Evolution of RFID applications in construction:A literature review

Radio frequency identification (RFID) technology has been widely used in the field of construction during the last two decades. Basically, RFID facilitates the control on a wide variety of processes in different stages of the lifecycle of a building, from its conception to its inhabitance. The main objective of this paper is to present a review of RFID applications in the construction industry, pointing out the existing developments, limitations and gaps. The paper presents the establishment of the RFID technology in four main stages of the lifecycle of a facility: planning and design, construction and commission and operation and maintenance. Concerning this last stage, an RFID application aiming to facilitate the identification of pieces of furniture in scanned inhabited environments is presented. Conclusions and future advances are presented at the end of the paper

3D GEOSPATIAL INDOOR NAVIGATION FOR DISASTER RISK REDUCTION AND RESPONSE IN URBAN ENVIRONMENT

Disaster management for urban environments with complex structures requires 3D extensions of indoor applications to support better risk reduction and response strategies. The paper highlights the need for assessment and explores the role of 3D geospatial information and modeling regarding the indoor structure and navigational routes which can be utilized as disaster risk reduction and response strategy. The reviewed models or methods are analysed testing parameters in the context of indoor risk and disaster management. These parameters are level of detail, connection to outdoor, spatial model and network, handling constraints. 3D reconstruction of indoors requires the structural data to be collected in a feasible manner with sufficient details. Defining the indoor space along with obstacles is important for navigation. Readily available technologies embedded in smartphones allow development of mobile applications for data collection, visualization and navigation enabling access by masses at low cost. The paper concludes with recommendations for 3D modeling, navigation and visualization of data using readily available smartphone technologies, drones as well as advanced robotics for Disaster Management

A benchmark framework of geometric digital twinning for slab and beam-slab bridges

We devise, implement, and benchmark a framework LUKIS to automate the process of geometric digital twinning for existing slab and beam-and-slab bridges. LUKIS follows a top-down strategy to detect and twin bridge concrete elements in point clouds into an established data format Industry Foundation Classes. Existing software packages require modellers to spend many labour hours in generating shapes to fit point cloud sub-parts. Previous methods can generate surface primitives combined with rule-based classification to produce cuboid and cylinder models. While these methods work well in synthetic datasets or simplified cases, they encounter challenges when dealing with real-world point clouds. We tackle this challenge by investigating the entire workflow of geometric digital twinning for bridges and proposing LUKIS to auto-generate bridge objects without needing to generate low-level surface primitives. We implement LUKIS on a single software platform. Experiments demonstrate its ability to rapidly twin geometric bridge concrete elements. Compared to manual operation, LUKIS reduces the overall twinning time by at least 95.4% while the twinning quality (spatial accuracy) is improved. It is the first framework of its kind to achieve the geometric digital twinning for primary concrete elements of bridges on one platform. It lays foundations for researchers to generate semantically enriched digital twins

Generating bridge geometric digital twins from point clouds

The automation of digital twinning for existing bridges from point clouds remains unsolved. Extensive manual effort is required to extract object point clusters from point clouds followed by fitting them with accurate 3D shapes. Previous research yielded methods that can automatically generate surface primitives combined with rule-based classification to create labelled cuboids and cylinders. While these methods work well in synthetic datasets or simplified cases, they encounter huge challenges when dealing with realworld point clouds. In addition, bridge geometries, defined with curved alignments and varying elevations, are much more complicated than idealized cases. None of the existing methods can handle these difficulties reliably. The proposed framework employs bridge engineering knowledge that mimics the intelligence of human modellers to detect and model reinforced concrete bridge objects in imperfect point clouds. It directly produces labelled 3D objects in Industry Foundation Classes format without generating low-level shape primitives. Experiments on ten bridge point clouds indicate the framework achieves an overall detection F1-score of 98.4%, an average modelling accuracy of 7.05 cm, and an average modelling time of merely 37.8 seconds. This is the first framework of its kind to achieve high and reliable performance of geometric digital twin generation of existing bridges

Generating bridge geometric digital twins from point clouds

The automation of digital twinning for existing bridges from point clouds remains unresolved. Previous research yielded methods that can generate surface primitives combined with rule-based classification to create labelled cuboids and cylinders. While these methods work well in synthetic datasets or simplified cases, they encounter huge challenges when dealing with real-world point clouds. The proposed framework employs bridge engineering knowledge that mimics the intelligence of human modellers to detect and model reinforced concrete bridge objects in imperfect point clouds. Experiments on ten bridge point clouds indicate the framework can achieve high and reliable performance of geometric digital twin generation of existing bridges.This research is funded by EPSRC, EU Infravation SeeBridge project under Grant No. 31109806.0007 and Trimble Research Fun

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Automated Indoor Mapping with Point Clouds

This dissertation examines the current state of automated indoor mapping and modeling using point cloud data produced by close range remote sensing systems. The first part looks at reality capture techniques that convert the physical form of indoor spaces into point clouds of millions of measured points, each with an (x,y,z) coordinate value. The second part examines methods for teasing out geometries from these point clouds -- often complicated by noise and voids -- and converting them into 3D geometric models. The final part examines techniques for merging the coordinate reference systems of these indoor maps and models with those of the outdoor world, resulting in a seamless representation of space. Lessons learned in this study revealed that theories, techniques, and practices in indoor mapping remain relatively elementary compared to those for the outdoors, yet they also present significant opportunities for future research propelled by emerging developments in remote sensing and a growing demand for indoor maps

Automated segmentation and reconstruction of structural elements for indoor multi-level room environment

3D laser scanners provide accurate as-built conditions for the surrounding environment in the form of 3D point cloud data. Although this technology has had high attention from the construction industry for the as-built documentation of buildings, the reconstruction process, especially identification and segmentation of the building elements, still has manual and labor-intensive tasks leading to time-consuming and human errors. In addition, it has not reconstructed the building elements successfully yet in multi-level building spaces. In an effort to address these issues, this research proposes an automatic 3D reconstruction framework that identifies, segments, and reconstructs vertical and horizontal building elements from the point clouds of multi-level building spaces. The proposed framework composes of: (1) identifying locations, diameters, lengths and the number of vertical building elements using Hough line and circle transform; (2) comparing the dimensions of the walls to determine single- or multi-level building spaces; (3) developing the region of interest defined by the building codes; (4) implementing plane RANSAC for not only segmentation of the vertical building elements but also identification and segmentation of horizontal building elements; and (5) reconstructing the segmented building elements into simple forms. The effectiveness of the proposed methodology has been validated with high accuracy and low deviation in three different building spaces at Concordia University, Montreal, Canada