Create 4D construction simulations by linking BIM elements to project schedules. Generate time-based visualizations, sequence analysis, and construction phasing with Gantt integration.
日本語の概要は準備中です。原文の説明を表示しています。
Calculate construction labor rates with overhead, benefits, and productivity factors. Regional rate databases and crew composition.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Labor costs account for 30-50% of construction costs. This skill calculates all-in labor rates including wages, benefits, overhead, and regional adjustments.
import pandas as pd
from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from enum import Enum
class LaborCategory(Enum):
"""Labor skill categories."""
LABORER = "laborer"
CARPENTER = "carpenter"
ELECTRICIAN = "electrician"
PLUMBER = "plumber"
IRONWORKER = "ironworker"
MASON = "mason"
OPERATOR = "equipment_operator"
FOREMAN = "foreman"
SUPERINTENDENT = "superintendent"
class WorkType(Enum):
"""Work type for productivity."""
NEW_CONSTRUCTION = "new"
RENOVATION = "renovation"
DEMOLITION = "demolition"
MAINTENANCE = "maintenance"
@dataclass
class LaborRate:
"""Complete labor rate breakdown."""
category: str
base_wage: float
benefits: float
taxes: float
insurance: float
overhead: float
profit: float
total_rate: float
unit: str = "hour"
@dataclass
class CrewComposition:
"""Crew composition for work."""
name: str
workers: List[Dict[str, Any]]
total_hourly_cost: float
output_per_hour: float
unit: str
class LaborRateCalculator:
"""Calculate construction labor rates."""
# Default burden rates (percent of base wage)
DEFAULT_BURDENS = {
'benefits': 0.30, # Health, pension, vacation
'taxes': 0.10, # FICA, unemployment
'insurance': 0.08, # Workers comp, liability
'overhead': 0.15, # General conditions
'profit': 0.10 # Contractor profit
}
# Base wages by category (USD/hour, US average)
BASE_WAGES = {
LaborCategory.LABORER: 22,
LaborCategory.CARPENTER: 32,
LaborCategory.ELECTRICIAN: 38,
LaborCategory.PLUMBER: 36,
LaborCategory.IRONWORKER: 35,
LaborCategory.MASON: 34,
LaborCategory.OPERATOR: 40,
LaborCategory.FOREMAN: 45,
LaborCategory.SUPERINTENDENT: 55
}
# Regional factors
REGIONAL_FACTORS = {
'US_National': 1.00,
'New_York': 1.45,
'San_Francisco': 1.40,
'Chicago': 1.15,
'Houston': 0.95,
'Atlanta': 0.90,
'Germany_Berlin': 1.20,
'UK_London': 1.35
}
def __init__(self, burden_rates: Dict[str, float] = None):
self.burdens = burden_rates or self.DEFAULT_BURDENS
def calculate_rate(self, category: LaborCategory,
region: str = 'US_National',
custom_wage: float = None) -> LaborRate:
"""Calculate all-in labor rate."""
# Get base wage
base = custom_wage or self.BASE_WAGES.get(category, 25)
# Apply regional factor
regional_factor = self.REGIONAL_FACTORS.get(region, 1.0)
base *= regional_factor
# Calculate burden components
benefits = base * self.burdens['benefits']
taxes = base * self.burdens['taxes']
insurance = base * self.burdens['insurance']
# Subtotal before markup
subtotal = base + benefits + taxes + insurance
# Overhead and profit
overhead = subtotal * self.burdens['overhead']
profit = (subtotal + overhead) * self.burdens['profit']
total = subtotal + overhead + profit
return LaborRate(
category=category.value,
base_wage=round(base, 2),
benefits=round(benefits, 2),
taxes=round(taxes, 2),
insurance=round(insurance, 2),
overhead=round(overhead, 2),
profit=round(profit, 2),
total_rate=round(total, 2)
)
def calculate_crew_cost(self, composition: Dict[LaborCategory, int],
region: str = 'US_National') -> float:
"""Calculate hourly cost for crew composition."""
total = 0
for category, count in composition.items():
rate = self.calculate_rate(category, region)
total += rate.total_rate * count
return round(total, 2)
def get_rate_table(self, region: str = 'US_National') -> pd.DataFrame:
"""Generate rate table for all categories."""
rates = []
for category in LaborCategory:
rate = self.calculate_rate(category, region)
rates.append({
'category': rate.category,
'base_wage': rate.base_wage,
'benefits': rate.benefits,
'taxes': rate.taxes,
'insurance': rate.insurance,
'overhead': rate.overhead,
'profit': rate.profit,
'total_rate': rate.total_rate
})
return pd.DataFrame(rates)
class ProductivityFactor:
"""Calculate productivity factors for labor."""
