Data Anomaly Detector for Construction
Overview
Detect unusual patterns, outliers, and anomalies in construction data. Identify cost overruns, schedule delays, productivity issues, and data quality problems before they impact projects.
Business Case
Construction data often contains anomalies that indicate:
Cost estimate errors or fraud
Schedule logic issues
Productivity problems
Data entry mistakes
Equipment or material issues
Early detection prevents costly corrections and project delays.
Technical Implementation
from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional, Tuple
from enum import Enum
import pandas as pd
import numpy as np
from datetime import datetime
from scipy import stats
class AnomalyType(Enum):
OUTLIER = "outlier"
PATTERN_BREAK = "pattern_break"
MISSING_SEQUENCE = "missing_sequence"
DUPLICATE = "duplicate"
IMPOSSIBLE_VALUE = "impossible_value"
TREND_DEVIATION = "trend_deviation"
class AnomalySeverity(Enum):
CRITICAL = "critical"
HIGH = "high"
MEDIUM = "medium"
LOW = "low"
@dataclass
class Anomaly:
id: str
anomaly_type: AnomalyType
severity: AnomalySeverity
field: str
value: Any
expected_range: Optional[Tuple[float, float]] = None
description: str = ""
row_index: Optional[int] = None
detection_method: str = ""
confidence: float = 0.0
suggested_action: str = ""
@dataclass
class AnomalyReport:
source: str
detected_at: datetime
total_records: int
anomalies: List[Anomaly]
summary: Dict[str, int]
class ConstructionAnomalyDetector:
"""Detect anomalies in construction data."""
# Construction-specific thresholds
COST_THRESHOLDS = {
'concrete_per_cy': (200, 800),
'steel_per_ton': (1500, 4000),
'labor_per_hour': (25, 150),
'overhead_percentage': (5, 25),
'contingency_percentage': (3, 20),
}
SCHEDULE_THRESHOLDS = {
'max_activity_duration': 365, # days
'max_lag': 30, # days
'min_productivity': 0.1,
'max_productivity': 10.0,
}
def __init__(self):
self.anomalies: List[Anomaly] = []
self.detection_history: List[AnomalyReport] = []
def detect_cost_anomalies(self, df: pd.DataFrame, cost_column: str,
group_by: str = None) -> List[Anomaly]:
"""Detect anomalies in cost data."""
anomalies = []
# Statistical outlier detection (IQR method)
Q1 = df[cost_column].quantile(0.25)
Q3 = df[cost_column].quantile(0.75)
IQR = Q3 - Q1
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR
outliers = df[(df[cost_column] < lower_bound) | (df[cost_column] > upper_bound)]
for idx, row in outliers.iterrows():
value = row[cost_column]
severity = AnomalySeverity.HIGH if abs(value - df[cost_column].median()) > 3 * IQR else AnomalySeverity.MEDIUM
anomalies.append(Anomaly(
id=f"COST-{idx}",
anomaly_type=AnomalyType.OUTLIER,
severity=severity,
field=cost_column,
value=value,
expected_range=(lower_bound, upper_bound),
description=f"Cost value {value:,.2f} outside expected range",
row_index=idx,
detection_method="IQR",
confidence=0.95,
suggested_action="Review cost estimate for errors"
))
# Negative cost check
negatives = df[df[cost_column] < 0]
for idx, row in negatives.iterrows():
anomalies.append(Anomaly(
id=f"COST-NEG-{idx}",
anomaly_type=AnomalyType.IMPOSSIBLE_VALUE,
severity=AnomalySeverity.CRITICAL,
field=cost_column,
value=row[cost_column],
expected_range=(0, None),
description="Negative cost value detected",
row_index=idx,
detection_method="Business Rule",
confidence=1.0,
suggested_action="Correct data entry error or investigate credit"
))
# Group-based anomalies (if grouped)
if group_by and group_by in df.columns:
group_stats = df.groupby(group_by)[cost_column].agg(['mean', 'std'])
for group_name, stats in group_stats.iterrows():
group_data = df[df[group_by] == group_name]
z_scores = np.abs((group_data[cost_column] - stats['mean']) / stats['std'])
for idx, z in z_scores.items():
if z > 3:
anomalies.append(Anomaly(
id=f"COST-GROUP-{idx}",
anomaly_type=AnomalyType.OUTLIER,
severity=AnomalySeverity.MEDIUM,
field=cost_column,
value=df.loc[idx, cost_column],
description=f"Unusual cost for group {group_name} (z-score: {z:.2f})",
row_index=idx,
detection_method="Z-Score by Group",
confidence=min(z / 5, 1.0)
))
return anomalies
def detect_schedule_anomalies(self, df: pd.DataFrame) -> List[Anomaly]:
"""Detect anomalies in schedule data."""
