Role Of Engineers In Disaster Management

The wildfires in Los Angeles have been devastating—homes lost, lives upended, and communities left reeling. It’s made me think: How can we use the incredible technology in defense to make a real difference when disasters strike? Like disaster response, defense tech thrives under pressure—missions demand speed, precision, and adaptability. Here are a few ways I see defense innovations playing a bigger role in protecting lives and communities: - Advanced Drones: These aren’t just for reconnaissance. Firefighters could use them to map wildfires in real time, monitor how they spread, and even guide evacuations. Drones with thermal imaging could cut through heavy smoke, giving responders the visibility they desperately need. - AI and Predictive Analytics: Security professionals already use AI to predict enemy movements. That same tech could be used to analyze weather, terrain, and vegetation to predict how a fire will behave, potentially changing the game for planning and containment efforts. - Communication Systems: Satellite communications and encrypted data transmission allow for real-time coordination among rescue teams, faster dissemination of critical information to affected populations, and more efficient management of relief efforts. This isn’t just about technology—it’s about leadership. Companies building defense tech have an incredible opportunity to step up and work directly with emergency services. How can we make sure defense tech companies and disaster management teams collaborate more effectively? What incentives could help drive innovation that serves both national security and public safety? The tools, talent, and technology already exist—we just need to make the connections. The next time disaster strikes, let’s make sure we’re ready to respond with everything we’ve got, not just as technologists, but as people who care deeply about protecting our communities.

When a #disaster happens, you can't waste any minute, and accurate, up-to-date data is crucial for disaster response.   This March, heavy rains in the municipality of Mimoso do Sul, Espírito Santo, Brazil, led to severe flooding, resulting in deaths and damages. To plan their actions, first responders led by the Jones dos Santos Neves Institute (IJSN) tried to use satellite imagery, but due to the dense cloud cover of the area and low resolution of images, this data didn't help much. Luckily, the IJSN and its partners had drones that helped to overcome this challenge. They used SPH Engineering #UgCS to plan and execute photogrammetry missions and capture geospatial data in these challenging conditions. Over two days, they collected thousands of photos, integrated these images with other data and identified almost 4,000 properties in the city heavily affected by the flooding. 

Have you seen this image circulating this week? Chances are you have! A tornado tore through a solar farm in Highline County, Florida during Hurricane Milton, and the damage is striking. A swath of solar modules was ripped from the single-axis trackers holding them in place. 🌪 As the renewable energy industry continues to grow and innovate, this event underscores the critical need to design and build projects that are more resilient to extreme weather events. Moreover, it serves as a clear reminder of the importance of ensuring and practicing the adoption of up-to-date, modern building codes and standards, given that most infrastructure systems across the U.S. were not built to withstand storms of this magnitude. 💡 That said, let’s take a closer look at the details: The storm was classified as an EF-2 tornado, with wind speeds of 111 to 135 mph. Duke Energy’s Lake Placid Solar Power Plant was commissioned in December 2019. At that time, the 6th Edition (2017) Florida Building Code was in place, which referenced the American Society of Civil Engineers (ASCE) 7-10 Standard for Minimum Design Loads and Associated Criteria for Buildings and Other Structures. Since then, ASCE 7-16 (2016) and ASCE 7-22 (2022) have been published, which include several notable changes to wind load provisions and criteria. These updates feature revisions to wind speed maps, the introduction of solar facilities provisions, updates to Risk Category designations, new tornado loads and guidance, and a host of other changes. As you can see, it’s imperative that future building code cycles integrate up-to-date engineering standards. I strongly believe that it is up to us — engineers, stakeholders, officials, and AHJs — to adopt these new codes and standards for the design, permitting, and inspection of new infrastructure projects. Manufacturers must then adjust their products to meet these new code requirements as well. Unfortunately, this entire process can be long, slow, and the adoption of new codes varies across the U.S. For reference, per the current 8th Edition (2023) Florida Building Code, which has been updated to reference ASCE 7-22 (the first state to do so, by the way), Risk Category II buildings and structures built in Highline County must be designed to resist the load effects caused by wind speeds of up to 140 mph. And to be clear, solar facilities are designated as Risk Category II infrastructure! Let me know what you think. 👇🏽

