Current Issue : April-June Volume : 2024 Issue Number : 2 Articles : 5 Articles
This paper presents a structural health monitoring method based on artificial neural networks (ANNs) capable of detecting, locating, and quantifying damage in a single stage. The proposed framework employs a supervised neural network model that uses input factors calculated by modal parameters (natural frequencies or mode shapes), and output factors that represent the damage situation of elements or regions in a structural system. Unlike many papers in the literature that test damage detection methods only in numerical examples or simple experimental tests, this work also assesses the presented method in a real structure showing that it has potential for applications in real practical situations. Three different cases are evaluated through the methodology: numerical simulations, an experimental lab structure, and a real bridge. Initially, a cantilever beam and a 10-bar truss were numerically analyzed under ambient vibrations with different damage scenarios and noise levels. Afterward, the method is assessed in an experimental beam structure and in the Z24 bridge benchmark. The numerical simulations showed that the methodology is promising for identifying, locating, and quantifying single and multiple damages in a single stage, even with noise in the acceleration signals and changes in the first vibration mode of 0.015%. In addition, the Z24 bridge study confirmed that the damage detection method can localize damage in real civil structures considering only natural frequencies in the input factors, despite a mean difference of 4.08% between the frequencies in the healthy and damaged conditions....
Cylindrical hydraulic dampers used to reduce impacts and vibrations typically have linear strokes. In this study, a new arc-shaped stroke-type origami hydraulic damper with a nonlinear damping performance was proposed. By examining the damping effect of the origami hydraulic damper, the damping force was found to be proportional to the square of the motion velocity. A nonlinear dynamics governing equation was established using the derived formula for the damping force of the origami hydraulic damper, and a numerical analysis using the Runge–Kutta method was established. An impact test device with an arc-shaped stroke was developed, and the error between the numerical analysis value of the impact displacement and the measured experimental value was confirmed to be sufficiently small. An impact verification experiment confirmed that the damping effect of the origami hydraulic damper increases with the input energy of the impact. By varying the diameter of the orifice hole, which is an important design factor for an origami hydraulic damper, the damping effect of the origami hydraulic damper was found to increase as the diameter of the orifice hole decreased. To examine the effect of the type of hydraulic oil inside the origami hydraulic damper, water and edible oil were used to conduct impact verification experiments, and it was found that the effect on the impact damping effect was relatively small....
This article is devoted to the use of the polymer K-9 reagent in porous claydite-ash concrete and the design of its optimal composition according to the general design method of the optimal composition of the general theory of artificial building conglomerates (ISC). The data of experiments confirming the positive effect of the polymer reagent on increasing the durability, improving the moisture and heat engineering modes of porous concrete are presented....
Offshore wind turbines play a significant role in the expansion of clean and renewable energy. However, their exposure to harsh marine environments and dynamic loading conditions poses significant challenges to their structural integrity. In particular, the grouted connection, serving as the crucial interface between the monopile and the transition piece, is susceptible to cracking and particle washout that can lead to destabilizing grout erosion over time. In this paper, we propose a microwave structural health monitoring (SHM) approach for damage detection in grouted connections based on a stepped-frequency continuous wave radar. The methodology exploits ultra-wideband (UWB) electromagnetic wave propagation in the frequency range from 100MHz to 2 GHz, where the microwaves propagate along the concrete-type dielectric material guided by the surrounding steel cylinders. For the proof of concept, a scaled laboratory demonstrator was built that realistically models the dynamic loading experienced by a full-scale monopile. The structure was equipped with an UWB radar system using two transmitting and three receiving antennas directly coupled to the grout. For validation, a large number of other sensors, i.e., accelerometers, strain gauges, and acoustic emission sensors have also been installed and measured synchronously during the fatigue test. It is demonstrated here that the proposed SHM methodology offers a nondestructive and real-time method for assessing the structural integrity of the grouted connection directly, actively, and automatically. This has the potential to support predictive maintenance activities in the future....
Representing crystal structures of materials to facilitate determining them via neural networks is crucial for enabling machine-learning applications involving crystal structure estimation. Among these applications, the inverse design of materials can contribute to explore materials with desired properties without relying on luck or serendipity. Here, we propose neural structure fields (NeSF) as an accurate and practical approach for representing crystal structures using neural networks. Inspired by the concepts of vector fields in physics and implicit neural representations in computer vision, the proposed NeSF considers a crystal structure as a continuous field rather than as a discrete set of atoms. Unlike existing gridbased discretized spatial representations, the NeSF overcomes the tradeoff between spatial resolution and computational complexity and can represent any crystal structure. We propose an autoencoder of crystal structures that can recover various crystal structures, such as those of perovskite structure materials and cuprate superconductors. Extensive quantitative results demonstrate the superior performance of the NeSF compared with the existing gridbased approach....
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