Although construction is often seen as an outdoor activity, much of the work is completed indoors. The indoor environment presents many challenges for the automatic capture of data during progress monitoring, quality control, quantity verification, and overall project documentation. This paper examines the potential of using robots, and more specifically, unmanned aerial vehicles (UAV) and unmanned aerial systems (UAS), to monitor indoor construction sites. UAVs have become ubiquitous due to their commercialization as an off-the-shelf tool. However, their potential to facilitate the automation of many mundane and challenging activities in indoor construction is very high. Unfortunately, they also come with some important challenges. This paper explores the potential applications for UAV in indoor construction sites, their benefits, and their challenges. A sample of the benefits of this technology include their in-flight agility, capacity to hold an array of sensors, and automation potential. Challenges include potential safety risks if they distract or interfere with workers, navigation within ever changing surroundings, battery capacity limits, and impacts of automation. Finally, challenges of a multi-sensory smart UAS with respect to emerging technologies such as internet of things (IoT) and possible solutions are discussed.

Inspection planning is a primary element of computer vision- and unoccupied aerial vehicle (UAV)-enabled construction monitoring. Prior to the on-site deployment of camera-mounted UAVs, the inspection objectives need to be identified, and optimal inspection plans must be developed; Such plans should ensure complete data acquisition and minimize the use of UAV’s limited flight time. The image capture configuration must be taken into account since it directly affects the downstream applications of the captured data such as progress detection and as-built modeling. This paper proposes a framework and a novel technique which utilizes four-dimensional (4D) building information models (BIM) and swarm intelligence to automatically generate the UAV inspection mission plans. It computationally supports both static and dynamic site layouts. The inspection objectives, their geometry, and their semantics are automatically extracted from BIM, and the corresponding elements are identified. An optimal inspection plan is developed using artificial intelligence, ensuring complete coverage of inspection targets while minimizing flight duration. The method has been tested in UAV-enabled data acquisition scenarios. It is based on the industry foundation classes (IFC), facilitating OpenBIM and reducing the costs associated with the lack of interoperability, a core challenge in information modeling. Due to the target extraction at element and sub-element levels, it supports computer vision-based construction progress monitoring and automated as-built and as-is BIM development.

Remoteness has a crucial role in risk assessments of megaprojects, resilience assessments of communities and infrastructure, and a wide range of public policymaking. The existing measures of remoteness require an extensive amount of population census and of road and infrastructure network data, and often are limited to narrow scopes. This paper presents a methodology to quantify remoteness using nighttime satellite imagery. The light clusters of nighttime satellite imagery are direct yet unintended consequences of human settled populations and urbanization; therefore, the absence of illuminated clusters is considered as evidence of remoteness. The proposed nighttime remoteness index (NIRI) conceptualizes the remoteness based on the distribution of nighttime lights within radii of up to 1,000 km. A predictive model was created using machine learning techniques such as multivariate adaptive regression splines and support vector machines regressions to establish a reliable and accurate link between nighttime lights and the Accessibility/Remoteness Index of Australia (ARIA). The model was used to establish NIRI for the United States and Canada, and in different years. The index was compared with the Canadian remoteness indexes published by Statistics Canada.

Job safety analysis (JSA) is a safety management framework for the construction sector that relies on manual processes and is constrained by the limitations of manual performance. Although ...