Automatically generate estimates from QTO data. Apply pricing rules to BIM quantities for cost estimates.
中文简介正在准备中。当前显示原文说明。
安装前可以查看交给 AI 代理的指令内容。
Manual estimate creation challenges:
Automated estimate generation from BIM/QTO data using configurable pricing rules and assembly mappings.
import pandas as pd
from typing import Dict, Any, List, Optional, Callable
from dataclasses import dataclass, field
from enum import Enum
class ElementType(Enum):
WALL = "wall"
FLOOR = "floor"
CEILING = "ceiling"
DOOR = "door"
WINDOW = "window"
COLUMN = "column"
BEAM = "beam"
FOUNDATION = "foundation"
ROOF = "roof"
STAIR = "stair"
MEP = "mep"
@dataclass
class QTOItem:
element_id: str
element_type: ElementType
name: str
quantity: float
unit: str
properties: Dict[str, Any] = field(default_factory=dict)
@dataclass
class PricingRule:
rule_id: str
name: str
element_type: ElementType
conditions: Dict[str, Any] = field(default_factory=dict)
unit_cost: float = 0
assembly_code: str = ""
cost_breakdown: Dict[str, float] = field(default_factory=dict)
@dataclass
class EstimateItem:
qto_element_id: str
description: str
quantity: float
unit: str
unit_cost: float
total_cost: float
rule_applied: str
wbs_code: str = ""
class AutoEstimateGenerator:
"""Generate estimates from QTO data automatically."""
def __init__(self, project_name: str):
self.project_name = project_name
self.pricing_rules: List[PricingRule] = []
self.qto_items: List[QTOItem] = []
self.estimate_items: List[EstimateItem] = []
self.unmapped_items: List[QTOItem] = []
def add_pricing_rule(self, rule: PricingRule):
"""Add pricing rule."""
self.pricing_rules.append(rule)
def load_pricing_rules_from_df(self, df: pd.DataFrame):
"""Load pricing rules from DataFrame."""
for _, row in df.iterrows():
conditions = {}
if 'material' in row:
conditions['material'] = row['material']
if 'thickness_min' in row:
conditions['thickness_min'] = row['thickness_min']
if 'thickness_max' in row:
conditions['thickness_max'] = row['thickness_max']
rule = PricingRule(
rule_id=row['rule_id'],
name=row['name'],
element_type=ElementType(row['element_type'].lower()),
conditions=conditions,
unit_cost=float(row['unit_cost']),
assembly_code=row.get('assembly_code', ''),
cost_breakdown={
'labor': float(row.get('labor_pct', 0.4)),
'material': float(row.get('material_pct', 0.5)),
'equipment': float(row.get('equipment_pct', 0.1))
}
)
self.add_pricing_rule(rule)
def load_qto_from_df(self, df: pd.DataFrame):
"""Load QTO items from DataFrame."""
for _, row in df.iterrows():
properties = {}
for col in df.columns:
if col not in ['element_id', 'element_type', 'name', 'quantity', 'unit']:
properties[col] = row[col]
qto = QTOItem(
element_id=str(row['element_id']),
element_type=ElementType(row['element_type'].lower()),
name=row['name'],
quantity=float(row['quantity']),
unit=row['unit'],
properties=properties
)
self.qto_items.append(qto)
def find_matching_rule(self, qto_item: QTOItem) -> Optional[PricingRule]:
"""Find pricing rule that matches QTO item."""
matching_rules = []
for rule in self.pricing_rules:
if rule.element_type != qto_item.element_type:
continue
# Check conditions
match = True
for key, value in rule.conditions.items():
if key.endswith('_min'):
prop_name = key[:-4]
if prop_name in qto_item.properties:
if qto_item.properties[prop_name] < value:
match = False
elif key.endswith('_max'):
prop_name = key[:-4]
if prop_name in qto_item.properties:
if qto_item.properties[prop_name] > value:
match = False
else:
if key in qto_item.properties:
if qto_item.properties[key] != value:
match = False
if match:
matching_rules.append(rule)
# Return most specific rule (most conditions)
if matching_rules:
return max(matching_rules, key=lambda r: len(r.conditions))
return None
def generate_estimate(self) -> Dict[str, Any]:
"""Generate estimate from QTO items."""
self.estimate_items = []
self.unmapped_items = []
total_cost = 0
for qto in self.qto_items:
rule = self.find_matching_rule(qto)
if rule:
item_cost = qto.quantity * rule.unit_cost
self.estimate_items.append(EstimateItem(
qto_element_id=qto.element_id,
description=f"{qto.name} ({rule.name})",
quantity=qto.quantity,
unit=qto.unit,
unit_cost=rule.unit_cost,
total_cost=round(item_cost, 2),
rule_applied=rule.rule_id,
wbs_code=rule.assembly_code
))
total_cost += item_cost
else:
self.unmapped_items.append(qto)
return {
'project': self.project_name,
'total_qto_items': len(self.qto_items),
'mapped_items': len(self.estimate_items),
'unmapped_items': len(self.unmapped_items),
'mapping_rate': round(len(self.estimate_items) / len(self.qto_items) * 100, 1) if self.qto_items else 0,
'total_cost': round(total_cost, 2),
'items': self.estimate_items
}
def get_cost_by_element_type(self) -> Dict[str, float]:
"""Get cost breakdown by element type."""
