Purpose: The aim of this study is o examine the advantages and disadvantages of different existing scoring systems in the cybersecurity domain and their applicability to the AEC industry and to systematically apply a scoring system to determine scores for some of the most significant construction participants.Design/methodology/approach: This study proposes a methodology that uses the Common Vulnerability Scoring System (CVSS) to calculate scores and the likelihood of occurrence based on communication frequencies to ultimately determine risk categories for different paths in a construction network. As a proof of concept, the proposed methodology is implemented in a construction network from a real project found in the literature.Findings: Results show that the proposed methodology could provide valuable information to assist project participants to assess the overall cybersecurity vulnerability of construction and assist during the vulnerability-management processes. For example, a project owner can use this information to get a better understanding of what to do to limit its vulnerability, which will lead to the overall improvement of the security of the construction network.Research limitations/implications: It has to be noted that the scoring systems, the scores and categories adopted in the study need not necessarily be an exact representation of all the construction participants or networks. Therefore, caution should be exercised to avoid generalizing the results of this study.Practical implications: The proposed methodology can provide valuable information and assist project participants to assess the overall cyber-vulnerability of construction projects and support the vulnerability-management processes. For example, a project owner can use this approach to get a better understanding of what to do to limit its cyber-vulnerability exposure, which will ultimately lead to the overall improvement of the construction network's security. This study will also help raise more awareness about the cybersecurity implications of the digitalization and automation of the AEC industry among practitioners and construction researchers.Social implications: Given the amount of digitized services and tools used in the AEC industry, cybersecurity is increasingly becoming critical for society in general. In some cases, (e.g. critical infrastructure) incidents could have significant economic and societal or public safety implications. Therefore, proper consideration and action from the AEC research community and industry are needed.Originality/value: To the authors' knowledge, this is the first attempt to measure and assess the cybersecurity of individual participants and the construction network as a whole by using the Common Vulnerability Scoring System.

Building service robots rely on dense instrumentation of the building (e.g., Bluetooth beacons) or require high computational capabilities [e.g., simultaneous localization and mapping (SLAM)]. To overcome these limitations, studies explored a landmark-based localization and navigation approach based on inexpensive, computationally efficient, and easily configurable fiducial markers (e.g., AprilTags). However, context-specific assumptions were made regarding the fiducial marker characteristics and sensor configurations. Taking this forward, this study develops a generalized simulation-based approach to determine the optimal design characteristics of the fiducial marker network to achieve successful autonomous mobile indoor robot navigation with low instrumentation and computation. Different marker and camera parameters such as marker size and marker placement to optimize the density of fiducial markers (i.e., the ideal distance between subsequent markers) were investigated using Robot Operating Systems (ROS) and Gazebo (simulator) software. The simulation experiments focused on a specific robotic platform (i.e., TurtleBot3) and marker type (i.e., AprilTag). Results from the simulations suggested that with an increase in marker distance, the navigation success rate does not necessarily decrease. In addition, a marker size of 0.1 m performed the best in terms of navigation success rate (as high as 100% for a specific combination of marker height, marker size, and marker-to-marker distance). Future work aims to test this in a real-world setting to compare and analyze the simulated and actual performance of the robot. The proposed methodology is generic and can be applied to any mobile robotic platform, marker type, building type (e.g., residential), and application (e.g., construction progress monitoring).

Most studies either addressed the technical gaps or adopted technological strategies to enable construction automation and digitalization. However, the cybersecurity implications of such technologies received little attention. Existing cybersecurity studies from other domains cannot be directly adopted because examining the overall security, existence, and spread of vulnerability in a construction network requires comprehensive knowledge of the construction participants and processes. To address these gaps, this study a) proposes an agent-based vulnerability assessment framework and b) presents an example focusing on the interactions between a contractor and a 3D printing system to show the feasibility and effectiveness of the proposed framework. It is expected that the proposed framework and the results from the example will raise more awareness about the cybersecurity implications in the architecture, engineering, and construction (AEC) industry and construction research community. Future work aims to quantify the vulnerability scores of individual participants and construction networks.

Construction is progressively making strides towards embracing new technologies such as robotics, big data analytics, blockchain, machine learning, virtual reality, and augmented reality. The adoption of those technologies to promote automation coupled with the digitalization of the industry are the pillars of Construction 4.0 which is the construction industry’s equivalent of Industry 4.0. Experts believe that successful implementation of the same will have the ability to positively impact the whole life cycle of a construction project from project conception to the end of life phase. Particularly, researchers and practitioners suggest that robotics forms an integral part of the Construction 4.0 framework. This is mainly attributed to the numerous advantages they offer such as a) Productivity improvement: can perform tasks significantly faster without getting tired (e.g. laying bricks); b) Safety improvement: can work in harsh and unsafe environments where humans are unwilling or unable to work (e.g. gas pipe inspection); c) Cost-effectiveness: can be more economical than human counterparts (e.g. performance evaluation of existing old buildings); d) Quality improvement: can be more precise and accurate than humans (e.g. structural monitoring). Since the 1970s, several different types of robotic systems have been studied, explored, suggested, and developed for various construction-related applications. Though there exist quite a few numbers of such prototypes, robots are still not widely represented in the construction industry. This is possibly due to the highly unstructured and harsh environments of construction sites. Thus, there is still a strong need to investigate the potential of these systems. The overarching goal of this chapter is to briefly discuss the history of robotics, underlying concepts, and corresponding construction-domain specific applications. Specifically, the objectives are to a) develop a taxonomy for construction robots based on the work environment, b) discuss some of the key fundamental capabilities for construction robotic systems, and c) discuss in detail a built environment case study application for autonomous multi-sensor fused mobile robots.