Quantum computing is expected to offer solutions to computational problems that are otherwise computationally intractable. Although the core technology is still being developed, quantum-inspired computing already has been offering practical advantages for several computationally challenging problems. Despite the promising potential of both quantum computing and quantum-inspired computing, applications in civil engineering remain underexplored. This study aims to lay the foundation for future adoption by introducing the fundamental principles of quantum computing and quantum-inspired computing and by conducting a multivocal literature review. The review provides insights into the current research landscape in civil engineering and offers a detailed analysis of potential use cases and application areas. The findings are expected to serve as a foundation for guiding future research endeavors and practical deployments of quantum computing and quantum-inspired computing in civil engineering, as these technologies continue to mature. • Introduction to quantum and quantum-inspired computing (QC & QiC). • Identifying the pros and cons of QC & QiC given civil engineering (CE) requirements. • Presenting a multivocal literature review on QC & QiC for CE. • Recommending high-potential use cases and QC & QiC approaches in CE • Identifying research topics to advance QC & QiC in CE for real-world impact.

Mobile robots are increasingly being adopted for automated building inspections to improve safety, inspection accuracy, and operational efficiency. Global path planning (GPP) is required for automated robotic inspections and usually performed on prerecorded maps generated by robots. In the construction industry, building information modeling (BIM) is ubiquitous, and BIM models can be used for GPP, eliminating the need for prerecording maps and leveraging geometric and semantic information stored in BIM models. Due to their inherent complexity, BIM models are not directly applicable to state-of-the-art path planning algorithms. Typically, navigation models that filter and structure the vast amount of information relevant to navigation are derived from BIM models to perform GPP. Current research addressing the GPP problem using BIM models is mostly performed across single floors, may provide suboptimal paths, may suffer from long computation times, and may not consider the size of robots for collision checking. This paper presents a two-level GPP framework, termed “GPP-BIM” that leverages BIM models for enhanced robot navigation. Level 1 uses BIM to generate topological maps, while level 2 creates occupancy maps for each room. The two-level approach combines the efficiency and semantic integration of topological map planning with the detailed geometric considerations of occupancy maps, such as accommodating robot dimensions and detecting obstacles. The GPP-BIM framework is implemented in the Robot Operating System (ROS) 2 and uses the Industry Foundation Classes (IFC) open standard of BIM models. The GPP-BIM framework is validated on multiple IFC-based BIM models. The results of the validation tests show that GPP-BIM enables fast planning of obstacle-free and near-optimal paths in BIM environments. The GPP-BIM code is available as open-source software. • Developed a framework for efficient global path planning in BIM models. • Topological and occupancy maps retain semantic and geometric BIM information. • Two-level path planning approach reduces planning time and preserves path optimality. • Extensive validation on a variety of IFC-based BIM models. • Implemented within the Nav2 framework in ROS2, the code is provided as open source.

Mobile robots deployed to automate tasks in the construction industry require accurate robot localization and navigation. Building information modeling (BIM) is increasingly prevalent, and BIM models are interpretable by robots to help navigate in or around buildings. Most approaches towards robot localization through BIM models solely rely on maps derived from the BIM models requiring models with a high level of development (LOD) and the accurate modeling of non-structural objects. In practice, however, BIM models often employ a limited LOD and non-structural objects, if modeled, may appear in different locations, which may result in scan-BIM deviations and thus in localization errors . This paper presents the so called “LIO-BIM” framework, which couples lidar inertial odometry (LIO) and BIM for robust mobile robot localization and mapping, using 3D lidar to overcome the issues related to scan-BIM deviations. LIO-BIM builds upon simultaneous localization and mapping techniques and performs scan matching multiple times, i.e. (i) scan matching of the latest lidar scan with lidar scans previously recorded to maintain an accurate map of the environment, and (ii) scan matching of a local map around the robot with a BIM model to enable localization and mapping relative to the BIM model. The maps may be used at run-time, e.g., for construction progress monitoring or quality inspection. The framework, whose code is provided as open source, is implemented on a quadruped robot equipped with a 3D lidar, an inertial measurement unit , and a camera, and it is validated in a cluttered indoor office environment represented by a BIM model. Furthermore, the framework is validated on the ConSLAM dataset showcasing a cluttered construction site environment. As a result, the validation tests demonstrate accurate and robust 3D localization and mapping aligned with BIM models in real-time.

The pervasive emergence of sensing technologies for structural health monitoring (SHM) and the digitalization ubiquitous in engineering (“Industry 4.0”) pose increasing demands on information modeling concepts in civil engineering . While in building information modeling (BIM) conventional building information (such as geometry, material, or cost) can precisely be described using current modeling standards, information about SHM systems , referred to as “monitoring-related information”, cannot be fully described on a well-defined, formal basis. In this paper, a BIM-based approach towards describing monitoring-related information is proposed, using the Industry Foundation Classes (IFC), an open BIM standard facilitating the interoperability of BIM models, as a formal basis. First, possibilities and constraints of describing monitoring-related information with the IFC schema are discussed. Then, information necessary to describe SHM systems is integrated into a semantic model serving as a technology-independent metamodel. Next, the IFC schema is extended to enable BIM-based descriptions of SHM systems in compliance with IFC modeling capabilities, which is referred to as “IFC Monitor” schema. The IFC Monitor schema is verified with test software used in the official IFC certification program . For validation, a prototype SHM system is formally described using the IFC Monitor schema. The validation aims at checking if the IFC Monitor schema is capable of precisely describing monitoring-related information. As will be shown in this paper, the description of the prototype SHM system meets the requirements of a well-defined IFC model as specified in the official IFC certification program . As a result, the IFC Monitor schema proposed in this study advances BIM-based descriptions of SHM systems in association with structural systems being monitored on a well-defined, formal basis.