No abstract

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.

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.

Throughout the life cycle of civil assets, construction, operation and maintenance phases require monitoring to assure reasonable decision makings. Current methods always involve speciallyassigned personnel conducting on-site inspections, which are work-intensive, time-consuming and errorprone. Computer vision, as a powerful alternative to manual inspection, has been extensively studied during the past decades. On the basis of existing summary papers, this paper reviews a wide range of literatures, including journal articles, conference proceedings and other resources. Current applications of computer vision during construction, operation and maintenance stages of civil structures are concluded, with a special focus on operation and maintenance phase. This review aims to provide a comprehensive insight about the utilization of computer vision in civil engineering and an inspiring guidance for future research.

To scrutinize the current application of building information modelling (BIM) and computational fluid dynamics (CFD) integration in research as well as industrial fields, the present study conducted a holistic review including a bibliometric exploration for existing articles, specific content analysis in different sectors, and follow-up qualitative discussion for the potential of this integrated technology. The bibliometric exploration is focused on analyzing main journals, keywords, and chronological change in representative research content by selecting 115 relevant studies. In content analysis, the representative integrated BIM and CFD application cases are divided into three different sectors. The functionality, interoperability, and sustainability of such integration in architecture, engineering, and construction (AEC) projects are described in detail. Furthermore, the future research based on the applications of BIM and CFD integration is discussed. Specifically, the more advanced hazard analysis is proposed reflecting the strength of such an integration. Comprehensive information for the possible hazards in AEC projects is digitized and quantified to make a more sensitive hazard recognition tool which can formalize reduction strategies and measures of potential hazards. As a result, the present review study contributes to relevant research by identifying representative application parts and practical requirements for BIM and CFD integration in whole design aspects, reviewing the current research trends and future direction in detail, and analyzing the major issues, such as an interoperability in BIM-compatible CFD for sustainable built environments.

No abstract

Over the preceding several decades, the architectural, engineering, and construction (AEC) industries have witnessed significant advances due to the rapid development of information technology [...]

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.