# Base productivity factors
WORK_TYPE_FACTORS = {
WorkType.NEW_CONSTRUCTION: 1.0,
WorkType.RENOVATION: 0.75,
WorkType.DEMOLITION: 0.90,
WorkType.MAINTENANCE: 0.65
}
# Condition factors
CONDITION_FACTORS = {
'ideal': 1.0,
'normal': 0.90,
'difficult': 0.75,
'hazardous': 0.60,
'confined_space': 0.50
}
# Weather factors
WEATHER_FACTORS = {
'clear': 1.0,
'hot': 0.85,
'cold': 0.80,
'rain': 0.60,
'wind': 0.75
}
def calculate_factor(self, work_type: WorkType,
condition: str = 'normal',
weather: str = 'clear',
overtime_hours: int = 0) -> float:
"""Calculate combined productivity factor."""
base = self.WORK_TYPE_FACTORS.get(work_type, 1.0)
cond = self.CONDITION_FACTORS.get(condition, 0.9)
weath = self.WEATHER_FACTORS.get(weather, 1.0)
# Overtime degradation (productivity drops after 8 hours)
overtime_factor = 1.0
if overtime_hours > 0:
# Each OT hour is ~15% less productive
overtime_factor = 1 - (overtime_hours * 0.015)
combined = base * cond * weath * overtime_factor
return round(max(combined, 0.3), 2) # Minimum 30% productivity
def adjust_labor_hours(self, base_hours: float,
work_type: WorkType,
condition: str = 'normal',
weather: str = 'clear') -> float:
"""Adjust labor hours for conditions."""
factor = self.calculate_factor(work_type, condition, weather)
return round(base_hours / factor, 1)
class CrewBuilder:
"""Build and optimize crew compositions."""
# Standard crew compositions
STANDARD_CREWS = {
'concrete_pour': {
LaborCategory.FOREMAN: 1,
LaborCategory.CARPENTER: 2,
LaborCategory.LABORER: 4,
LaborCategory.OPERATOR: 1
},
'framing': {
LaborCategory.FOREMAN: 1,
LaborCategory.CARPENTER: 4,
LaborCategory.LABORER: 2
},
'electrical_rough': {
LaborCategory.FOREMAN: 1,
LaborCategory.ELECTRICIAN: 3,
LaborCategory.LABORER: 1
},
'plumbing_rough': {
LaborCategory.FOREMAN: 1,
LaborCategory.PLUMBER: 2,
LaborCategory.LABORER: 1
},
'masonry': {
LaborCategory.FOREMAN: 1,
LaborCategory.MASON: 4,
LaborCategory.LABORER: 4
}
}
def __init__(self, rate_calculator: LaborRateCalculator):
self.calc = rate_calculator
def get_crew(self, work_type: str,
region: str = 'US_National') -> CrewComposition:
"""Get standard crew composition with costs."""
if work_type not in self.STANDARD_CREWS:
raise ValueError(f"Unknown work type: {work_type}")
composition = self.STANDARD_CREWS[work_type]
total_cost = self.calc.calculate_crew_cost(composition, region)
workers = []
for category, count in composition.items():
rate = self.calc.calculate_rate(category, region)
workers.append({
'category': category.value,
'count': count,
'hourly_rate': rate.total_rate,
'subtotal': rate.total_rate * count
})
return CrewComposition(
name=work_type,
workers=workers,
total_hourly_cost=total_cost,
output_per_hour=1.0, # Placeholder
unit='hour'
)
def custom_crew(self, workers: Dict[LaborCategory, int],
region: str = 'US_National') -> CrewComposition:
"""Build custom crew composition."""
total_cost = self.calc.calculate_crew_cost(workers, region)
worker_list = []
for category, count in workers.items():
rate = self.calc.calculate_rate(category, region)
worker_list.append({
'category': category.value,
'count': count,
'hourly_rate': rate.total_rate,
'subtotal': rate.total_rate * count
})
return CrewComposition(
name='custom',
workers=worker_list,
total_hourly_cost=total_cost,
output_per_hour=1.0,
unit='hour'
)
calc = LaborRateCalculator()
# Get single rate
rate = calc.calculate_rate(LaborCategory.CARPENTER, region='New_York')
print(f"Carpenter rate NYC: ${rate.total_rate}/hr")
# Rate table
rates = calc.get_rate_table('US_National')
print(rates)
builder = CrewBuilder(calc)
concrete_crew = builder.get_crew('concrete_pour', 'Chicago')
print(f"Crew cost: ${concrete_crew.total_hourly_cost}/hr")
productivity = ProductivityFactor()
factor = productivity.calculate_factor(
WorkType.RENOVATION,
condition='difficult',
weather='hot'
)
adjusted_hours = productivity.adjust_labor_hours(100, WorkType.RENOVATION)
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概要と使いどころ
Create 4D construction simulations by linking BIM elements to project schedules. Generate time-based visualizations, sequence analysis, and construction phasing with Gantt integration.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze 5000+ IFC and Revit projects at scale for patterns, benchmarks, and insights. Big data analysis for construction.
日本語の概要は準備中です。原文の説明を表示しています。
Check construction AI systems against the EU AI Act (Regulation 2024/1689): classify risk of estimation, scheduling, CV and agent tools, document transparency, keep human oversight. Use when deploying or auditing AI in construction.
日本語の概要は準備中です。原文の説明を表示しています。
Orchestrate multiple AI agents for construction workflows: estimator, scheduler, document, QA and safety agents coordinated by a supervisor agent, with human checkpoints. Use when automating end-to-end project processes with agentic AI.
日本語の概要は準備中です。原文の説明を表示しています。
Create Apache Airflow DAGs for construction data pipelines. Orchestrate ETL, validation, and reporting workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Automate as-built documentation and digital handover for construction. Compile project records, generate O&M manuals, create asset databases, and ensure complete project closeout.
日本語の概要は準備中です。原文の説明を表示しています。