kbarbel640-del 4D Simulation for Construction
Overview
Based on DDC methodology (Chapter 3.3), this skill implements 4D BIM simulations - linking 3D model elements to the 4th dimension: time. Visualize construction sequences, detect scheduling conflicts, and optimize work phasing.
Book Reference: "4D, 6D-8D и расчет CO2" / "4D-8D BIM and CO2 Calculation"
"4D моделирование позволяет визуализировать последовательность строительства и выявлять конфликты на этапе планирования." — DDC Book, Chapter 3.3
Quick Start
import pandas as pd
from datetime import datetime, timedelta
# BIM elements with schedule data
elements = pd.DataFrame({
'ElementId': ['E001', 'E002', 'E003', 'E004'],
'Category': ['Foundation', 'Column', 'Beam', 'Slab'],
'Level': ['Level 0', 'Level 1', 'Level 1', 'Level 1'],
'Start_Date': ['2024-01-01', '2024-01-15', '2024-02-01', '2024-02-15'],
'End_Date': ['2024-01-14', '2024-01-31', '2024-02-14', '2024-02-28'],
'Phase': ['Structure', 'Structure', 'Structure', 'Structure']
})
elements['Start_Date'] = pd.to_datetime(elements['Start_Date'])
elements['End_Date'] = pd.to_datetime(elements['End_Date'])
elements['Duration_Days'] = (elements['End_Date'] - elements['Start_Date']).dt.days
# Get elements active on a specific date
target_date = pd.to_datetime('2024-01-20')
active_elements = elements[
(elements['Start_Date'] <= target_date) &
(elements['End_Date'] >= target_date)
]
print(f"Elements under construction on {target_date.date()}:")
print(active_elements[['ElementId', 'Category']])
4D Data Model
Schedule-Element Linking
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from typing import List, Dict, Optional
class ScheduleElementLinker:
"""Link BIM elements to schedule activities"""
def __init__(self, elements_df: pd.DataFrame, schedule_df: pd.DataFrame):
self.elements = elements_df.copy()
self.schedule = schedule_df.copy()
self.links = pd.DataFrame()
def auto_link_by_category(self, mapping: Dict[str, str]):
"""Auto-link elements to activities by category mapping
Args:
mapping: Dict mapping element categories to activity names
e.g., {'Wall': 'Structural Walls', 'Slab': 'Floor Construction'}
"""
links = []
for category, activity_name in mapping.items():
# Find elements of this category
category_elements = self.elements[
self.elements['Category'] == category
]['ElementId'].tolist()
# Find matching activity
activity = self.schedule[
self.schedule['Activity'].str.contains(activity_name, case=False)
]
if not activity.empty and category_elements:
for elem_id in category_elements:
links.append({
'ElementId': elem_id,
'ActivityId': activity.iloc[0]['ActivityId'],
'Activity': activity.iloc[0]['Activity'],
'Start_Date': activity.iloc[0]['Start_Date'],
'End_Date': activity.iloc[0]['End_Date']
})
self.links = pd.DataFrame(links)
return self.links
def auto_link_by_level(self):
"""Auto-link elements based on level and construction sequence"""
# Get unique levels in order
levels = sorted(self.elements['Level'].unique())
links = []
for i, level in enumerate(levels):
level_elements = self.elements[self.elements['Level'] == level]
# Find activity for this level
level_activity = self.schedule[
self.schedule['Activity'].str.contains(level, case=False)
]
if not level_activity.empty:
for _, elem in level_elements.iterrows():
links.append({
'ElementId': elem['ElementId'],
'ActivityId': level_activity.iloc[0]['ActivityId'],
'Activity': level_activity.iloc[0]['Activity'],
'Start_Date': level_activity.iloc[0]['Start_Date'],
'End_Date': level_activity.iloc[0]['End_Date']
})
self.links = pd.DataFrame(links)
return self.links
def manual_link(self, element_id: str, activity_id: str):
"""Manually link element to activity"""
element = self.elements[self.elements['ElementId'] == element_id]
activity = self.schedule[self.schedule['ActivityId'] == activity_id]
