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1. Identification of prestress losses under early-age cracking in prefabricated prestressed slabs with complex cross-sections using long-gauge sensors

   https://doi.org/10.1177/14759217251362305  

   Liang, Yitian; Glisic, Branko (September 2025, Structural Health Monitoring)

   Off-site fabrication allows for efficient production and construction, while the prestressing process enhances the load-bearing capacity of structural components. Due to these advantages, the application of prestressed prefabricated structures increases significantly. However, various influences present at the early stage of fabrication, such as pouring conditions, friction with formworks, and early-age cracks, may cause differences between designed and real values of prestress forces, thereby affecting the bearing capacity and durability of prefabricated components. These differences are often reflected in the strain field. Therefore, it is of interest to monitor the performance of prefabricated structural components at early stages, that is., before, during, and after prestressing, by studying the internal strain distribution. This article aims at developing a methodology to identify prestress losses under early-age cracks in prefabricated prestressed beam-like concrete structures with a complex geometric cross-section and validating the application on a double-T slab of a five-floor garage at Princeton University. Embedded long-gauge strain sensors are used to monitor the strain at different locations. The focus of this article is on the analysis of the sensors embedded in the slab’s longitudinal direction (longitudinal sensors). The main challenges of this research include the non-linear strain distribution in the complex cross-section of the structures, which makes the Bernoulli hypothesis only partially valid, the uncertainties of geometric and mechanical parameters, and the effects of early-age crack opening on the evaluation of prestress forces. The developed methodology, based on the measurements of strain distribution before, during, and after prestressing, enabled the identification, that is, detection, localization, and quantification of prestress losses under early-age cracks in the prefabricated slab. The findings of this study have important implications for the design, construction, and maintenance of prefabricated structural components, enabling enhanced safety and durability throughout their service life. 

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2. On maintaining bistability of prestressed laminates after clamping

   https://doi.org/10.1016/j.compstruct.2024.118278  

   Boddapati, Karthik; Osorio, Juan C; Arrieta, Andres F (September 2024, Composite Structures)

   Bistable composite laminates exhibit a high degree of shape change and stiffness variation between their stable configurations, making them suitable for applications in morphing structures and energy harvesting. However, integration of these laminates into larger systems often imposes different boundary conditions, which can eliminate one of their stable states. Moreover, clamping one or more edges of a rectangular bistable laminate causes a drastic change in its strain energy landscape, indicating a strong interplay between the laminate geometry, boundary conditions, and prestress. In this work, we investigate the effect of clamping on the bistability of rectangular prestressed laminates. An analytical approach is proposed to examine the deflection decay imposed by the boundary condition along the laminate’s length. Different prestress values, laminate dimensions, and material properties are analyzed to establish their effect on the curvature change due to the localized clamp effect. A length criterion is determined to guarantee bistability after clamping the bistable laminate, suggesting the need to utilize complementary techniques to retain the bistable behavior for orthotropic prestressed laminates. Different strategies to counter the clamped edge effect and thereby retain the bistability of these types of laminates are then examined. The proposed analytical model is expanded to consider multi-section composite laminates, showing the role of the symmetric regions in bistability retention. Finally, the results from the model are validated against experiments. 

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3. Two-level systems and growth-induced metastability in hydrogenated amorphous silicon

   https://doi.org/10.1088/2053-1591/abb498  

   Molina-Ruiz, M.; Jacks, H. C.; Queen, D. R.; Wang, Q.; Crandall, R. S.; Hellman, F. (September 2020, Materials Research Express)

   Abstract Specific heat measurements from 2 to 300 K of hydrogenated amorphous silicon prepared by hot-wire chemical vapor deposition show a large excess specific heat at low temperature, significantly larger than the Debye specific heat calculated from the measured sound velocity. The as-prepared films have a Schottky anomaly that is associated with metastable hydrogen in the amorphous network, as well as large linear and excess cubic term commonly associated with tunneling two-level systems in amorphous solids. Annealing at 200 °C, a temperature that enables hydrogen mobility but not evaporation, irreversibly reduces the heat capacity, eliminating the Schottky anomaly and leaving a reduced linear heat capacity. A non-monotonic dependence on growth temperature and H content is observed in all effects, except for sound velocity, which suggests that the tunneling two-level systems and the Schottky anomaly are associated with atomic hydrogen and require low density regions to form, while sound velocity is associated with the silicon network and increases with increasing growth temperature. 

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4. Predicting failure in disordered solids from structural metrics

   https://doi.org/10.1103/PhysRevMaterials.00.003600  

   Richard, D; Ozawa, M; Patinet, S; Stanifer, E; Shang, B; Ridout, S; Xu, B; Zhang, G; Morse, P; Barrat, J-L; et al (November 2020, Physical review materials)

   null (Ed.)

   Amorphous solids lack long-range order. Therefore identifying structural defects—akin to dislocations in crystalline solids—that carry plastic flow in these systems remains a daunting challenge. By comparing many different structural indicators in computational models of glasses, under a variety of conditions we carefully assess which of these indicators are able to robustly identify the structural defects responsible for plastic flow in amorphous solids. We further demonstrate that the density of defects changes as a function of material preparation and strain in a manner that is highly correlated with the macroscopic material response. Our work represents an important step towards predicting how and when an amorphous solid will fail from its microscopic structure. 

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5. Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity

   Oktay, Deniz; Mirramezani, Mehran; Medina, Eder; Adams, Ryan P. (April 2023, International Conference on Learning Representations (ICLR))

   Intelligent biological systems are characterized by their embodiment in a complex environment and the intimate interplay between their nervous systems and the nonlinear mechanical properties of their bodies. This coordination, in which the dynamics of the motor system co-evolved to reduce the computational burden on the brain, is referred to as "mechanical intelligence" or "morphological computation". In this work, we seek to develop machine learning analogs of this process, in which we jointly learn the morphology of complex nonlinear elastic solids along with a deep neural network to control it. By using a specialized differentiable simulator of elastic mechanics coupled to conventional deep learning architectures---which we refer to as neuromechanical autoencoders---we are able to learn to perform morphological computation via gradient descent. Key to our approach is the use of mechanical metamaterials---cellular solids, in particular---as the morphological substrate. Just as deep neural networks provide flexible and massively-parametric function approximators for perceptual and control tasks, cellular solid metamaterials are promising as a rich and learnable space for approximating a variety of actuation tasks. In this work we take advantage of these complementary computational concepts to co-design materials and neural network controls to achieve nonintuitive mechanical behavior. We demonstrate in simulation how it is possible to achieve translation, rotation, and shape matching, as well as a "digital MNIST" task. We additionally manufacture and evaluate one of the designs to verify its real-world behavior. 

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