Course title: Resilience, Sustainability & Digitalisation in Critical Infrastructure

Introductory video:

Point cloud generation:

The value of emerging technologies in climate resilience and sustainability of infrastructure.

Emerging and disruptive digital technologies have the potential to enhance climate resilience of critical infrastructure, by providing rapid and accurate assessment of asset condition and support decision-making and adaptation. Such emerging digital technologies include Internet of Things, digital twins, Artificial Intelligence which can be placed at the service of engineers to design more sustainable and resilient structures.

What is a point cloud and why we need them in infrastructure management?

Point clouds hold rich spatial data for comprehending and managing infrastructure assets, e.g., bridges. They offer precise, detailed structural representations, capturing geometry and are valuable in creating digital twins, which mirror real-world structures. Point clouds and digital twins act as indistinguishable digital counterparts, facilitating simulation, testing, monitoring, and adaptation to stressors like climate change.

What is a point cloud and why we need them in infrastructure management? Point clouds offer a wealth of spatial information that helps in understanding, analyzing, and managing infrastructure assets, like bridges. Point clouds provide a highly accurate and detailed representation of the structure. They capture the geometry. They are very useful in generating digital twins, which are digital representations of an actual real-world physical structure. Point clouds and digital twins serve as the effectively indistinguishable digital counterpart of a structure for practical purposes, such as simulation, testing, monitoring, and adaptation to new stressors, such as climate change.

Main steps for the development of the point cloud. 

To generate a point cloud for a bridge that you see behind me you would typically follow a number of steps:

1. Data capture planning: First we need to determine the appropriate data capture method based on the size, complexity, and accessibility of the bridge. Common methods include airborne, mobile, or terrestrial LiDAR. The selection should consider factors like resolution requirements & accuracy depending on the purpose of the point cloud.

2. Data acquisition: We then use the chosen data capture method to collect the necessary data. For example, if we use a terrestrial LiDAR, that requires setting up stationary scanners and capturing data from multiple viewpoints to cover all sizes of the bridge-that will lead us to millions after millions of points each one of which has a unique set of coordinates.

3. Reference Points: Thus, we also need a coordinate system and reference points. These reference points serve as common origin or baseline for all the points captured in the cloud. Commonly we use as reference points global positioning system (GPS) coordinates or survey control points. 

Weather Conditions: Weather significantly affects point cloud data quality and reliability, notably especially in remote sensing tech like LiDAR (Light Detection and Ranging). Sunlight causes shadows, impacting accuracy. Lighting conditions should be carefully considered when planning data collection. Additionally, motion artifacts can result from traffic and passersby. Hence, data from multiple sources, viewpoints, and time instances must be combined.