excel-to-bim
Push Excel data back to BIM models. Update parameters, properties, and attributes from structured spreadsheets.
git clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction /tmp/excel-to-bim && cp -r /tmp/excel-to-bim/1_DDC_Toolkit/CAD-Converters/excel-to-bim ~/.claude/skills/excel-to-bimSKILL.md
# Excel to BIM Update
## Business Case
### Problem Statement
After extracting BIM data to Excel and enriching it (cost codes, classifications, custom data):
- Changes need to flow back to the BIM model
- Manual re-entry is error-prone
- Updates must match by element ID
### Solution
Push Excel data back to BIM models, updating element parameters and properties from spreadsheet changes.
### Business Value
- **Bi-directional workflow** - BIM → Excel → BIM
- **Bulk updates** - Change thousands of parameters
- **Data enrichment** - Add classifications, codes, costs
- **Consistency** - Spreadsheet as single source of truth
## Technical Implementation
### Workflow
```
BIM Model (Revit/IFC) → Excel Export → Data Enrichment → Excel Update → BIM Model
```
### Python Implementation
```python
import pandas as pd
from pathlib import Path
from typing import Dict, Any, List, Optional, Tuple
from dataclasses import dataclass, field
from enum import Enum
import json
class UpdateType(Enum):
"""Type of BIM parameter update."""
TEXT = "text"
NUMBER = "number"
BOOLEAN = "boolean"
ELEMENT_ID = "element_id"
@dataclass
class ParameterMapping:
"""Mapping between Excel column and BIM parameter."""
excel_column: str
bim_parameter: str
update_type: UpdateType
transform: Optional[str] = None # Optional transformation
@dataclass
class UpdateResult:
"""Result of single element update."""
element_id: str
parameters_updated: List[str]
success: bool
error: Optional[str] = None
@dataclass
class BatchUpdateResult:
"""Result of batch update operation."""
total_elements: int
updated: int
failed: int
skipped: int
results: List[UpdateResult]
class ExcelToBIMUpdater:
"""Update BIM models from Excel data."""
# Standard ID column names
ID_COLUMNS = ['ElementId', 'GlobalId', 'GUID', 'Id', 'UniqueId']
def __init__(self):
self.mappings: List[ParameterMapping] = []
def add_mapping(self, excel_col: str, bim_param: str,
update_type: UpdateType = UpdateType.TEXT):
"""Add column to parameter mapping."""
self.mappings.append(ParameterMapping(
excel_column=excel_col,
bim_parameter=bim_param,
update_type=update_type
))
def load_excel(self, file_path: str,
sheet_name: str = None) -> pd.DataFrame:
"""Load Excel data for update."""
if sheet_name:
return pd.read_excel(file_path, sheet_name=sheet_name)
return pd.read_excel(file_path)
def detect_id_column(self, df: pd.DataFrame) -> Optional[str]:
"""Detect element ID column in DataFrame."""
for col in self.ID_COLUMNS:
if col in df.columns:
return col
# Case-insensitive check
for df_col in df.columns:
if df_col.lower() == col.lower():
return df_col
return None
def prepare_updates(self, df: pd.DataFrame,
id_column: str = None) -> List[Dict[str, Any]]:
"""Prepare update instructions from DataFrame."""
if id_column is None:
id_column = self.detect_id_column(df)
if id_column is None:
raise ValueError("Cannot detect ID column")
updates = []
for _, row in df.iterrows():
element_id = str(row[id_column])
params = {}
for mapping in self.mappings:
if mapping.excel_column in df.columns:
value = row[mapping.excel_column]
# Convert value based on type
if mapping.update_type == UpdateType.NUMBER:
value = float(value) if pd.notna(value) else 0
elif mapping.update_type == UpdateType.BOOLEAN:
value = bool(value) if pd.notna(value) else False
elif mapping.update_type == UpdateType.TEXT:
value = str(value) if pd.notna(value) else ""
params[mapping.bim_parameter] = value
if params:
updates.append({
'element_id': element_id,
'parameters': params
})
return updates
def generate_dynamo_script(self, updates: List[Dict],
output_path: str) -> str:
"""Generate Dynamo script for Revit updates."""
# Generate Python code for Dynamo
script = '''
# Dynamo Python Script for Revit Parameter Updates
# Generated by DDC Excel-to-BIM
import clr
clr.AddReference('RevitAPI')
clr.AddReference('RevitServices')
from RevitServices.Persistence import DocumentManager
from RevitServices.Transactions import TransactionManager
from Autodesk.Revit.DB import *
doc = DocumentManager.Instance.CurrentDBDocument
# Update data
updates = '''
script += json.dumps(updates, indent=2)
script += '''
# Apply updates
TransactionManager.Instance.EnsureInTransaction(doc)
results = []
for update in updates:
try:
element_id = int(update['element_id'])
element = doc.GetElement(ElementId(element_id))
if element:
for param_name, value in update['parameters'].items():
param = element.LookupParameter(param_name)
if param and not param.IsReadOnly:
if isinstance(value, (int, float)):
param.Set(float(value))
elif isinstance(value, bool):
param.Set(1 if value else 0)
else:
param.Set(str(value))
results.append({'id': element_id, 'status': 'success'})
else:
results.append({'id': element_id, 'status': 'not found'})
except Exception as e:
results.append({'id': update['element_id'], 'status': str(e)})
TransactionManager.Instance.TranGenerate 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.
Calculate CO2 emissions and carbon footprint from BIM model data. Analyze embodied carbon by material, element, and building system.
Extract quantities from IFC/Revit models for quantity takeoff. Uses DDC converters to get element counts, areas, volumes, lengths with grouping and reporting.