Conventional damage detection techniques are gradually being replaced by state-of-the-art smart monitoring and decision-making solutions. Near real-time and online damage assessment in structural health monitoring (SHM) systems is a promising transition toward bridging the gaps between the past’s applicative inefficiencies and the emerging technologies of the future. In the age of the smart city, Internet of Things (IoT), and big data analytics, the complex nature of data-driven civil infrastructures monitoring frameworks has not been fully matured. Machine learning (ML) algorithms are thus providing the necessary tools to augment the capabilities of SHM systems and provide intelligent solutions for the challenges of the past. This article aims to clarify and review the ML frontiers involved in modern SHM systems. A detailed analysis of the ML pipelines is provided, and the in-demand methods and algorithms are summarized in augmentative tables and figures. Connecting the ubiquitous sensing and big data processing of critical information in infrastructures through the IoT paradigm is the future of SHM systems. In line with these digital advancements, considering the next-generation SHM and ML combinations, recent breakthroughs in (1) mobile device-assisted, (2) unmanned aerial vehicles, (3) virtual/augmented reality, and (4) digital twins are discussed at length. Finally, the current and future challenges and open research issues in SHM-ML conjunction are examined. The roadmap of utilizing emerging technologies within ML-engaged SHM is still in its infancy; thus, the article offers an outlook on the future of monitoring systems in assessing civil infrastructure integrity.

This article investigates the shear behavior of deep concrete beams reinforced with glass fiber reinforced polymer for flexure and without shear reinforcement. A total of 13 beams were tested under four-point loading until failure. Nine of which reinforced with glass fiber reinforced polymer bars and four with steel bars. The ultimate shear capacity along with the load–deformation relationship of all beams was studied. The effects of the shear span to depth ratio a/d, reinforcement ratio ρ, beam effective depth d, and concrete compressive strength on the ultimate shear capacity and mode of failure of all beams were also investigated. Results show that the stiffness of steel-reinforced concrete beams (slope of the ascending portion of load–deflection curve) is higher than that of beams reinforced with fiber-reinforced polymer bars as expected, due to the low axial stiffness of the fiber-reinforced polymer material. A slight variation in the ultimate shear capacity was noticed but no clear trend was observed. In addition, beams reinforced with fiber-reinforced polymer exhibited larger deformation at their ultimate failure load, after which a sudden failure occurred especially for beams having high shear capacity.

Engineers, building officials and authorities, developers, and other stakeholders have long recognized the significance of structural health monitoring (SHM) in civil infrastructure. With an increasing number of large structures and modern, complex design, the need for high-performance SHM systems is unprecedented. Structural performance assessment, damage detection, and continuous monitoring are among the current SHM applications. However, all SHM applications face challenges and limitations in several aspects. This paper presents a state-of-the-art review of such challenges and limitations, and sheds light on some future research needs and directions. The most widely used frameworks and their respective limitations are presented and discussed with a focus on real-life applications pertaining to large structures. Driven by many recent technological advancements, SHM systems are shifting away from conventional, contact-based sensors in favor of more efficient, non-contact-based sensors. This is not intended to be an exhaustive review, but rather a stepping-stone and pathway to a solid understanding of the SHM field and its current intricacies pertaining to large structures. Naturally, some of the highlighted issues can be extended to all types of structures.