anomalies = []
# Check for required columns
required = ['start_date', 'end_date']
if not all(col in df.columns for col in required):
return anomalies
# Convert dates
df['start_date'] = pd.to_datetime(df['start_date'])
df['end_date'] = pd.to_datetime(df['end_date'])
# Calculate duration
df['duration'] = (df['end_date'] - df['start_date']).dt.days
# Negative duration (end before start)
negative_duration = df[df['duration'] < 0]
for idx, row in negative_duration.iterrows():
anomalies.append(Anomaly(
id=f"SCHED-NEG-{idx}",
anomaly_type=AnomalyType.IMPOSSIBLE_VALUE,
severity=AnomalySeverity.CRITICAL,
field="duration",
value=row['duration'],
description="End date before start date",
row_index=idx,
detection_method="Business Rule",
confidence=1.0,
suggested_action="Correct dates"
))
# Extremely long durations
long_tasks = df[df['duration'] > self.SCHEDULE_THRESHOLDS['max_activity_duration']]
for idx, row in long_tasks.iterrows():
anomalies.append(Anomaly(
id=f"SCHED-LONG-{idx}",
anomaly_type=AnomalyType.OUTLIER,
severity=AnomalySeverity.MEDIUM,
field="duration",
value=row['duration'],
expected_range=(0, self.SCHEDULE_THRESHOLDS['max_activity_duration']),
description=f"Task duration {row['duration']} days exceeds threshold",
row_index=idx,
detection_method="Threshold",
confidence=0.9,
suggested_action="Review if task should be broken down"
))
# Zero duration non-milestones
if 'is_milestone' in df.columns:
zero_duration = df[(df['duration'] == 0) & (~df['is_milestone'])]
for idx, row in zero_duration.iterrows():
anomalies.append(Anomaly(
id=f"SCHED-ZERO-{idx}",
anomaly_type=AnomalyType.IMPOSSIBLE_VALUE,
severity=AnomalySeverity.HIGH,
field="duration",
value=0,
description="Zero duration task that is not a milestone",
row_index=idx,
detection_method="Business Rule",
confidence=1.0,
suggested_action="Add duration or mark as milestone"
))
return anomalies
def detect_productivity_anomalies(self, df: pd.DataFrame,
quantity_col: str,
hours_col: str) -> List[Anomaly]:
"""Detect productivity anomalies."""
anomalies = []
# Calculate productivity
df['productivity'] = df[quantity_col] / df[hours_col].replace(0, np.nan)
# Use Modified Z-Score (more robust for skewed data)
median = df['productivity'].median()
mad = np.abs(df['productivity'] - median).median()
modified_z = 0.6745 * (df['productivity'] - median) / mad
outliers = df[np.abs(modified_z) > 3.5]
for idx, row in outliers.iterrows():
prod = row['productivity']
z = modified_z.loc[idx]
severity = AnomalySeverity.HIGH if abs(z) > 5 else AnomalySeverity.MEDIUM
direction = "high" if z > 0 else "low"
anomalies.append(Anomaly(
id=f"PROD-{idx}",
anomaly_type=AnomalyType.OUTLIER,
severity=severity,
field="productivity",
value=prod,
description=f"Unusually {direction} productivity: {prod:.2f} units/hour",
row_index=idx,
detection_method="Modified Z-Score",
confidence=min(abs(z) / 7, 1.0),
suggested_action=f"Investigate {direction} productivity cause"
))
return anomalies
def detect_time_series_anomalies(self, df: pd.DataFrame,
date_col: str,
value_col: str,
window: int = 7) -> List[Anomaly]:
"""Detect anomalies in time series data (e.g., daily costs, progress)."""
