete", ["bbox"])
# 2. Extract quantities
df = exporter.read_elements(str(xlsx))
quantities = df.groupby('Category')['Volume'].sum().to_dict()
# 3. Search CWICR for pricing
search = CWICRSemanticSearch()
costs = {}
for category, volume in quantities.items():
results = search.search_work_items(category, limit=5)
if not results.empty:
avg_price = results['unit_price'].mean()
costs[category] = volume * avg_price
print(f"Total estimate: ${sum(costs.values()):,.2f}")
basic for quick analysis, complete for full data