Scan4Façade: Automated As-Is Façade Modeling of Historic High-Rise Buildings Using Drones and AI

This paper presents an automated as-is façade modeling method for existing and historic high-rise buildings, named Scan4Façade. To begin with, a camera drone with a spiral path is employed to capture building exterior images, and photogrammetry is used to conduct three-dimensional (3D) reconstruction and create mesh models for the scanned building façades. High-resolution façade orthoimages are then generated from mesh models and pixelwise segmented by an artificial intelligence (AI) model named U-net. A combined data augmentation strategy, including random flipping, rotation, resizing, perspective transformation, and color adjustment, is proposed for model training with a limited number of labels. As a result, the U-net achieves an average pixel accuracy of 0.9696 and a mean intersection over union of 0.9063 in testing. Then, the developed twoStagesClustering algorithm, with a two-round shape clustering and a two-round coordinates clustering, is used to precisely extract façade elements’ dimensions and coordinates from façade orthoimages and pixelwise label. In testing with the Michigan Central Station (office tower), a historic high-rise building, the developed algorithm achieves an accuracy of 99.77% in window extraction. In addition, the extracted façade geometric information and element types are transformed into AutoCAD command and script files to create CAD drawings without manual interaction. Experimental results also show that the proposed Scan4Façade method can provide clear and accurate information to assist BIM feature creation in Revit. Future research recommendations are also stated in this paper

A Model for Automated Construction Materials Tracking

Materials management is a critical factor in construction project performance, particularly in the industrial sector. Research has shown that construction materials and installed equipment may constitute more than 50% of the total cost for a typical industrial project. Therefore the proper management of this single largest component can improve the productivity and cost efficiency of a project and help ensure its timely completion. One of the major problems associated with construction materials management is tracking materials in the supply chain and tracking their locations at job sites. Identification is integral to this process. Research projects conducted during the last decade to automate the identification and tracking of materials have concluded that such automation can increase productivity and cost efficiency as well as improve schedule performance, reduce the number of lost items, improve route and site optimization, and improve data entry. However, these technologies have been rapidly evolving, and knowledge concerning their implementation is sparse. One new approach enables locating of components within a few meters at a cost at least a magnitude lower than preceding technologies. It works by combining GPS located reads of RFID tags read at a rate of several thousand Hertz in order to estimate the location of these inexpensive tags which are attached to key construction materials. This technology was rapidly prototyped and deployed on two large industrial construction projects in 2007 and 2008. This thesis analyzes and synthesizes the data and experiences from these unique and large scale field trials as well as the literature in order to develop a general implementation model for automated construction materials tracking for industrial projects. It is concluded from the model that this new automated construction materials tracking technology is likely to be successful if implemented full scale on well selected future projects. This conclusion is supported by subsequent industry decisions