A vision-based approach has been employed in Structural Health Monitoring (SHM) of bridge infrastructure. The approach has many advantages: non-contact, non-destructive, long-distance, high precision, immunity from electromagnetic interference, and multiple-target monitoring. This review aims to summarise the vision- and Digital Image Correlation (DIC)-based SHM methods for bridge infrastructure because of their strategic significance and security concerns. Four different bridge types were studied: concrete, suspension, masonry, and steel bridge. DIC applications in SHM have recently garnered attention in aiding to assess the bridges’ structural response mechanisms under loading. Different non-destructive diagnostics methods for SHM in civil infrastructure have been used; however, vision-based techniques like DIC were only developed over the last two decades, intending to facilitate damage detection in bridge systems with prompt and accurate data for efficient and sustainable operation of the bridge structure throughout its service life. Research works reviewed in this article demonstrated the DIC capability to detect damage such as cracks, spalling, and structural parameters such as deformation, strains, vibration, deflection, and rotation. In addition, the reviewed works indicated that the DIC as an efficient and reliable technique could provide sustainable monitoring solutions for different bridge infrastructures.

Road networks are considered as one of the most important transport infrastructure systems, since they attain the economic and social prosperity of modern societies. For this reason, it is vital to improve the resiliency of road networks in order to function normally under daily stressors and recover quickly after natural disasters such as an earthquake event. In the last decades, vulnerability assessment studies for road networks and their assets gained great attention among the research community. This literature review includes a brief introduction about seismic vulnerability assessment, followed by the roadway assets damage and their damage states, and then the main typologies for the vulnerability assessment of roadway assets. Moreover, it focuses on available assessment methods, which were proposed to quantify the vulnerability of road networks and its assets. These methods are divided into two main categories, physical and traffic-based approaches. Methods based on fragility functions and vulnerability indexes were investigated in physical approach for roadways and its assets. On the other hand, accessibility and link importance index were explored in traffic-based approach for road networks. This paper reviews and comments the most common vulnerability assessment methods for road networks and its assets and points out their advantages and disadvantages. The main gaps and needs are identified and recommendations for future studies are provided.

Rapid visual screening is a quick and simple approach often used by researchers to estimate the seismic vulnerability of buildings in an area. In this study, preliminary seismic vulnerability assessment of 500 buildings situated at Northern and Eastern George Town, Malaysia, was carried out by utilizing a modified FEMA-154 (2002) method that suits Malaysian conditions. Data were collected from online sources via Google Maps and Google Earth instead of traditional surveying data collection through street screening. The seismic assessment analysis of this study was based on the RVS performance score and the damage state classification for each building typology. This approach generates, for each building, a final performance score based on governing parameters such as structural resisting system, height, structural irregularities, building age, and soil type. The findings revealed the immediate need for effective seismic mitigation strategies, as 90% of the studied buildings required a further detailed analyses to pinpoint their exact seismic vulnerability performance. Most of the surveyed buildings were predicted to experience moderate-to-substantial damage, with 220 out of 500 being classed as damage state 2 (D2) and damage state 3 (D3). A GIS map, “RVS Malaysian Form-George Town Area”, was generated via ArcGIS and shared with the public to provide vital information for further research.