The wastewater industry is constantly evolving. New developments in technology show us faster and more accurate ways to complete maintenance work—and often with fewer resources. Through these advancements, we get a glimpse into what’s possible in the industry and what more can be learned from our systems underground.
Today, we see this in the buzz surrounding artificial intelligence and its potential role in sewer inspections: automatically identifying defects and coding accordingly before a human operator gets involved. And we see this in a shift toward 3D data as an integral part of inspection workflows. When 3D data is acquired, it offers additional insight into sewers.
This data can be used to further characterize defects, and also to build a virtual model of a collection system. A virtual model can benefit engineers in a number of ways, including helping them model flow capacity, better understand operational risks, work more proactively to predict failures, perform what-if analyses, and optimize overall operations. A virtual model can also be used for augmented reality, giving site workers a virtual glimpse at infrastructure underground in order to plan work. There are a variety of methods used for acquiring 3D data in sewers.
As with laser triangulation, LiDAR uses laser light to scan 3D space. But instead of relying on geometric calculations, LiDAR uses time-of-flight, wavelength and projection angle to determine the distance and location where a laser point hits an object. Multiply this over millions of points and you end up with a cloud of points that can be stitched together into a geometric mesh. In pipe and manhole inspections, LiDAR can create a continuous 3-D model. The ovality and surface condition of the pipe, joint and positions and offsets, size and location of defects and connections, flow level, and more can be identified from this model. Simple LiDAR rangefinders can also be used for tasks like measuring the depth of a manhole, or the distance to a lateral connection within a mainline.
With photogrammetry, geometry can be determined by comparing a series of images (usually sequential frames of video) collected during an inspection. Dimensional data is extracted from the footage based on how things change from one video frame to the next. Unlike other methods, this process can be applied to existing inspection video for a retrospective analysis.
An inertial navigation system (INS), also known as an XYZ sensor, is being added to small-diameter inspection equipment to track the camera’s orientation and position in 3D space. These sensors are beneficial for understanding the location of assets--particularly where lateral and drain lines bend, at what angle, and in what direction.
Many of these technologies are effective for capturing 3D data from asset interiors, while others are well suited for establishing the position of those assets in world space or the trajectory of a line. When these technologies are combined, it allows for the creation of an all-purpose virtual model. Virtual models can help with flow studies, rehabilitation and maintenance planning, capacity planning, and what-if modeling. Combining traditional inspection data with 3D data can also enable augmented reality (AR), where asset features and parameters are georeferenced—or placed in real-world 3D space--and then overlaid on live video. AR will allow a field technician with a smartphone or inspection camera to see virtual representations of hidden infrastructure and details about an assets’ composition, condition and history.