anomalies = []
df = df.sort_values(date_col).copy()
df['rolling_mean'] = df[value_col].rolling(window=window, center=True).mean()
df['rolling_std'] = df[value_col].rolling(window=window, center=True).std()
# Points outside 2 standard deviations from rolling mean
df['z_score'] = (df[value_col] - df['rolling_mean']) / df['rolling_std']
outliers = df[np.abs(df['z_score']) > 2].dropna()
for idx, row in outliers.iterrows():
anomalies.append(Anomaly(
id=f"TS-{idx}",
anomaly_type=AnomalyType.TREND_DEVIATION,
severity=AnomalySeverity.MEDIUM if abs(row['z_score']) < 3 else AnomalySeverity.HIGH,
field=value_col,
value=row[value_col],
expected_range=(
row['rolling_mean'] - 2 * row['rolling_std'],
row['rolling_mean'] + 2 * row['rolling_std']
),
description=f"Value deviates from {window}-day trend",
row_index=idx,
detection_method="Rolling Z-Score",
confidence=min(abs(row['z_score']) / 4, 1.0)
))
return anomalies
def detect_duplicate_anomalies(self, df: pd.DataFrame,
key_columns: List[str]) -> List[Anomaly]:
"""Detect duplicate records."""
anomalies = []
duplicates = df[df.duplicated(subset=key_columns, keep=False)]
if len(duplicates) > 0:
dup_groups = duplicates.groupby(key_columns).size()
for keys, count in dup_groups.items():
anomalies.append(Anomaly(
id=f"DUP-{hash(str(keys)) % 10000}",
anomaly_type=AnomalyType.DUPLICATE,
severity=AnomalySeverity.HIGH,
field=str(key_columns),
value=keys,
description=f"Found {count} duplicate records for {keys}",
detection_method="Exact Match",
confidence=1.0,
suggested_action="Review and remove duplicates"
))
return anomalies
def detect_sequence_gaps(self, df: pd.DataFrame, sequence_col: str) -> List[Anomaly]:
"""Detect gaps in sequential data (invoice numbers, PO numbers, etc.)."""
anomalies = []
# Extract numeric part if mixed format
df['seq_num'] = pd.to_numeric(
df[sequence_col].astype(str).str.extract(r'(\d+)')[0],
errors='coerce'
)
sorted_seq = df['seq_num'].dropna().sort_values()
expected = range(int(sorted_seq.min()), int(sorted_seq.max()) + 1)
actual = set(sorted_seq.astype(int))
missing = set(expected) - actual
if missing:
# Group consecutive missing numbers
missing_ranges = []
sorted_missing = sorted(missing)
start = sorted_missing[0]
end = start
for num in sorted_missing[1:]:
if num == end + 1:
end = num
else:
missing_ranges.append((start, end))
start = num
end = num
missing_ranges.append((start, end))
for start, end in missing_ranges:
range_str = str(start) if start == end else f"{start}-{end}"
anomalies.append(Anomaly(
id=f"SEQ-{start}",
anomaly_type=AnomalyType.MISSING_SEQUENCE,
severity=AnomalySeverity.MEDIUM,
field=sequence_col,
value=range_str,
description=f"Missing sequence number(s): {range_str}",
detection_method="Sequence Analysis",
confidence=1.0,
suggested_action="Investigate missing numbers"
))
return anomalies
def run_full_detection(self, df: pd.DataFrame, config: Dict) -> AnomalyReport:
"""Run all applicable anomaly detection methods."""
