tomevault-io Predictive Analytics for Construction
Overview
Use historical project data to predict future outcomes: cost overruns, schedule delays, quality issues, and risks. Apply machine learning models tailored for construction industry patterns.
Business Case
Predictive analytics enables proactive project management:
- Early Warning: Identify projects likely to overrun before it happens
- Resource Optimization: Allocate resources based on predicted needs
- Risk Mitigation: Focus on high-risk areas early
- Better Estimates: Learn from historical accuracy
Technical Implementation
from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional, Tuple
import pandas as pd
import numpy as np
from datetime import datetime
from sklearn.ensemble import RandomForestRegressor, GradientBoostingClassifier
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.metrics import mean_absolute_error, accuracy_score, classification_report
import warnings
warnings.filterwarnings('ignore')
@dataclass
class PredictionResult:
prediction: float
confidence: float
prediction_type: str
features_used: List[str]
feature_importance: Dict[str, float]
comparable_projects: List[str]
risk_factors: List[str]
@dataclass
class ModelMetrics:
model_name: str
accuracy: float
mae: float
feature_importance: Dict[str, float]
training_samples: int
last_trained: datetime
class ConstructionPredictiveAnalytics:
"""Predictive analytics for construction projects."""
def __init__(self):
self.models: Dict[str, Any] = {}
self.scalers: Dict[str, StandardScaler] = {}
self.encoders: Dict[str, LabelEncoder] = {}
self.metrics: Dict[str, ModelMetrics] = {}
self.feature_columns: Dict[str, List[str]] = {}
def prepare_features(self, df: pd.DataFrame, target_col: str) -> Tuple[pd.DataFrame, pd.Series]:
"""Prepare features for model training."""
# Separate numeric and categorical columns
numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()
categorical_cols = df.select_dtypes(include=['object', 'category']).columns.tolist()
# Remove target from features
if target_col in numeric_cols:
numeric_cols.remove(target_col)
if target_col in categorical_cols:
categorical_cols.remove(target_col)
# Encode categorical variables
df_encoded = df.copy()
for col in categorical_cols:
if col not in self.encoders:
self.encoders[col] = LabelEncoder()
df_encoded[col] = self.encoders[col].fit_transform(df[col].astype(str))
else:
df_encoded[col] = self.encoders[col].transform(df[col].astype(str))
feature_cols = numeric_cols + categorical_cols
X = df_encoded[feature_cols].fillna(0)
y = df[target_col]
return X, y, feature_cols
def train_cost_overrun_model(self, historical_data: pd.DataFrame) -> ModelMetrics:
"""Train model to predict cost overrun percentage."""
# Expected columns: project_type, original_estimate, gross_area, duration_months,
# num_change_orders, complexity_score, contractor_experience, final_cost
required_cols = ['original_estimate', 'final_cost']
if not all(col in historical_data.columns for col in required_cols):
raise ValueError(f"Missing required columns: {required_cols}")
# Calculate overrun percentage
df = historical_data.copy()
df['overrun_pct'] = ((df['final_cost'] - df['original_estimate']) / df['original_estimate']) * 100
# Prepare features
feature_cols = [col for col in df.columns if col not in ['final_cost', 'overrun_pct', 'project_id', 'project_name']]
X, y, used_features = self.prepare_features(df[feature_cols + ['overrun_pct']], 'overrun_pct')
# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Scale features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
# Train model
model = GradientBoostingRegressor(n_estimators=100, max_depth=5, random_state=42)
model.fit(X_train_scaled, y_train)
# Evaluate
y_pred = model.predict(X_test_scaled)
mae = mean_absolute_error(y_test, y_pred)
# Cross-validation
cv_scores = cross_val_score(model, X_train_scaled, y_train, cv=5, scoring='neg_mean_absolute_error')
# Feature importance
importance = dict(zip(used_features, model.feature_importances_))
# Store model
self.models['cost_overrun'] = model
self.scalers['cost_overrun'] = scaler
self.feature_columns['cost_overrun'] = used_features
metrics = ModelMetrics(
model_name='cost_overrun',
accuracy=1 - (mae / df['overrun_pct'].std()),
mae=mae,
feature_importance=importance,
training_samples=len(X_train),
last_trained=datetime.now()
)
self.metrics['cost_overrun'] = metrics
return metrics
def train_schedule_delay_model(self, historical_data: pd.DataFrame) -> ModelMetrics:
"""Train model to predict schedule delay probability."""