$𝟱𝟱 𝗕𝗶𝗹𝗹𝗶𝗼𝗻 𝗶𝗻 𝗟𝗼𝘀𝘀𝗲𝘀 - 𝗜𝘀 𝗬𝗼𝘂𝗿 𝗣𝗼𝘄𝗲𝗿 𝗚𝗿𝗶𝗱 𝗥𝗲𝗮𝗱𝘆 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗡𝗲𝘅𝘁 𝗕𝗶𝗴 𝗦𝘁𝗼𝗿𝗺? Storm-related events cause damage of $20B to $55B annually in the US. Examples of notable events are; > Hurricane Laura (2020) $17.5B. > Hurricane Ida (2021) $78.7B. > Winter Storm Uri (2021) $80B to $130B These events underscore the need for resilient power infrastructure that can withstand 21st-century threats, both manmade and natural. Here is how utilities can enhance their power resiliency with maximum efficiency: 𝗦𝗺𝗮𝗿𝘁𝗲𝗻𝗶𝗻𝗴 𝘁𝗵𝗲 𝗴𝗿𝗶𝗱 → Use of advanced meters for automatic outage detection and service restoration → Data collection for resilience improvements 𝗛𝗮𝗿𝗱𝗲𝗻𝗶𝗻𝗴 𝘁𝗵𝗲 𝗴𝗿𝗶𝗱 → Raise seawalls around key assets → Restore natural coastal protections → Underground powerlines 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗻𝗴 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 → Increase the number of generation sources → Diversify fuel types → Locate sources in a more distributed way around service areas 𝗕𝘂𝗶𝗹𝗱 𝗿𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 𝗼𝗻 𝗱𝗲𝗺𝗮𝗻𝗱 → Use of battery storage → Internet-addressable thermostats and appliances → Passive solars Grid resiliency comes with an additional cost, but it is crucial for continuous energy supply to consumers even in uncertain conditions that may arise due to natural disasters or manmade causes. ASEC ENGINEERS has specialized in grid management and resiliency since 1991. With a focus on reliability, client compliance, wildfire mitigation, and advanced mapping integration, we offer comprehensive and tailored solutions for today’s toughest challenges. #innovation #technology #energy #sustainability #electricalengineering

🚁 New Publication Alert: "Enhancing Disaster Resilience with UAV-Assisted Edge Computing" Excited to share our latest research published in the ACM Journal on Autonomous Transportation Systems (May 2025). Our work titled "Enhancing Disaster Resilience with UAV-Assisted Edge Computing: A Reinforcement Learning Approach to Managing Heterogeneous Edge Devices" delves into crucial advancements in disaster management. 🔍 Core Problem: In times of disasters such as floods, wildfires, or grid failures, stationary edge devices often encounter power and connectivity disruptions. Our study investigates leveraging drones (UAVs) to assist these devices by: - Offloading compute tasks to conserve battery life. - Serving as relay nodes to ensure network coverage. 🧠 Our Approach: We employ reinforcement learning to: - Model power/connectivity failures across various edge devices. - Forecast potential device failures. - Guide maintenance efforts and prioritize critical assistance. 🏙️ Simulation Highlights: Our experiments span rural town and dense urban evacuation scenarios, showcasing: - Substantial network operation extension. - Enhanced device longevity. 💡 Key Takeaway: Intelligent UAV deployment, guided by data-driven insights, fortifies emergency systems, directing resources to areas of utmost need. This research represents a significant stride towards adaptive emergency response systems integrating edge computing with aerial support. We anticipate this framework will benefit disaster relief planners, urban resilience teams, and networks seeking autonomous reliability. Kudos to my dedicated PhD students Talha Azfar (leading the implementation) and Kaicong Huang for their invaluable contributions. 👥 If you're engaged in UAV systems, critical environment edge computing, or resource optimization through reinforcement learning, let's connect and explore potential collaborations! Access the paper for free here: [Link to the Publication](https://lnkd.in/dbJX2Aqk) #UAV #EdgeComputing #ReinforcementLearning #DisasterResilience #AutonomousSystems

What if your next firefighter was a robot on four legs? 🤖🔥     In a recent breakthrough from China, emergency response teams have deployed quadruped firefighting robots, agile and intelligent machines designed to navigate disaster zones where humans can't.     This robot is equipped with a water cannon, thermal imaging, and autonomous mobility.      It is trained to combat fires in high-risk environments like chemical plants, tunnels, and collapsed buildings, reducing danger for frontline responders. 👨🚒     💡 Why does this matter?     Reaches places too dangerous or inaccessible for humans    Operates in extreme heat, toxic smoke, and unstable terrain    Enhances efficiency in emergency rescue and disaster relief     🛠️ Built and tested in China, these robots are part of the country's push for AI-powered emergency tech.     The results are already turning heads in public safety circles worldwide.     🌍 The social impact?     Safer working conditions for fire crews.     Faster, smarter emergency response.     Fewer lives lost in industrial and urban disasters.     Excited to see this innovation reach India, where such tech can revolutionize disaster management, especially in crowded cities and hazardous industrial zones.     The future of public safety is not just human. It is human + machine, working together.     #applogiq #firefighting #robotics #emergencyresponse #ai #publicsafety #disastertech #robotdog #smartcities #makeinindia #innovationforgood #techforimpact #makedigitallives 