if element.empty or activity.empty:
raise ValueError("Element or activity not found")
new_link = pd.DataFrame([{
'ElementId': element_id,
'ActivityId': activity_id,
'Activity': activity.iloc[0]['Activity'],
'Start_Date': activity.iloc[0]['Start_Date'],
'End_Date': activity.iloc[0]['End_Date']
}])
self.links = pd.concat([self.links, new_link], ignore_index=True)
return self.links
def get_linked_elements(self):
"""Get elements with schedule data"""
return self.elements.merge(
self.links[['ElementId', 'ActivityId', 'Start_Date', 'End_Date']],
on='ElementId',
how='left'
)
4D Simulation Engine
class Simulation4D:
"""4D construction simulation engine"""
def __init__(self, linked_elements: pd.DataFrame):
self.elements = linked_elements.copy()
self.elements['Start_Date'] = pd.to_datetime(self.elements['Start_Date'])
self.elements['End_Date'] = pd.to_datetime(self.elements['End_Date'])
self.project_start = self.elements['Start_Date'].min()
self.project_end = self.elements['End_Date'].max()
def get_state_at_date(self, target_date: datetime) -> pd.DataFrame:
"""Get element states at a specific date"""
target = pd.to_datetime(target_date)
# Determine state for each element
conditions = [
target < self.elements['Start_Date'], # Not started
(self.elements['Start_Date'] <= target) & (target <= self.elements['End_Date']), # In progress
target > self.elements['End_Date'] # Completed
]
choices = ['not_started', 'in_progress', 'completed']
self.elements['State'] = np.select(conditions, choices, default='unknown')
return self.elements.copy()
def generate_timeline(self, interval_days: int = 7) -> List[Dict]:
"""Generate timeline snapshots"""
timeline = []
current_date = self.project_start
while current_date <= self.project_end:
state = self.get_state_at_date(current_date)
snapshot = {
'date': current_date,
'not_started': len(state[state['State'] == 'not_started']),
'in_progress': len(state[state['State'] == 'in_progress']),
'completed': len(state[state['State'] == 'completed']),
'total': len(state)
}
snapshot['progress_pct'] = (snapshot['completed'] / snapshot['total']) * 100
timeline.append(snapshot)
current_date += timedelta(days=interval_days)
return timeline
def get_elements_in_progress(self, target_date: datetime) -> pd.DataFrame:
"""Get elements currently under construction"""
state = self.get_state_at_date(target_date)
return state[state['State'] == 'in_progress']
def analyze_construction_sequence(self) -> pd.DataFrame:
"""Analyze construction sequence by category and level"""
sequence = self.elements.groupby(['Level', 'Category']).agg({
'Start_Date': 'min',
'End_Date': 'max',
'ElementId': 'count'
}).rename(columns={'ElementId': 'Element_Count'})
sequence['Duration_Days'] = (sequence['End_Date'] - sequence['Start_Date']).dt.days
sequence = sequence.sort_values('Start_Date').reset_index()
return sequence
def detect_parallel_work(self) -> pd.DataFrame:
"""Detect work happening in parallel"""
dates = pd.date_range(self.project_start, self.project_end, freq='D')
parallel_work = []
for date in dates:
state = self.get_state_at_date(date)
in_progress = state[state['State'] == 'in_progress']
if len(in_progress) > 1:
categories = in_progress['Category'].unique().tolist()
levels = in_progress['Level'].unique().tolist()
parallel_work.append({
'date': date,
'parallel_count': len(in_progress),
'categories': ', '.join(categories),
'levels': ', '.join(levels)
})
return pd.DataFrame(parallel_work)
Gantt Chart Integration
Gantt Chart Generator
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.patches import Patch
class GanttChartGenerator:
"""Generate Gantt charts for 4D simulation"""
def __init__(self, elements: pd.DataFrame):
self.elements = elements.copy()
self.colors = {
'Foundation': '#8B4513',
'Column': '#4169E1',
'Beam': '#228B22',