by_type = {}
for qto in self.qto_items:
for est_item in self.estimate_items:
if est_item.qto_element_id == qto.element_id:
type_name = qto.element_type.value
by_type[type_name] = by_type.get(type_name, 0) + est_item.total_cost
return {k: round(v, 2) for k, v in by_type.items()}
def get_unmapped_summary(self) -> pd.DataFrame:
"""Get summary of unmapped items."""
if not self.unmapped_items:
return pd.DataFrame()
data = []
for item in self.unmapped_items:
data.append({
'Element ID': item.element_id,
'Type': item.element_type.value,
'Name': item.name,
'Quantity': item.quantity,
'Unit': item.unit,
'Properties': str(item.properties)
})
return pd.DataFrame(data)
def export_to_excel(self, output_path: str) -> str:
"""Export estimate to Excel."""
result = self.generate_estimate()
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Summary
summary_df = pd.DataFrame([{
'Project': self.project_name,
'Total QTO Items': result['total_qto_items'],
'Mapped Items': result['mapped_items'],
'Unmapped Items': result['unmapped_items'],
'Mapping Rate %': result['mapping_rate'],
'Total Cost': result['total_cost']
}])
summary_df.to_excel(writer, sheet_name='Summary', index=False)
# Estimate items
items_df = pd.DataFrame([{
'Element ID': item.qto_element_id,
'Description': item.description,
'Quantity': item.quantity,
'Unit': item.unit,
'Unit Cost': item.unit_cost,
'Total Cost': item.total_cost,
'WBS': item.wbs_code,
'Rule': item.rule_applied
} for item in self.estimate_items])
items_df.to_excel(writer, sheet_name='Estimate', index=False)
# By element type
by_type_df = pd.DataFrame([
{'Element Type': k, 'Cost': v}
for k, v in self.get_cost_by_element_type().items()
])
by_type_df.to_excel(writer, sheet_name='By Type', index=False)
# Unmapped items
unmapped_df = self.get_unmapped_summary()
if not unmapped_df.empty:
unmapped_df.to_excel(writer, sheet_name='Unmapped', index=False)
return output_path
def suggest_missing_rules(self) -> List[Dict[str, Any]]:
"""Suggest pricing rules for unmapped items."""
suggestions = []
seen_types = set()
for item in self.unmapped_items:
key = (item.element_type.value, str(item.properties))
if key not in seen_types:
seen_types.add(key)
suggestions.append({
'element_type': item.element_type.value,
'sample_name': item.name,
'properties': item.properties,
'count': sum(1 for i in self.unmapped_items
if i.element_type == item.element_type
and str(i.properties) == str(item.properties))
})
return sorted(suggestions, key=lambda x: x['count'], reverse=True)
# Initialize generator
generator = AutoEstimateGenerator("Office Building A")
# Add pricing rules
generator.add_pricing_rule(PricingRule(
rule_id="W-001",
name="Interior Wall - Drywall",
element_type=ElementType.WALL,
conditions={"material": "Drywall"},
unit_cost=45.00,
assembly_code="09.29.10"
))
generator.add_pricing_rule(PricingRule(
rule_id="W-002",
name="Exterior Wall - Masonry",
element_type=ElementType.WALL,
conditions={"material": "Masonry"},
unit_cost=125.00,
assembly_code="04.21.13"
))
# Load QTO data
generator.qto_items = [
QTOItem("W-001", ElementType.WALL, "Interior Wall L1", 500, "SF", {"material": "Drywall"}),
QTOItem("W-002", ElementType.WALL, "Exterior Wall", 1200, "SF", {"material": "Masonry"})
]
# Generate estimate
result = generator.generate_estimate()
print(f"Total Cost: ${result['total_cost']:,.2f}")
print(f"Mapping Rate: {result['mapping_rate']}%")
by_type = generator.get_cost_by_element_type()
for element_type, cost in by_type.items():
print(f"{element_type}: ${cost:,.2f}")
unmapped = generator.get_unmapped_summary()
print(unmapped)
suggestions = generator.suggest_missing_rules()
for s in suggestions:
print(f"Need rule for: {s['element_type']} ({s['count']} items)")
还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问，或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
Create 4D construction simulations by linking BIM elements to project schedules. Generate time-based visualizations, sequence analysis, and construction phasing with Gantt integration.
中文简介正在准备中。当前显示原文说明。
Analyze 5000+ IFC and Revit projects at scale for patterns, benchmarks, and insights. Big data analysis for construction.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
Create Apache Airflow DAGs for construction data pipelines. Orchestrate ETL, validation, and reporting workflows.
中文简介正在准备中。当前显示原文说明。
Automate as-built documentation and digital handover for construction. Compile project records, generate O&M manuals, create asset databases, and ensure complete project closeout.
中文简介正在准备中。当前显示原文说明。