Code Implementation of a Rapid Disaster Assessment Tool Using IBM’s Open-Source ResNet-50 Model (Colab Notebook Included) In this tutorial, we explore an innovative and practical application of IBM’s open-source ResNet-50 deep learning model, showcasing its capability to classify satellite imagery for disaster management rapidly. Leveraging pretrained convolutional neural networks (CNNs), this approach empowers users to swiftly analyze satellite images to identify and categorize disaster-affected areas, such as floods, wildfires, or earthquake damage. Using Google Colab, we’ll walk through a step-by-step process to easily set up the environment, preprocess images, perform inference, and interpret results..... Full Tutorial: https://lnkd.in/gzUwG58B Colab Notebook: https://lnkd.in/gwCz-tcT

In disaster scenarios, timing can mean the difference between safety and catastrophe. Dr. Dianhong Chen, a researcher at the University of Texas at Arlington, has developed an AI-powered tool that helps emergency managers simulate and optimize real-time evacuation plans. Using traffic flow data, population density, and infrastructure stressors, the system enables tailored strategies for hurricanes, wildfires, and other emergencies. The innovation offers a crucial advantage: moving from generic evacuation orders to dynamic, localized guidance that evolves with the crisis. With extreme weather events becoming more frequent, this kind of AI-driven adaptability could be a game-changer for public safety. Smart evacuation isn’t just faster—it’s fairer, safer, and more responsive to real human conditions on the ground. Key Takeaways: - AI is enabling real-time, data-informed evacuation decision-making - The tool accounts for congestion, vulnerable populations, and disaster type - Smarter evacuation planning enhances both speed and equity in crisis response Read the full article: https://lnkd.in/eTgHT_XQ

Now, we’re only one click away from turning hours of post-disaster #surveying… into instant, #actionable #insights. Having worked in multiple #disaster and post-disaster sites, I’ve always wished for one thing: Less time spent painstakingly #mapping and #quantifying #damage, and more time spent #planning and executing #emergency #interventions. When it comes to #historic #structures, #time is not just money. It’s the difference between #saving #heritage and losing it forever. That’s why I’m so glad I developed a 3D point-cloud damage #segmentation tool that instantly identifies, visualizes, measures, and tabulates #structural damage in historic #buildings. With a single click, we can now transform scans into clear, quantifiable maps of where help is needed most, speeding up decision-making when every second counts. To know more, check my published paper here: https://lnkd.in/eGgCaDSU Penn State College of Engineering Penn State University #postdisaster #disasterriskreduction #risk #reduction #response #disasterrespinse #natural #manmade #hazards #manmade #3D #pointcloud #scanning #photogrammetry #technology #tech #AI #ML #artificial #intelligence #artificialintelligence #machine #learning #machinelearning #impact #beirut #ukraine #unesco #pennstate

Natural disasters are projected to cause approximately $460 billion in annual losses to infrastructure globally by 2050. How can we fundamentally shift from reacting to these events to proactively building for a more resilient future? A new report from Deloittehighlights the urgent need for a new approach to infrastructure development. As the value of our physical and digital networks grows, they become more vulnerable to natural disasters that are growing more frequent and intense. The good news is that AI offers a powerful solution by helping us build smarter, more durable systems that are ready for future challenges. Here’s how AI helps at every stage of a disaster: 🟢 Before the Storm (Plan): AI-powered "digital twins" can simulate how a new bridge or power station would handle a flood, allowing engineers to strengthen the design and make it more resilient from the very beginning. This kind of planning can make a huge difference. 🟢 During or just before an event (Respond): AI-driven early warning systems use real-time data from sensors and weather patterns to predict events like floods or wildfires. This gives people more time to prepare, which can reduce overall damage and even save lives. You've probably seen me post about tools like this from Google! 🟢 After the Damage (Recover): After a disaster, AI tools using realtime images can quickly scan a damaged area to see what needs to be fixed first. This helps get power and water services back online much faster, limiting economic disruption. It’s clear that building for resilience means building with intelligence. For better or for worse, planning to use AI tools for natural disasters is a crucial step in ensuring our world can handle the challenges ahead. #DigitalTransformation #SustainabilityTech #Resilience #SmartCities #Google