'Slab': '#DC143C',
'Wall': '#FF8C00',
'Roof': '#9932CC',
'MEP': '#20B2AA',
'Finishes': '#FFD700'
}
def create_gantt(self, group_by='Category', figsize=(14, 8)):
"""Create Gantt chart grouped by specified column"""
fig, ax = plt.subplots(figsize=figsize)
# Group elements
groups = self.elements.groupby(group_by)
y_pos = 0
y_labels = []
legend_elements = []
for group_name, group_df in groups:
color = self.colors.get(group_name, '#808080')
for _, row in group_df.iterrows():
start = row['Start_Date']
duration = (row['End_Date'] - row['Start_Date']).days
ax.barh(y_pos, duration, left=start, height=0.6,
color=color, alpha=0.8, edgecolor='black', linewidth=0.5)
y_pos += 1
y_labels.append(group_name)
legend_elements.append(Patch(facecolor=color, label=group_name))
# Formatting
ax.set_yticks(range(len(self.elements)))
ax.set_yticklabels(self.elements['ElementId'])
ax.set_xlabel('Date')
ax.set_title('Construction Schedule - Gantt Chart', fontsize=14, fontweight='bold')
# Date formatting
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax.xaxis.set_major_locator(mdates.WeekdayLocator(interval=2))
plt.xticks(rotation=45)
# Legend
ax.legend(handles=legend_elements, loc='upper right')
# Grid
ax.grid(axis='x', alpha=0.3)
plt.tight_layout()
return fig
def create_level_gantt(self, figsize=(14, 10)):
"""Create Gantt chart grouped by level"""
fig, ax = plt.subplots(figsize=figsize)
levels = sorted(self.elements['Level'].unique())
for i, level in enumerate(levels):
level_elements = self.elements[self.elements['Level'] == level]
for _, row in level_elements.iterrows():
color = self.colors.get(row['Category'], '#808080')
start = row['Start_Date']
duration = (row['End_Date'] - row['Start_Date']).days
ax.barh(i, duration, left=start, height=0.4,
color=color, alpha=0.8, edgecolor='black', linewidth=0.5)
ax.set_yticks(range(len(levels)))
ax.set_yticklabels(levels)
ax.set_xlabel('Date')
ax.set_ylabel('Building Level')
ax.set_title('Construction Sequence by Level', fontsize=14, fontweight='bold')
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
plt.xticks(rotation=45)
# Add legend
legend_patches = [Patch(color=c, label=cat) for cat, c in self.colors.items()
if cat in self.elements['Category'].values]
ax.legend(handles=legend_patches, loc='upper right')
plt.tight_layout()
return fig
def create_progress_chart(self, timeline: List[Dict], figsize=(12, 6)):
"""Create S-curve progress chart"""
df = pd.DataFrame(timeline)
fig, ax = plt.subplots(figsize=figsize)
ax.plot(df['date'], df['progress_pct'], 'b-', linewidth=2, label='Progress')
ax.fill_between(df['date'], 0, df['progress_pct'], alpha=0.3)
# Add milestones at 25%, 50%, 75%, 100%
for milestone in [25, 50, 75, 100]:
ax.axhline(y=milestone, color='gray', linestyle='--', alpha=0.5)
ax.text(df['date'].iloc[0], milestone + 2, f'{milestone}%',
fontsize=9, color='gray')
ax.set_xlabel('Date')
ax.set_ylabel('Progress (%)')
ax.set_title('Project S-Curve', fontsize=14, fontweight='bold')
ax.set_ylim(0, 105)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
plt.xticks(rotation=45)
plt.tight_layout()
return fig
Sequence Analysis
Construction Sequence Optimizer
class SequenceAnalyzer:
"""Analyze and optimize construction sequences"""
def __init__(self, elements: pd.DataFrame):
self.elements = elements.copy()
self.dependencies = []
def add_dependency(self, predecessor: str, successor: str, lag_days: int = 0):
"""Add dependency between elements"""
self.dependencies.append({
'predecessor': predecessor,
'successor': successor,
'lag_days': lag_days
})
def check_sequence_violations(self) -> List[Dict]:
"""Check for sequence violations"""
violations = []
for dep in self.dependencies:
pred = self.elements[self.elements['ElementId'] == dep['predecessor']]
succ = self.elements[self.elements['ElementId'] == dep['successor']]
