In the past, to ensure that concrete floors were level and flat, floor inspectors and contractors would use electronic floor profilers. Leading building experts now, however, use laser scanning—a far more precise, effective, and adaptable method. Contractors and inspectors can easily determine the exact flatness of any concrete floor thanks to laser scanning instruments, which completely comply with industry norms such as ASTM, ADA, and AISC.
Laser scanning, as opposed to traditional measurement techniques, collects thorough, incredibly precise, as-built data to preserve project tolerances. Users may swiftly locate and fix high or low spots in flooring thanks to this scanning technique, which gathers millions of points across vast sections of concrete.
Let us now get into how the process of laser scanning for floor flatness and levelness actually works.
Performing laser scanning for floor flatness and levelness
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For finding out the FF and FL values on concrete floors and flatwork, 3D laser scanning rapidly and affordably collects highly-detailed point cloud data. It begins with accurate reality capturing of the site and it’s floor components.
Before scanning, ensure the area is free of obstructions and calibrate the laser scanning equipment. The scanner captures 3D data on floor surfaces, providing real-time monitoring. Then it is the point cloud to heatmap conversion that takes place.
Point cloud to Heatmap data generation
Converting a point cloud to a heatmap involves several steps, generally including data preprocessing, interpolation, and visualization. Here’s a basic overview of the process:
Point Cloud Pre-Processing
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Filtering: In this stage, the point cloud data is cleaned of any noise or anomalies. Outliers or noise might skew the accuracy of the heatmap. Statistical procedures or thresholding can be used as filtering strategies to find and eliminate undesired data items.
Normalization: To guarantee scale uniformity throughout the dataset, normalization is implemented as needed. This stage facilitates data comparison across datasets and guarantees consistent data representation.
Spatial Partitioning
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There are smaller spatial divisions or grids created from the point cloud. Large datasets may be processed and managed more effectively with the support of this division. For this, methods like grid-based partitioning and voxelization are frequently employed.
Interpolation
Values are estimated for locations where data points are sparse or absent using interpolation techniques. Typical techniques for interpolation include:
Nearest-neighbor method: The closest data point is assigned to the missing location using the nearest-neighbor method.
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Inverse distance weighting: Making a weighted average of close-by data items depending on their distances is known as “inverse distance weighting.”
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“Inverse Distance Weighting (IDW) interpolation estimates unknown values with specifying search distance, closest points, power setting & barriers.”
Kriging: A geostatistical technique for interpolation that takes the data’s spatial autocorrelation into account.
Heatmap Generation
Density Calculation: The density of points within each grid cell determines the values that are allocated to those cells. Methods like Kernel Density Estimation (KDE) or a straightforward point count within each grid cell might be used for this.
Color Mapping: A color gradient is used to map density values to different colors. Warmer colors, such as red, can symbolize higher densities, whereas cooler colors, such as blue, can represent lower densities. The color gradient makes differences in density across the heatmap easier to see.
Visualization
The necessary libraries or applications are used to visualize the heatmap. Tools such as Matplotlib, Plotly, or specialist GIS (Geographic Information System) software may fall under this category. To improve clarity and understanding, visualization factors like color scale, opacity, and grid size can be changed.
Also Read: BIM And GIS Integration: Bringing Together Geo-spatial Data And Design
Post-processing
It is possible to enhance visual clarity by using post-processing techniques once the heatmap has been generated. Smoothing techniques may be used in this to improve feature visibility or lower noise. To help with understanding, additional visual components like legends and annotations may be provided.
Optional Analysis
The created heatmap can be subjected to additional research in order to glean insights or spot patterns in the data. To find temporal or geographical trends, this may entail comparing numerous heatmaps, locating activity hotspots, or doing a clustering study.
Keep in mind that the specific techniques and tools used for each step may vary depending on the requirements of the project and the characteristics of the point cloud data. Additionally, the effectiveness of the conversion process depends on factors such as data quality, interpolation method, and visualization parameters.
Why use laser scanning for floor flatness and levelness
Precision is one of the key advantages of using laser scanning for floor flatness reports. One could be able to see and modify high or low locations with more accuracy thanks to this approach.
A long-range 3D scanner with tilt compression accurate to 1.5 arc seconds and +/- 2 mm precision for this building job across wide areas, this scanner reliably gathers measurement data.
Another significant benefit of long-range laser scanning is speed. We can collect up to a million data points every second during each scan. In addition to point cloud data, it also records a 360° perspective around the area, enabling us to offer a thorough elevation assessment.
Also, what makes floor scanning or laser scanning for floor testing reporting more effective is the state-of-the-art software that is being deployed in the process. Construction crews may easily examine and accurately verify laser-scanned floor measurements with the help of modern software.
Further Reading
