Natural and man-made disasters such as earthquakes, floods, hurricanes, tsunamis, and industrial accidents pose severe threats to built infrastructure. After such catastrophic events, rapid and accurate inspection of infrastructure like bridges, roads, buildings, pipelines, and dams is critical to ensure public safety, assess damage, and prioritize repair. Traditionally, these inspections are manual, time-consuming, hazardous, and dependent on human availability and skill. To overcome these challenges, automated infrastructure inspection using robotics and automation technologies has emerged as a transformative solution in civil engineering. This chapter explores the application of autonomous systems such as UAVs (Unmanned Aerial Vehicles), ground robots, sensor networks, and AI-driven software tools for efficient post-disaster inspection and assessment.

• Safety Assessment: Determining the structural integrity of critical infrastructure.
• Rescue and Relief Support: Identifying blocked routes, collapsed structures, or access points.
• Damage Documentation: Creating digital records for insurance, government reporting, and repairs.
• Time Sensitivity: Immediate inspection is required to avoid secondary collapses or hazards.
• Risk Mitigation: Reduces the exposure of human inspectors to dangerous environments.

• Accessibility Issues: Difficult or impossible to inspect certain areas manually due to debris, height, or instability.
• Time and Labor Intensive: Delays in assessing large-scale damage.
• Subjective Judgements: Relies on inspector's experience; may lead to inconsistencies.
• Data Collection Limitations: Limited ability to capture comprehensive quantitative data.

• Rapid Deployment: Drones and mobile robots can be quickly deployed to disaster zones.
• Real-Time Data: Enables live streaming of video, thermal imaging, and sensor data.
• Autonomous Navigation: Capable of path planning and obstacle avoidance without human control.
• 3D Mapping and Modeling: Using LIDAR and photogrammetry for reconstructing damaged structures.
• Integration with AI: For damage classification, anomaly detection, and predictive modeling.

29.4.1 Unmanned Aerial Vehicles (UAVs)
• Equipped with HD cameras, thermal sensors, and LIDAR.
• Used for aerial mapping, roof inspections, and monitoring inaccessible areas.
• Capable of covering large regions quickly.
• Example Applications: Inspecting collapsed buildings, monitoring flooded zones.

29.4.2 Ground Robots (UGVs)
• Tracked or wheeled robots designed for rough terrain.
• Useful in confined or collapsed spaces.
• Can carry instruments like moisture sensors, structural probes, and ground-penetrating radar.

29.4.3 Climbing Robots
• Used for inspecting vertical structures like bridge piers and tall buildings.
• Employ magnetic wheels, vacuum adhesion, or bio-inspired mechanisms for wall climbing.

29.4.4 Amphibious and Marine Robots
• Used to inspect submerged infrastructure such as piers, dams, and pipelines.
• Equipped with sonar and underwater cameras.

29.5.1 Sensors and Imaging Systems
• LIDAR (Light Detection and Ranging): Captures 3D structure data.
• Infrared Cameras: Detects heat signatures indicating hidden damage or water ingress.
• Ultrasonic Sensors: Measures wall thickness and detects internal flaws.
• Accelerometers: Monitor vibrations and structural responses.

29.5.2 Artificial Intelligence and Machine Learning
• Image Recognition: Automatically detects cracks, corrosion, or deformations in images.
• Change Detection: Compares pre- and post-disaster images for damage estimation.
• Predictive Analytics: Estimates future degradation based on current data.

29.5.3 Data Communication and Cloud Platforms
• Edge Computing: Processes data onboard the robot in real-time.
• Cloud Uploading: Sends data to centralized systems for storage, analysis, and sharing.
• Wireless Communication: Use of LTE, 5G, or mesh networks in field operations.

1. Pre-Deployment Planning
   • Define inspection objectives and target structures.
   • Select appropriate robotic platforms and sensors.
   • Upload base maps or BIM models for navigation.
2. Field Data Acquisition
   • Robots or drones deployed to disaster site.
   • Real-time data collection and monitoring.
3. Data Processing
   • Generation of 3D models or maps.
   • Detection of structural damage using AI tools.
4. Analysis and Decision Making
   • Structural health scoring.
   • Repair priority assessment and logistics planning.
5. Reporting and Archival
   • Automated report generation.
   • Integration with government or insurance systems.

29.7.1 Earthquake in Nepal (2015)
• UAVs used to inspect temple ruins and collapsed buildings in Kathmandu.
• Enabled creation of 3D reconstructions for cultural heritage restoration.

29.7.2 Hurricane Harvey (USA, 2017)
• Drones assessed flooded roads and bridges in Texas.
• Ground robots used in search and rescue operations.

29.7.3 Morandi Bridge Collapse, Italy (2018)
• Robotics deployed to analyze remaining sections and aid in forensic investigations.

• Navigation in Complex Environments: Unstable debris, GPS-denied zones.
• Power and Endurance: Battery limitations in long missions.
• Data Overload: Managing and processing large volumes of visual and sensor data.
• Regulatory Issues: Flight permissions, safety standards, and data privacy.
• Cost and Training: High initial cost and need for skilled operators.

• Swarm Robotics: Collaborative teams of drones/robots covering large disaster zones.
• AI-Driven Autonomy: Fully autonomous inspection with minimal human intervention.
• Integration with BIM and GIS: For real-time overlay of structural data.
• Mixed-Reality Visualization: For engineers and decision-makers using AR/VR tools.
• Self-Repair Bots: Future concept of robots that could perform emergency patching.