if pred.empty or succ.empty:
continue
pred_end = pred.iloc[0]['End_Date']
succ_start = succ.iloc[0]['Start_Date']
required_start = pred_end + timedelta(days=dep['lag_days'])
if succ_start < required_start:
violations.append({
'predecessor': dep['predecessor'],
'successor': dep['successor'],
'predecessor_end': pred_end,
'successor_start': succ_start,
'required_start': required_start,
'violation_days': (required_start - succ_start).days
})
return violations
def check_spatial_conflicts(self) -> List[Dict]:
"""Check for spatial conflicts (same location, same time)"""
conflicts = []
# Group by location (Level + Zone if available)
location_col = 'Level'
for location, group in self.elements.groupby(location_col):
if len(group) < 2:
continue
# Check for overlapping work
for i, row1 in group.iterrows():
for j, row2 in group.iterrows():
if i >= j:
continue
# Check date overlap
overlap = (row1['Start_Date'] <= row2['End_Date'] and
row2['Start_Date'] <= row1['End_Date'])
if overlap:
conflicts.append({
'location': location,
'element1': row1['ElementId'],
'element1_category': row1['Category'],
'element2': row2['ElementId'],
'element2_category': row2['Category'],
'overlap_start': max(row1['Start_Date'], row2['Start_Date']),
'overlap_end': min(row1['End_Date'], row2['End_Date'])
})
return conflicts
def calculate_critical_path(self) -> List[str]:
"""Calculate critical path (simplified)"""
# Build dependency graph
graph = {}
for elem in self.elements['ElementId']:
graph[elem] = {
'predecessors': [],
'duration': 0
}
for dep in self.dependencies:
if dep['successor'] in graph:
graph[dep['successor']]['predecessors'].append(dep['predecessor'])
# Calculate durations
for _, row in self.elements.iterrows():
if row['ElementId'] in graph:
graph[row['ElementId']]['duration'] = (row['End_Date'] - row['Start_Date']).days
# Find longest path (simplified critical path)
def longest_path(node, memo={}):
if node in memo:
return memo[node]
if not graph[node]['predecessors']:
return graph[node]['duration']
max_pred = max(
longest_path(pred, memo) for pred in graph[node]['predecessors']
)
memo[node] = max_pred + graph[node]['duration']
return memo[node]
# Get all path lengths
path_lengths = {elem: longest_path(elem) for elem in graph.keys()}
# Critical path elements
max_length = max(path_lengths.values())
critical = [elem for elem, length in path_lengths.items() if length == max_length]
return critical
Export and Integration
def export_4d_schedule(elements: pd.DataFrame, output_path: str):
"""Export 4D schedule to Excel with multiple views"""
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Full schedule
elements.to_excel(writer, sheet_name='Schedule', index=False)
# By level
level_summary = elements.groupby('Level').agg({
'ElementId': 'count',
'Start_Date': 'min',
'End_Date': 'max'
}).rename(columns={'ElementId': 'Element_Count'})
level_summary['Duration_Days'] = (level_summary['End_Date'] - level_summary['Start_Date']).dt.days
level_summary.to_excel(writer, sheet_name='By_Level')
# By category
cat_summary = elements.groupby('Category').agg({
'ElementId': 'count',
'Start_Date': 'min',
'End_Date': 'max'
}).rename(columns={'ElementId': 'Element_Count'})
cat_summary.to_excel(writer, sheet_name='By_Category')
return output_path
Quick Reference
| Concept | Description |
|---|---|
| 4D = 3D + Time | BIM model linked to schedule |
| Activity | Scheduled work item |
| Element State | not_started / in_progress / completed |
| Critical Path | Longest sequence determining project duration |
Resources
- Book: "Data-Driven Construction" by Artem Boiko, Chapter 3.3
- Website: https://datadrivenconstruction.io
Next Steps
- See
gantt-chartfor schedule visualization - See
co2-estimationfor 6D (sustainability) analysis - See
clash-detection-analysisfor 4D conflict detection