Near real-time tracking of construction operations and timely progress reporting are essential for effective management of construction projects. This does not only mitigate potential negative impact of schedule delays and cost overruns but also helps to improve safety on site. Such timely tracking circumvents the drawbacks of conventional methods for data acquisition, which are manual, labor-intensive, and not reliable enough for various construction purposes. To address these issues, a wide range of automated site data acquisition, including remote sensing (RS) technologies, has been introduced. This review article describes the capabilities and limitations of various scenarios employing RS enabling technologies for localization, with a focus on multi-sensor data fusion models. In particular, we have considered integration of real-time location systems (RTLSs) including GPS and UWB with other sensing technologies such as RFID, WSN, and digital imaging for their use in construction. This integrated use of technologies, along with information models (e.g., BIM models) is expected to enhance the efficiency of automated site data acquisition. It is also hoped that this review will prompt researchers to investigate fusion-based data capturing and processing.

A considerable body of literature exists on automated object localization and tracking of construction operations. While GPS-based solutions have been widely investigated in many studies for outdoor tracking of these operations, indoor tracking proved to be more challenging. This paper focuses on indoor material localization and investigates the use of two remote sensing technologies—ultra-wideband and radio frequency identification—and the integrated use of these technologies to leverage the benefits of each for a cost-effective and practical solution for location identification of materials on site. The developed method is based on an experimental study conducted in two phases. In the first phase, experiments are designed and performed to evaluate the accuracy of ultra-wideband for localization, as well as to determine the optimal output power for a hand-held radio frequency identification reader. The optimal power is identified by evaluating the range measurement accuracy and maximum reading range of the hand-held radio frequency identification reader. In the second phase, the integrated use of radio frequency identification device and ultra-wideband for object localization is studied, and an improved trilateration technique is developed. The results of the experiments show an absolute error of 0.52 m and 1.15 m for 2D and 3D localization, respectively. Accordingly, the integration of these two technologies eliminates the need for using a large number of radio frequency identification reference tags on site for indoor material localization. The method is expected to enhance automated material tracking on construction sites by improving the localization accuracy and providing a straightforward data acquisition protocol. The analysis of experimental data captured in a lab setting is also presented, demonstrating the advantages of the proposed method.

This paper introduces a newly developed framework for the automated generation of inspection reports in construction. The method utilizes earlier published developments by the authors on object recognition and localization. Inspection reports in this paper are documents that monitor and record the progress of installed project components and their targeted locations. Components in this research are also referred to as objects, which include installed piping systems, tanks, and mechanical equipment. The reports identify the deviations of the installed components by comparing their as-built status to the asplanned ones. The framework integrates the data acquired by a Real-Time Location System (RTLS) and a computer vision-based LiDar technology to generate the reports using object identification and localization. While considerable work has been reported using such technologies, the contribution of the framework presented here lies in the efficient integration of these technologies to acquire as-built 3D coordinates of the installed components. The framework has been validated through laboratory experiments, demonstrating an accuracy of approximately 27 centimetres for the installations' coordinates in the inspection reports. The framework presented here can be used for project commissioning to ascertain the precise locations of the project's installations.