Probing in situ capacities of prestressed stayed columns: towards a novel structural health monitoring technique.

Prestressed stayed columns (PSCs) are structural systems whose compressive load-carrying capacity is enhanced through pre-tensioned cable stays. Much research has demonstrated that PSCs buckle subcritically when their prestressing levels maximize the critical buckling load of the theoretically perfect arrangement. Erosion of the pre-tensioned cables' effectiveness (e.g. through creep or corrosion) can thus lead to sudden collapse. The present goal is to develop a structural health monitoring (SHM) technique for in-service PSCs that returns the current structural utilization factor based on selected probing measurements. Hence, PSCs with different cable erosion and varying compression levels are probed in the pre-buckling range within the numerical setting through a nonlinear finite element (FE) model. In contrast to the previous work, it is found presently that the initial lateral stiffness from probing a PSC provides a suitable health index for in-service structures. A machine learning-based surrogate is trained on simulated data of the loading factor, cable erosion and probing indices; it is then used as a predictive tool to return the current utilization factor for PSCs alongside the level of cable erosion given probing measurements, showing excellent accuracy and thus provides confidence that an SHM technique based on probing is indeed feasible. This article is part of the theme issue 'Probing and dynamics of shock sensitive shells'.

Experimental and numerical datasets for benchmark tests on high strength steel I-section frames.

This data article presents experimental and numerical datasets for eight fixed-base, single storey, unbraced high strength steel welded I-section frames subjected to in-plane horizontal and vertical loading. A detailed description of the full-scale frame testing programme is provided in the related research article 'Benchmark tests on high strength steel frames'. The experimental dataset can be used to steer future research in full-scale structural testing and provide benchmark results that are suitable for the validation of finite element models and the development of system-level design approaches. In addition to the benchmark experimental frame data, all necessary details and data for shell finite element (FE) model validation using geometrically and materially nonlinear analysis (GMNIA) is presented. The general purpose FE software Abaqus was used. The dataset can be used as an illustrative example of GMNIA validation in accordance with EN 1993-1-14 with all relevant data for reproducibility provided.

Data related to the manufacturing and mechanical performance of 3D-printed metal honeycombs.

The data available in this article include 3D mechanical designs used for the computer-aided fabrication of metal honeycombs produced by additive manufacturing and studied in [1]. In addition, the force-displacement data utilized to evaluate the mechanical performance of the metal used in this study are available via the digital image correlation technique. Further, the surface features obtained using 3D scanning microscopy of the fabricated parts are available as raw files and processed data. Finally, the impact test data are presented as high-frame-rate videos showing the time-displacement numerical values. This information has been provided in this data article to complement the related research, serve as a guide for future studies, and ensure the data's repeatability and reliability of the related research paper. The research article [1] investigates the mechanical performance and failure mechanism of additively manufactured metallic honeycombs under various scenarios, from quasi-static to dynamic loading. It also investigates the design optimization of these energy-absorbing hollow structures by comparing hollow structures made of three distinct novel cell designs (triangular, diamond-shaped, and diamond-shaped with curved walls) with traditional honeycombs made of hexagonal cells.