all_anomalies = []
# Cost anomalies
if 'cost_columns' in config:
for col in config['cost_columns']:
if col in df.columns:
all_anomalies.extend(
self.detect_cost_anomalies(df, col, config.get('group_by'))
)
# Schedule anomalies
if 'start_date' in df.columns and 'end_date' in df.columns:
all_anomalies.extend(self.detect_schedule_anomalies(df))
# Productivity
if 'quantity_col' in config and 'hours_col' in config:
all_anomalies.extend(
self.detect_productivity_anomalies(
df, config['quantity_col'], config['hours_col']
)
)
# Duplicates
if 'key_columns' in config:
all_anomalies.extend(
self.detect_duplicate_anomalies(df, config['key_columns'])
)
# Sequence gaps
if 'sequence_column' in config:
all_anomalies.extend(
self.detect_sequence_gaps(df, config['sequence_column'])
)
# Create summary
summary = {}
for a in all_anomalies:
key = f"{a.anomaly_type.value}_{a.severity.value}"
summary[key] = summary.get(key, 0) + 1
report = AnomalyReport(
source=config.get('source_name', 'Unknown'),
detected_at=datetime.now(),
total_records=len(df),
anomalies=all_anomalies,
summary=summary
)
self.detection_history.append(report)
return report
def generate_report(self, report: AnomalyReport) -> str:
"""Generate markdown anomaly report."""
lines = [f"# Anomaly Detection Report", ""]
lines.append(f"**Source:** {report.source}")
lines.append(f"**Detected At:** {report.detected_at.strftime('%Y-%m-%d %H:%M')}")
lines.append(f"**Total Records:** {report.total_records:,}")
lines.append(f"**Anomalies Found:** {len(report.anomalies)}")
lines.append("")
# Summary by severity
lines.append("## Summary by Severity")
for severity in AnomalySeverity:
count = sum(1 for a in report.anomalies if a.severity == severity)
if count > 0:
lines.append(f"- **{severity.value.upper()}:** {count}")
lines.append("")
# Critical anomalies first
critical = [a for a in report.anomalies if a.severity == AnomalySeverity.CRITICAL]
if critical:
lines.append("## Critical Anomalies")
for a in critical:
lines.append(f"\n### {a.id}")
lines.append(f"- **Type:** {a.anomaly_type.value}")
lines.append(f"- **Field:** {a.field}")
lines.append(f"- **Value:** {a.value}")
lines.append(f"- **Description:** {a.description}")
lines.append(f"- **Action:** {a.suggested_action}")
# All anomalies table
lines.append("\n## All Anomalies")
lines.append("| ID | Type | Severity | Field | Description |")
lines.append("|-----|------|----------|-------|-------------|")
for a in report.anomalies[:50]:
lines.append(f"| {a.id} | {a.anomaly_type.value} | {a.severity.value} | {a.field} | {a.description[:50]} |")
if len(report.anomalies) > 50:
lines.append(f"\n*... and {len(report.anomalies) - 50} more anomalies*")
return "\n".join(lines)
Quick Start
import pandas as pd
# Load data
df = pd.read_excel("project_costs.xlsx")
# Initialize detector
detector = ConstructionAnomalyDetector()
# Run detection
config = {
'source_name': 'Project Costs Q1 2026',
'cost_columns': ['total_cost', 'labor_cost', 'material_cost'],
'group_by': 'cost_code',
'key_columns': ['project_id', 'cost_code', 'date'],
'sequence_column': 'invoice_number'
}
report = detector.run_full_detection(df, config)
# Generate report
print(detector.generate_report(report))
# Get critical anomalies for immediate action
critical = [a for a in report.anomalies if a.severity == AnomalySeverity.CRITICAL]
print(f"\n{len(critical)} critical anomalies require immediate attention")
Dependencies
pip install pandas numpy scipy
Resources
Statistical Methods: IQR, Z-Score, Modified Z-Score
Construction Benchmarks: RSMeans, ENR indices