Search for: All records

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. 

Abstract Structural health monitoring (SHM) is the automation of the condition assessment process of an engineered system. When applied to geometrically large components or structures, such as those found in civil and aerospace infrastructure and systems, a critical challenge is in designing the sensing solution that could yield actionable information. This is a difficult task to conduct cost-effectively, because of the large surfaces under consideration and the localized nature of typical defects and damages. There have been significant research efforts in empowering conventional measurement technologies for applications to SHM in order to improve performance of the condition assessment process. Yet, the field implementation of these SHM solutions is still in its infancy, attributable to various economic and technical challenges. The objective of this Roadmap publication is to discuss modern measurement technologies that were developed for SHM purposes, along with their associated challenges and opportunities, and to provide a path to research and development efforts that could yield impactful field applications. The Roadmap is organized into four sections: distributed embedded sensing systems, distributed surface sensing systems, multifunctional materials, and remote sensing. Recognizing that many measurement technologies may overlap between sections, we define distributed sensing solutions as those that involve or imply the utilization of numbers of sensors geometrically organized within (embedded) or over (surface) the monitored component or system. Multi-functional materials are sensing solutions that combine multiple capabilities, for example those also serving structural functions. Remote sensing are solutions that are contactless, for example cell phones, drones, and satellites. It also includes the notion of remotely controlled robots. 

Concrete exhibits time-dependent long-term behavior driven by creep and shrinkage. These rheological effects are difficult to predict due to their stochastic nature and dependence on loading history. Existing empirical models used to predict rheological effects are fitted to databases composed largely of laboratory tests of limited time span and that do not capture differential rheological effects. A numerical model is typically required for application of empirical constitutive models to real structures. Notwithstanding this, the optimal parameters for the laboratory databases are not necessarily ideal for a specific structure. Data-driven approaches using structural health monitoring data have shown promise towards accurate prediction of long-term time-dependent behavior in concrete structures, but current approaches require different model parameters for each sensor and do not leverage geometry and loading. In this work, a physics-informed data-driven approach for long-term prediction of 2D normal strain field in prestressed concrete structures is introduced. The method employs a simplified analytical model of the structure, a data-driven model for prediction of the temperature field, and embedding of neural networks into rheological time-functions. In contrast to previous approaches, the model is trained on multiple sensors at once and enables the estimation of the strain evolution at any point of interest in the longitudinal section of the structure, capturing differential rheological effects.