Structural health monitoring ensures the safety and longevity of structures like buildings and bridges. As the volume and scale of structures and the impact of their failure continue to grow, there is a dire need for SHM techniques that are scalable, inexpensive, can operate passively without human intervention, and are customized for each mechanical structure without the need for complex baseline models. We present a novel “deploy-and-forget” approach for automated detection and localization of damage in structures. It is a synergistic integration of entirely passive measurements from inexpensive sensors, data compression, and a mechanics-informed autoencoder. Once deployed, the model continuously learns and adapts a bespoke baseline model for each structure, learning from its undamaged state’s response characteristics. After learning from just 3 hours of data, it can autonomously detect and localize different types of unforeseen damage. Results from numerical simulations and experiments indicate that incorporating the mechanical characteristics into the autoencoder allows for up to a 35% improvement in the detection and localization of minor damage over a standard autoencoder. Our approach holds significant promise for reducing human intervention and inspection costs while enabling proactive and preventive maintenance strategies. This will extend the lifespan, reliability, and sustainability of civil infrastructures.

Energy harvesting from ambient vibrations in civil structures provides a potential permanent power source for sensor networks used in structural health monitoring. These structures exhibit a narrow, natural frequency-response range, which is generally one to two orders of magnitude lower than the operating frequency spectrum of most piezoelectric-scavenging devices. This considerably limits the levels of harvestable power and thus hinders the implementation of advanced sensing functionalities. In this paper, the improvement of the energy-harvesting characteristics of a bimorph cantilever lead zirconate titanate (PZT) piezoelectric beam is studied. The application of a variable prestress-loading condition is used as a solution to modify the system’s properties. A generalized model that takes into account all the vibration-mode shapes of the beam and the back-coupling effect is derived using the Hamiltonian principle. The model describes the effect of the prestress parameters on the harvestable ener...

Thanks to their efficiency enhancement systems based on post-buckled structural elements have been extensively used in many applications such as actuation, remote sensing and energy harvesting. The post-buckling snap-through behavior of bilaterally constrained beams has been exploited to create sensing or energy harvesting mechanisms for quasi-static applications. The conversion mechanism has been used to transform low-rate and low-frequency excitations into high-rate motions. Electric energy has been generated from such high-rate motions using piezoelectric transducers. However, lack of control over the post-buckling behavior severely limits the mechanism’s efficiency. This study aims to maximize the levels of harvestable power by controlling the location of snap-throughs along the beam at different buckling transitions. Since the snap-through location cannot be controlled by tuning the geometric properties of a uniform beam, non-uniform cross-sections are examined. An energy-based theoretical model is herein developed to predict the post-buckling response of non-prismatic beams. The total potential energy is minimized under constraints that represent the physical confinement of the beam between the lateral boundaries. The experimentally validated results show that changing the shape and geometric dimensions of non-uniform beams allows for the accurate controlling of the snap-through location at different buckling transitions. A 78.59% improvement in harvested energy levels has been achieved by optimization of beam shape.

Smart structures have attracted significant research attention in the last decade, mainly due to the capabilities of advanced concrete in electrical resistance-enabled self-sensing. In this study, we present a type of environment-friendly, self-sensing concrete enabled by electrical resistance. Environment-friendly, self-sensing concrete was casted with the additions of byproduct wastes (i.e., coal fly ash (FA), blast furnace slag (BOF) and red mud (RM)) at various volume fractions and cured using the conditions of 3, 7 and 28 days. The self-sensing concrete samples were experimentally tested to investigate the effects of the byproduct wastes on the mechanical and electrical properties (i.e., compressive strength and electrical resistance). In the end, parametric studies were experimentally conducted to investigate the influences of the byproduct wastes on the mechanical and electrical properties of the reported environment-friendly, self-sensing concrete.