As-built documentation is an essential set of records, consisting of construction drawings, specifications and equipment location, which are kept for facility management purposes. These documents are constantly being created and modified throughout the life of a project. This process is usually manual and fraught with errors, which inhibits reliable decision making. Technological advancements have made it possible to generate 3D models to assess as-built conditions for construction monitoring purposes, such as verifying conformance to baseline project schedules and contract specifications. For this purpose, 3D point clouds are widely generated using laser scanners. However, this approach has limitations in the construction industry due to the expensive and fragile equipment, lack of portability and need of trained operators. This study aims at investigating an alternate technology to generate as-built 3D point clouds using photos taken using handheld digital cameras, compare them against the original as-built 3D models, and check for accuracy of the modeling process. This analysis can aid in more reliable and effective decision making due to its cost effectiveness and ease of use, particularly in heavy infrastructure projects which are continually undergoing rehabilitation work. To achieve these objectives, a set of guidelines are developed for taking photographs that enable effective generation of 3D point clouds using off-the-shelf software packages. The accuracy of the modeling process is investigated using the results of the as-built 3D point cloud modeling of a 2000 feet under construction bridge in southern United States. Finally, the range of tolerance and deviation of element dimensions is determined by comparing the photo based model to the actual as-built model (developed using 2D drawings). Furthermore, to compare point clouds of laser scanning and photogrammetry, a laser scan and an image based survey of an exterior wall of a university building was also done. Results show that this technology in its present state is not suitable for modeling infrastructure projects, however technological developments can enable this to be an efficient way to extract measurements of inaccessible objects for progress monitoring purposes and the models can also be stored for future dimension takeoffs for decision making and asset management purposes. / text

Digital models are essential to quantifying the behavior of structural systems. In many cases, the creation of these models involves manual measurements taken in the field, followed by a manual creation of this model using these measurements. Both of these steps are time consuming and prohibitively expensive, leading to a lack of utilization of accurate models. We propose a framework built on the processing of 3D laser scanning data to partially automate the creation of these models. We focus on steel structures, as they represent a gap in current research into this field. Previous research has focused on segmentation of the point cloud data in order to extract relevant geometries. These approaches cannot easily be extended to steel structures, so we propose a novel method of processing this data with the goal of creating a full finite element model from the information extracted. Our approach sidesteps the need for segmentation by directly extracting the centerlines of structural elements. We begin by taking "slices" of the point cloud in the three principal directions. Each of these slices is flattened into an image, which allows us to take advantage of powerful image processing techniques. Within these images we use 2d convolution as a template match to isolate structural cross sections. This gives us the centroids of cross sections in the image space, which we can map back to the point cloud space as points along the centerline of the element. By fitting lines in 3d space to these points, we can determine the equations for the centerline of each element. This information could be easily passed into a finite element modeling software where the cross sections are manually defined for each line element. / Modern buildings require a digital counterpart to the physical structure for accurate analysis. Historically, these digital counterparts would be created by hand using the measurements that the building was intended to be built to. Often this is not accurate enough and the as-built system must be measured on site to capture deviations from the original plans. In these cases, a large amount of time must be invested to send personnel out into the field and take large amounts of measurements of the structure. Additionally, these "hand measurements" are prone to user error. We propose a novel method of gathering these field measurements quickly and accurately by using a technique called "laser scanning". These laser scans essentially take a 3D snapshot of the site, which contains all the geometric information of visible elements. While it is difficult to locate items such as steel beams in the 3D data, the cross sections of these structural elements are easily defined in 2D. Our method involves taking 2D slices of this 3D scan which allows us to locate the cross sections of the structural members by searching for template cross-sectional shapes. Once the cross sections have been isolated, their centers can be mapped back from the 2D slice to the 3D space as points along the centerlines of the structural elements. These centerlines represent one of the most time consuming requirements to building digital models of modern buildings, so this method could drastically reduce the total modeling time required by automating this particular step.