The past decade has witnessed a notable transformation in the Architecture, Engineering and Construction (AEC) industry, with efforts made both in the academia and industry to facilitate improvement of efficiency, safety and sustainability in civil projects. Such advances have greatly contributed to a higher level of automation in the lifecycle management of civil assets within a digitalised environment. To integrate all the achievements delivered so far and further step up their progress, this study proposes a novel theory, Engineering Brain, by effectively adopting the Metaverse concept in the field of civil engineering. Specifically, the evolution of the Metaverse and its key supporting technologies are first reviewed; then, the Engineering Brain theory is presented, including its theoretical background, key components and their inter-connections. Outlooks of this theory’s implementation within the AEC sector are offered, as a description of the Metaverse of future engineering. Through a comparison between the proposed Engineering Brain theory and the Metaverse, their relationships are illustrated; and how Engineering Brain may function as the Metaverse for future engineering is further explored. Providing an innovative insight into the future engineering sector, this study can potentially guide the entire industry towards its new era based on the Metaverse environment.

Pavement markings constitute an effective way of conveying regulations and guidance to drivers. They constitute the most fundamental way to communicate with road users, thus, greatly contributing to ensuring safety and order on roads. However, due to the increasingly extensive traffic demand, pavement markings are subject to a series of deterioration issues (e.g., wear and tear). Markings in poor condition typically manifest as being blurred or even missing in certain places. The need for proper maintenance strategies on roadway markings, such as repainting, can only be determined based on a comprehensive understanding of their as-is worn condition. Given the fact that an efficient, automated and accurate approach to collect such condition information is lacking in practice, this study proposes a vision-based framework for pavement marking detection and condition assessment. A hybrid feature detector and a threshold-based method were used for line marking identification and classification. For each identified line marking, its worn/blurred severity level was then quantified in terms of worn percentage at a pixel level. The damage estimation results were compared to manual measurements for evaluation, indicating that the proposed method is capable of providing indicative knowledge about the as-is condition of pavement markings. This paper demonstrates the promising potential of computer vision in the infrastructure sector, in terms of implementing a wider range of managerial operations for roadway management.

Bridges experience regular inspections from transport agencies/authorities periodically. However, current approaches for the storage and visualization of inspection-related data are often restricted to spreadsheets and inspection reports and cannot sufficiently support open building information modeling (BIM) processes. This research proposes an industry foundation classes (IFC)-based method to document and represent inspection-related information in bridge BIM models. Specifically, the geometry of defects, including spatial placement and shape representation, was modeled parametrically. Interrelationships between defects and other IFC entities (e.g., bridge elements, root causes, and maintenance actions) were represented. The proposed method was validated on a concrete highway bridge, with two scenarios designed to illustrate its capability to support cause diagnosis and maintenance decision making. The interoperability of the proposed method across different BIM tools was evaluated as well. This work demonstrates the parametric-driven representation of defect information based on the latest IFC, facilitating an integrated BIM environment for the lifecycle management of civil assets.

Stay cable is the major load-carrying element in cable-stayed bridges. The process of monitoring cable forces would be beneficial to ensure the safety of bridges. The conventional sensor-based approaches to measure stay cable forces is complicated in operation, time-consuming and relatively expensive. In order to confront these disadvantages, a lightweight measurement method using smartphone imagery was proposed in this paper. The video data acquisition process was first standardized by using a pre-designed target. Then, a novel algorithm to extract the vibration displacement of stay cables under complex condition was developed. An automatic correction algorithm was provided to further improve the displacement results. On top of that, a smartphone-based software for determining cable forces was developed and tested on a real-life bridge. The results showed a maximum error of 1.99% compared with the cable force obtained by using a dynamic tester. The developed software is proven to be feasible in real-life projects and can achieve high accuracy in cable force determination. At the same time, the proposed method does not require a fixed camera for measurement and is not limited by personnel experience and measurement time, facilitating real-time monitoring of multiple projects, multiple cable surfaces and multiple personnel in a visual vibration environment.

Main cables, comprising a number of wire strands, constitute a vital element in long-span suspension bridges. The determination of their alignment during construction is of great importance, and relative sag is commonly measured for the efficient sag adjustment of general strands. The conventional approach uses the caterpillar method, which is inconvenient, difficult-to-implement, and potentially dangerous. In order to realize the high-precision measurement of cable alignment in a strong wind environment, a vision-based method for relative sag measurement of the general cable strands is proposed in this paper. In the proposed measurement system, images of pre-installed optical targets are collected and analyzed to realize the remote, automatic, and real-time measurement of the relative sag. The influences of wind-induced cable shaking and camera shaking on the accuracy of the height difference measurement are also theoretically analyzed. The results show that cable strand torsion and camera roll have a great impact on the measurement accuracy, while the impacts of the cable strand swing and vibration, camera swing and vibration, and camera pitch and yaw are insignificant. The vision-based measurement system tested in the field experiment also shows a measurement error within 3 mm, which meets the requirements for cable adjustment construction. At the same time, the vision-based measurement method proposed and validated in this paper can improve the measurement accuracy and efficiency of strand alignment in a strong wind environment. Potential risks involved in the manual measurement, e.g., working at heights and in strong wind environments, can be eliminated, facilitating the automation of the cable erection process.