df = historical_data.copy()
# Binary classification: was project delayed?
df['was_delayed'] = (df['actual_duration'] > df['planned_duration']).astype(int)
feature_cols = [col for col in df.columns
if col not in ['actual_duration', 'was_delayed', 'project_id', 'project_name']]
X, y, used_features = self.prepare_features(df[feature_cols + ['was_delayed']], 'was_delayed')
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
model = GradientBoostingClassifier(n_estimators=100, max_depth=5, random_state=42)
model.fit(X_train_scaled, y_train)
y_pred = model.predict(X_test_scaled)
accuracy = accuracy_score(y_test, y_pred)
importance = dict(zip(used_features, model.feature_importances_))
self.models['schedule_delay'] = model
self.scalers['schedule_delay'] = scaler
self.feature_columns['schedule_delay'] = used_features
metrics = ModelMetrics(
model_name='schedule_delay',
accuracy=accuracy,
mae=0,
feature_importance=importance,
training_samples=len(X_train),
last_trained=datetime.now()
)
self.metrics['schedule_delay'] = metrics
return metrics
def predict_cost_overrun(self, project_data: Dict) -> PredictionResult:
"""Predict cost overrun for a new project."""
if 'cost_overrun' not in self.models:
raise ValueError("Cost overrun model not trained. Call train_cost_overrun_model first.")
model = self.models['cost_overrun']
scaler = self.scalers['cost_overrun']
features = self.feature_columns['cost_overrun']
# Prepare input
input_df = pd.DataFrame([project_data])
# Encode categorical
for col in input_df.select_dtypes(include=['object']).columns:
if col in self.encoders:
input_df[col] = self.encoders[col].transform(input_df[col].astype(str))
# Ensure all features present
for feat in features:
if feat not in input_df.columns:
input_df[feat] = 0
X = input_df[features].fillna(0)
X_scaled = scaler.transform(X)
prediction = model.predict(X_scaled)[0]
# Get feature importance for this prediction
importance = dict(zip(features, model.feature_importances_))
top_features = sorted(importance.items(), key=lambda x: -x[1])[:5]
# Identify risk factors
risk_factors = []
if prediction > 10:
risk_factors.append(f"High overrun risk: {prediction:.1f}%")
for feat, imp in top_features[:3]:
risk_factors.append(f"Key factor: {feat} (importance: {imp:.2%})")
return PredictionResult(
prediction=prediction,
confidence=0.8, # Could calculate from model uncertainty
prediction_type='cost_overrun_percentage',
features_used=features,
feature_importance=dict(top_features),
comparable_projects=[],
risk_factors=risk_factors
)
def predict_delay_probability(self, project_data: Dict) -> PredictionResult:
"""Predict probability of schedule delay."""
if 'schedule_delay' not in self.models:
raise ValueError("Schedule delay model not trained.")
model = self.models['schedule_delay']
scaler = self.scalers['schedule_delay']
features = self.feature_columns['schedule_delay']
input_df = pd.DataFrame([project_data])
for col in input_df.select_dtypes(include=['object']).columns:
if col in self.encoders:
input_df[col] = self.encoders[col].transform(input_df[col].astype(str))
for feat in features:
if feat not in input_df.columns:
input_df[feat] = 0
X = input_df[features].fillna(0)
X_scaled = scaler.transform(X)
probability = model.predict_proba(X_scaled)[0][1]
prediction = model.predict(X_scaled)[0]
importance = dict(zip(features, model.feature_importances_))
top_features = sorted(importance.items(), key=lambda x: -x[1])[:5]
risk_factors = []
if probability > 0.7:
risk_factors.append(f"High delay probability: {probability:.1%}")
elif probability > 0.4:
risk_factors.append(f"Moderate delay probability: {probability:.1%}")
return PredictionResult(
prediction=probability,
confidence=probability if prediction == 1 else 1 - probability,
prediction_type='delay_probability',
features_used=features,
feature_importance=dict(top_features),
comparable_projects=[],
risk_factors=risk_factors
)
def find_similar_projects(self, project_data: Dict, historical_data: pd.DataFrame,
n: int = 5) -> pd.DataFrame:
"""Find similar projects from historical data."""
