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Process LiDAR scans of tunnels and bridges, calculate structural displacement in millimetres, generate safety heatmaps, and schedule maintenance — all in one workflow call.

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Quick Start

1

Run the prebuilt monitoring workflow

from praisonaiagents import Agent, tool
from examples.cookbooks.Industry_Templates.transportation_template import (
    infrastructure_monitoring_workflow,
    InfrastructureType
)

tunnels = ["TUN-001", "TUN-002", "TUN-003"]
traffic = {"peak_volume": 3000, "off_peak_volume": 800, "peak_hours": [7, 9, 17, 19]}

result = infrastructure_monitoring_workflow(tunnels, InfrastructureType.TUNNEL, traffic)

print(result["safety_assessments"])
print(result["emergency_alerts"])
print(f"Network health score: {result['network_health_score']:.1f}/100")
2

Adapt for bridges, railways, or airports

from examples.cookbooks.Industry_Templates.transportation_template import MultiModalTransportPatterns

bridge_monitor  = MultiModalTransportPatterns.adapt_for_bridge_monitoring()
railway_monitor = MultiModalTransportPatterns.adapt_for_railway_tracks()
runway_monitor  = MultiModalTransportPatterns.adapt_for_airport_runways()

result = bridge_monitor.start("Check deck deflection and cable tension for BRG-007")
print(result)

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How It Works

AgentResponsibilitySLA
LiDARFusionProcess 5M-point scans from mobile and terrestrial LiDAR≤ 5 min
DisplacementCalculatorCompare current vs. baseline scan; calculate mm-level settlement≤ 2 min
HeatmapGeneratorGenerate 2D safety grid highlighting critical zones≤ 1 min
MaintenancePlannerPlan preventive, corrective, or emergency maintenance≤ 3 min

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Configuration Options

Pydantic I/O schemas used by this template:
SchemaKey Fields
LiDARScanscan_id, infrastructure_id, point_cloud_size, scan_resolution, coverage_area, anomaly_points, reference_baseline
DisplacementAnalysisanalysis_id, infrastructure_id, max_displacement, displacement_vector, critical_zones, settlement_rate, tilt_angle, safety_factor
SafetyHeatmapheatmap_id, infrastructure_id, grid_resolution, safety_scores, critical_areas, overall_safety, risk_trends
MaintenanceScheduleschedule_id, infrastructure_id, maintenance_type, priority_level, scheduled_date, estimated_duration, traffic_impact, cost_estimate
Safety levels (used in SafetyHeatmap.overall_safety)
Levelsafety_factor rangeAction
safe≥ 0.9Routine monitoring
caution0.8 – 0.9Preventive maintenance within 30 days
warning0.7 – 0.8Corrective maintenance within 7 days
danger0.3 – 0.7Emergency maintenance within 24 h
critical< 0.3Immediate closure

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Common Patterns

Predict structural failure probability from displacement history 
from examples.cookbooks.Industry_Templates.transportation_template import PredictiveMaintenancePatterns

displacement_history = [2.0, 2.3, 2.8, 3.5, 4.3]  # mm over past 5 months
environment = {"temp_variation": 30, "humidity": 75}

prob = PredictiveMaintenancePatterns.predict_failure_probability(
    displacement_history, environment
)
print(f"Failure probability: {prob:.2%}")
Optimise maintenance budget across a network 
from examples.cookbooks.Industry_Templates.transportation_template import PredictiveMaintenancePatterns

conditions = [
    {"id": "TUN-001", "safety_factor": 0.65, "cost": 80000, "recommended_date": "2024-04-01"},
    {"id": "TUN-002", "safety_factor": 0.92, "cost": 30000, "recommended_date": "2024-06-01"},
]

schedule = PredictiveMaintenancePatterns.optimize_maintenance_schedule(
    infrastructure_conditions=conditions,
    budget_constraint=100000.0
)
print(schedule)
Plan traffic flow during maintenance 
from examples.cookbooks.Industry_Templates.transportation_template import TrafficManagementPatterns

traffic_plan = TrafficManagementPatterns.optimize_traffic_flow(
    maintenance_window={"start_time": "22:00", "duration": 8},
    traffic_patterns={"peak_hours": [7, 9, 17, 19]}
)
print(traffic_plan["diversion_routes"])

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Best Practices

DisplacementCalculator computes change relative to a reference_baseline. Capture this baseline during construction or last major rehabilitation, and store it in a versioned database alongside each new scan.
The workflow automatically raises an emergency_alert with immediate_action: close_to_traffic for danger/critical safety levels. Wire this alert to your traffic management system to trigger lane closures without human delay.
Scan accuracy degrades when vehicles pass through the scan envelope. Use TrafficManagementPatterns.optimize_traffic_flow() to identify overnight windows with minimal traffic before dispatching the LiDAR crew.
The network_health_score (0–100) aggregates safety factors across all monitored structures. Report this monthly to asset managers — a score below 70 should trigger a portfolio-level maintenance budget review.

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