Comparative Study of Streamers＇ Characteristics in Different Seed Based Insulating Oils under Lightning Impulse Voltages

This paper is aimed at the streamers in natural esters （vegetable oils） in point--plane electrode arrangement under lightning impulse voltage. The shape, stopping length, velocity and current of streamers are investigated. Six untreated commercial oils extracted from grape seeds, sunflower and rape seeds, corn, rice and sesame that could constitute potential liquids for high voltage applications are tested. A naphthenic mineral oil is also tested for comparison. It＇s shown that the streamers are filamentary for both polarities. For a given voltage, the stopping lengths （Lf） of streamers are longer when the point is positive than when it is negative; also, except mineral oil when the point is negative, the values of Lf-are very close in all tested oils. The streamers＇ velocities are in the same range for all vegetable oils and they vary between 0.4 km/s and 1.2 km/s for positive polarity and 0.2 km/s and 0.8 km/s for negative polarity.

Forecasting measured responses of structures using temporal deep learning and dual attention

The objective of this study is to develop a novel and efficient model for forecasting the nonlinear behavior of structures in response to time-varying random excitation.The key idea is to design a deep learning architecture to leverage the relationships,between external excitations and structure's vibration signals,and between historical values and future values,within multiple time-series data.The proposed method consists of two main steps:the first step applies a global attention mechanism to combine multiple-measured time series and time-varying excitation into a weighted time series before feeding it to a temporal architecture;the second step utilizes a self-attention mechanism followed by a fully connected layer to predict multi-step future values.The viability of the proposed method is demonstrated via two case studies involving synthetic data from a three-dimensional(3D)reinforced concrete structure and experimental data from an 18-story steel frame.Furthermore,comparison and robustness studies are carried out,showing that the proposed method outperforms conventional methods and maintains high performance in the presence of noise with an amplitude of less than 10%.

Development of an automatic and knowledge-infused framework for structural health monitoring based on prompt engineering

Collecting and analyzing vibration signals from structures under time-varying excitations is a non-destructive structural health monitoring approach that can provide meaningful information about the structures’safety without interrupting their normal operations.This paper develops a novel framework using prompt engineering for seamlessly integrating users’domain knowledge about vibration signals with the advanced inference ability of well-trained large language models(LLMs)to accurately identify the actual states of structures.The proposed framework involves formulating collected data into a standardized form,utilizing various prompts to gain useful insights into the dynamic characteristics of vibration signals,and implementing an in-house program with the help of LLMs to perform damage detection.The advantages,as well as limitations,of the proposed method are qualitatively and quantitatively assessed through two realistic case studies from literature,demonstrating that the present method is a new way to quickly construct practical and reliable structural health monitoring applications without requiring advanced programming/mathematical skills or obscure specialized programs.