from sklearn.neighbors import NearestNeighbors
numeric_cols = historical_data.select_dtypes(include=[np.number]).columns.tolist()
exclude = ['final_cost', 'actual_duration', 'overrun_pct']
feature_cols = [c for c in numeric_cols if c not in exclude]
X = historical_data[feature_cols].fillna(0)
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Prepare new project
new_project = pd.DataFrame([project_data])[feature_cols].fillna(0)
new_scaled = scaler.transform(new_project)
# Find neighbors
nn = NearestNeighbors(n_neighbors=min(n, len(X)), metric='euclidean')
nn.fit(X_scaled)
distances, indices = nn.kneighbors(new_scaled)
similar = historical_data.iloc[indices[0]].copy()
similar['similarity_score'] = 1 / (1 + distances[0])
return similar
def generate_prediction_report(self, project_data: Dict, historical_data: pd.DataFrame) -> str:
"""Generate comprehensive prediction report."""
lines = ["# Project Prediction Report", ""]
lines.append(f"**Generated:** {datetime.now().strftime('%Y-%m-%d %H:%M')}")
lines.append(f"**Project:** {project_data.get('project_name', 'New Project')}")
lines.append("")
# Cost prediction
if 'cost_overrun' in self.models:
cost_pred = self.predict_cost_overrun(project_data)
lines.append("## Cost Overrun Prediction")
lines.append(f"**Predicted Overrun:** {cost_pred.prediction:.1f}%")
lines.append(f"**Confidence:** {cost_pred.confidence:.1%}")
lines.append("")
lines.append("**Key Factors:**")
for feat, imp in list(cost_pred.feature_importance.items())[:5]:
lines.append(f"- {feat}: {imp:.2%}")
lines.append("")
# Schedule prediction
if 'schedule_delay' in self.models:
delay_pred = self.predict_delay_probability(project_data)
lines.append("## Schedule Delay Prediction")
lines.append(f"**Delay Probability:** {delay_pred.prediction:.1%}")
lines.append("")
# Similar projects
lines.append("## Similar Historical Projects")
similar = self.find_similar_projects(project_data, historical_data, n=5)
for _, row in similar.iterrows():
name = row.get('project_name', 'Project')
overrun = row.get('overrun_pct', 0)
similarity = row.get('similarity_score', 0)
lines.append(f"- **{name}**: {overrun:.1f}% overrun (similarity: {similarity:.1%})")
# Risk summary
lines.append("")
lines.append("## Risk Summary")
all_risks = []
if 'cost_overrun' in self.models:
all_risks.extend(cost_pred.risk_factors)
if 'schedule_delay' in self.models:
all_risks.extend(delay_pred.risk_factors)
for risk in all_risks:
lines.append(f"- ⚠️ {risk}")
return "\n".join(lines)
Quick Start
import pandas as pd
# Load historical data
historical = pd.read_excel("historical_projects.xlsx")
# Initialize analytics
analytics = ConstructionPredictiveAnalytics()
# Train models
cost_metrics = analytics.train_cost_overrun_model(historical)
print(f"Cost model MAE: {cost_metrics.mae:.2f}%")
delay_metrics = analytics.train_schedule_delay_model(historical)
print(f"Delay model accuracy: {delay_metrics.accuracy:.1%}")
# Predict for new project
new_project = {
'project_type': 'Office',
'original_estimate': 5000000,
'gross_area': 50000,
'duration_months': 18,
'complexity_score': 7,
'contractor_experience': 15
}
cost_prediction = analytics.predict_cost_overrun(new_project)
print(f"Predicted overrun: {cost_prediction.prediction:.1f}%")
delay_prediction = analytics.predict_delay_probability(new_project)
print(f"Delay probability: {delay_prediction.prediction:.1%}")
# Generate report
report = analytics.generate_prediction_report(new_project, historical)
print(report)
Dependencies
pip install pandas numpy scikit-learn
Resources
- ML for Construction: Research on predictive models
- Feature Engineering: Construction-specific features
Source: datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction — distributed by TomeVault.