Shuyi Xie

China National Petroleum Corporation (China)

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Yang Mo

China University of Petroleum, Beijing

Lianxin Jiang

Guangxi Research Institute of Chemical Industry

Jian Ma

Tianjin University

Jianping Shen

Western Michigan University

Bohong Wang

Zhejiang Ocean University

Haiqing Yin

University of Science and Technology Beijing

Lifeng Li

Institute of Physics

Haiqin Yang

Dingxi City People's Hospital

Zengqiang Zhang

Northwest A&F University

Yinuo Chen

University of Science and Technology Beijing

Cooperative Institutions

China University of Petroleum, Beijing

University of Science and Technology Beijing

China National Petroleum Corporation (China)

Ping An (China)

Chinese Academy of Sciences

China University of Petroleum, East China

Tsinghua University

South China Agricultural University

Northwestern Polytechnical University

Ministry of Agriculture and Rural Affairs

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Deformation and Stress Prediction of Tank under Uneven Settlement

Journal of Physics Conference Series (2024)

Gang Wu Shuyi Xie Huiping Zhou Jinheng Luo Yan Long

Abstract The differential settlement of crude oil tank foundations can lead to deformation and stress concentration, posing significant risks to the safe operation of storage tanks. This study developed a finite element model for a large crude oil storage tank subjected to harmonic settlement to address the issue of non-uniform settlement around the tank. The model systematically varied the tank’s diameter-to-thickness ratio, height-to-diameter ratio, and the wind beam’s relative bending stiffness. The research examined the impact of these three factors on the maximum dimensionless radial displacement at the tank top and the maximum equivalent stress at the tank bottom. A predictive method was proposed for estimating the radial displacement at the tank top and the equivalent stress at the tank bottom under uneven settlement, offering valuable insights for assessing the integrity of large tanks in such conditions.

Settlement (finance)

10.1088/1742-6596/2860/1/012031

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Discovery of potential risks for the gas transmission station using monitoring data and the OOBN method

Reliability Engineering & System Safety (2022)

Yinuo Chen Zhigang Tian Rui He Yifei Wang Shuyi Xie

Identification

CUSUM

10.1016/j.ress.2022.109084

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Citations (9)

A Hybrid Approach of Deep Semantic Matching and Deep Rank for Context Aware Question Answer System

International Conference on Computational Linguistics (2019)

Shuyi Xie Chia‐Hao Chang Zhi Zhang Yang Mo Lianxin Jiang

Rank (graph theory)

Source

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Intelligent Methods for Pipeline Operation and Integrity

Journal of Pipeline Systems Engineering and Practice (2023)

Yufeng Yang Qiang Zhang Xixiang Zhang Shuyi Xie Gang Wu

As an important part of the energy transportation system, oil and gas pipelines are developing toward intelligence and digitalization. Vigorously developing and constructing pipeline networks based on intelligent methods such as big data and neural networks will help improve the efficiency of operation and management. This work provides an overview of recent developments of intelligent methods, including machine learning approaches, heuristic algorithms, and mathematical programming, used in the pipeline industry that can provide beneficial support for various applications enhancing operation and maintenance. Several aspects such as operating condition recognition, safety monitoring, pipeline remaining life prediction, and fault detection are reviewed and discussed. The study shows the need to focus on improving management level and ensuring pipeline safety.

Condition Monitoring

10.1061/jpsea2.pseng-1490

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Corrosion Cracking Causes in 13Cr-110 Tubing in Oil and Gas Extraction and Transportation

Energies (2025)

Kai Xu Shuyi Xie Jinheng Luo Bohong Wang

With the continuous development of oil and gas fields, the demand for corrosion-resistant tubing is increasing, which is important for the safe exploitation of oil and gas energy. Due to its excellent CO2 corrosion resistance, 13Cr-110 martensitic stainless steel is widely used in sour gas-containing oil fields in western China. This paper describes a case of stress corrosion cracking (SCC) in a 13Cr-110 serviced in an ultra-deep gas well. The failure mode of the tubing is brittle along the lattice fracture, and the cracks are generated because of nitrogen gas-lift production-enhancement activities during the service of the tubing, leading to corrosion damage zones and cracks in the 13Cr-110 material under the synergistic effect of oxygen and chloric acid-containing environments. During subsequent production, the tubing is subjected to tensile stresses and cracks expanded at the 13Cr-110 lattice boundaries due to reduced structural strength in the corrosion region. To address the corrosion sensitivity of 13Cr-110 in an oxygen environment, it is recommended that the oxygen content in the wellbore be strictly controlled and that antioxidant corrosion inhibitors be added.

10.3390/en18040910

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XSYSIGMA at SemEval-2020 Task 7: Method for Predicting Headlines’ Humor Based on Auxiliary Sentences with EI-BERT

Jian Ma Shuyi Xie Meizhi Jin Lianxin Jiang Yang Mo

This paper describes xsysigma team’s system for SemEval 2020 Task 7: Assessing the Funniness of Edited News Headlines. The target of this task is to assess the funniness changes of news headlines after minor editing and is divided into two subtasks: Subtask 1 is a regression task to detect the humor intensity of the sentence after editing; and Subtask 2 is a classification task to predict funnier of the two edited versions of an original headline. In this paper, we only report our implement of Subtask 2. We first construct sentence pairs with different features for Enhancement Inference BERT(EI-BERT)’s input. We then conduct data augmentation strategy and Pseudo-Label method. After that, we apply feature enhancement interaction on the encoding of each sentence for classification with EI-BERT. Finally, we apply weighted fusion algorithm to the logits results which obtained by different pre-trained models. We achieve 64.5% accuracy in subtask2 and rank the first and the fifth in dev and test dataset 1 , respectively.

SemEval

10.18653/v1/2020.semeval-1.142

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MagicPai at SemEval-2021 Task 7: Method for Detecting and Rating Humor Based on Multi-Task Adversarial Training

arXiv (Cornell University) (2021)

Jian Ma Shuyi Xie Haiqin Yang Lianxin Jiang Mengyuan Zhou

This paper describes MagicPai's system for SemEval 2021 Task 7, HaHackathon: Detecting and Rating Humor and Offense. This task aims to detect whether the text is humorous and how humorous it is. There are four subtasks in the competition. In this paper, we mainly present our solution, a multi-task learning model based on adversarial examples, for task 1a and 1b. More specifically, we first vectorize the cleaned dataset and add the perturbation to obtain more robust embedding representations. We then correct the loss via the confidence level. Finally, we perform interactive joint learning on multiple tasks to capture the relationship between whether the text is humorous and how humorous it is. The final result shows the effectiveness of our system.

SemEval

10.48550/arxiv.2104.10336

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Screening the TiZrHfNbVMoTa refractory high-entropy alloys with multi-property constraints

Journal of Alloys and Compounds (2025)

Ruixia Sun Haiqing Yin Jie Liu Shuyi Xie Cong Zhang

Refractory (planetary science)

10.1016/j.jallcom.2025.179284

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Site preference and elastic properties of L21-Ni2TiAl doped with refractory metal elements from first principles