Bridge Resilience Assessment with INSPIRE Data

This project proposed a methodology to assess the impact of corrosion on the performance of bridges. The combined analytical and numerical modeling of shear-critical and lap-spliced columns is detailed, and outcomes are verified with previous experimental test data. The impact of corrosion on risk is assessed through conducting fragility analyses. Results quantify the increase in failure probabilities of these structures, measured by increasing probabilities of exceeding defined damage states, with increasing levels of corrosion. Corrosion is found to have a larger impact on increasing probabilities of exceeding more severe damage states. Twenty percent mass loss of reinforcement increases the probability of exceeding the complete damage state by up to 49% and 34% for a shear-critical and lap-spliced column, respectively. The effect is more pronounced at intermediate loading intensities, where there is uncertainty about the performance of the structure. Comparing between failure modes, bridges with columns of short lap splice are more vulnerable to collapse under the same degree of corrosion compared with shear-critical columns

Updating Bridge Resilience Assessment based on Corrosion and Foundation Scour Inspection Data

Aging and degradation of bridge structural components due to corrosion and scour create severe safety issues in the structural system and can lead to possible bridge failures. Collecting and analyzing inspection data provide a way to monitor and assess the safety condition of bridges. This paper proposes a framework to utilize collected inspection data to assess the condition of a bridge through updating both component- and system-level fragility curves of the bridge. Particularly, collected data such as mass loss of reinforcement and depth of scour hole are utilized to update the mechanical properties of structural members in the finite element model. Fragility curves are then updated through performing a series of nonlinear time analyses based on the inspection data. As bridges age, they are susceptible to increasing corrosion and scour. This study investigates the performance of bridges considering the combined effect of reinforcement corrosion and foundation scour under extreme loadings such as seismic events to assess bridge resilience. Fragility results quantify increases in the probabilities of damage and collapse of the structural system as measured mass loss and scour depth increase

Bridge Resilience Assessment with INSPIRE Data

In this research, we proposed analytical models validated with numerical results to assess how inspection-collected data on corrosion can be used to update bridge models and predict performance under future events. A detailed investigation of the vulnerability of bridges due to corrosion at both component and global levels. The results show the impact of corrosion-induced degradation on the seismic fragility of reinforced concrete columns under various failure modes, e.g., flexural, shear, and lap-spliced failures. As increasing amounts of data are collected on the states of bridges, the results show how these data can be used to update bridge assessments and prioritize decisions for repair and retrofit to increase component and system performance

NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search

Graph neural architecture search (GraphNAS) has recently aroused considerable attention in both academia and industry. However, two key challenges seriously hinder the further research of GraphNAS. First, since there is no consensus for the experimental setting, the empirical results in different research papers are often not comparable and even not reproducible, leading to unfair comparisons. Secondly, GraphNAS often needs extensive computations, which makes it highly inefficient and inaccessible to researchers without access to large-scale computation. To solve these challenges, we propose NAS-Bench-Graph, a tailored benchmark that supports unified, reproducible, and efficient evaluations for GraphNAS. Specifically, we construct a unified, expressive yet compact search space, covering 26,206 unique graph neural network (GNN) architectures and propose a principled evaluation protocol. To avoid unnecessary repetitive training, we have trained and evaluated all of these architectures on nine representative graph datasets, recording detailed metrics including train, validation, and test performance in each epoch, the latency, the number of parameters, etc. Based on our proposed benchmark, the performance of GNN architectures can be directly obtained by a look-up table without any further computation, which enables fair, fully reproducible, and efficient comparisons. To demonstrate its usage, we make in-depth analyses of our proposed NAS-Bench-Graph, revealing several interesting findings for GraphNAS. We also showcase how the benchmark can be easily compatible with GraphNAS open libraries such as AutoGL and NNI. To the best of our knowledge, our work is the first benchmark for graph neural architecture search

Disentangled Representation Learning with Large Language Models for Text-Attributed Graphs

Text-attributed graphs (TAGs) are prevalent on the web and research over TAGs such as citation networks, e-commerce networks and social networks has attracted considerable attention in the web community. Recently, large language models (LLMs) have demonstrated exceptional capabilities across a wide range of tasks. However, the existing works focus on harnessing the potential of LLMs solely relying on prompts to convey graph structure information to LLMs, thus suffering from insufficient understanding of the complex structural relationships within TAGs. To address this problem, in this paper we present the Disentangled Graph-Text Learner (DGTL) model, which is able to enhance the reasoning and predicting capabilities of LLMs for TAGs. Our proposed DGTL model incorporates graph structure information through tailored disentangled graph neural network (GNN) layers, enabling LLMs to capture the intricate relationships hidden in text-attributed graphs from multiple structural factors. Furthermore, DGTL operates with frozen pre-trained LLMs, reducing computational costs and allowing much more flexibility in combining with different LLM models. Experimental evaluations demonstrate the effectiveness of the proposed DGTL model on achieving superior or comparable performance over state-of-the-art baselines. Additionally, we also demonstrate that our DGTL model can offer natural language explanations for predictions, thereby significantly enhancing model interpretability

Graph Meets LLMs: Towards Large Graph Models

Large models have emerged as the most recent groundbreaking achievements in artificial intelligence, and particularly machine learning. However, when it comes to graphs, large models have not achieved the same level of success as in other fields, such as natural language processing and computer vision. In order to promote applying large models for graphs forward, we present a perspective paper to discuss the challenges and opportunities associated with developing large graph models. First, we discuss the desired characteristics of large graph models. Then, we present detailed discussions from three key perspectives: representation basis, graph data, and graph models. In each category, we provide a brief overview of recent advances and highlight the remaining challenges together with our visions. Finally, we discuss valuable applications of large graph models. We believe this perspective can encourage further investigations into large graph models, ultimately pushing us one step closer towards artificial general intelligence (AGI). We are the first to comprehensively study large graph models, to the best of our knowledge.Comment: Accepted by NeurIPS 2023 New Frontiers in Graph Learning Workshop. Comments are welcom

Short-term power load forecasting method based on CNN-SAEDN-Res

In deep learning, the load data with non-temporal factors are difficult to process by sequence models. This problem results in insufficient precision of the prediction. Therefore, a short-term load forecasting method based on convolutional neural network (CNN), self-attention encoder-decoder network (SAEDN) and residual-refinement (Res) is proposed. In this method, feature extraction module is composed of a two-dimensional convolutional neural network, which is used to mine the local correlation between data and obtain high-dimensional data features. The initial load fore-casting module consists of a self-attention encoder-decoder network and a feedforward neural network (FFN). The module utilizes self-attention mechanisms to encode high-dimensional features. This operation can obtain the global correlation between data. Therefore, the model is able to retain important information based on the coupling relationship between the data in data mixed with non-time series factors. Then, self-attention decoding is per-formed and the feedforward neural network is used to regression initial load. This paper introduces the residual mechanism to build the load optimization module. The module generates residual load values to optimize the initial load. The simulation results show that the proposed load forecasting method has advantages in terms of prediction accuracy and prediction stability.Comment: in Chinese language, Accepted by Electric Power Automation Equipmen