Qiang Liu

Shanghai Ocean University

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Aiping Tang

Hainan University

Delong Huang

Southwest Medical University

Xiaosheng Han

China University of Geosciences (Beijing)

Aiping Tang

Second Affiliated Hospital of Nanchang University

Dianrui Mu

Kunming University of Science and Technology

Ziyuan Huang

Harbin Institute of Technology

Zhongling Zong

Shanghai Ocean University

Bin Zhang

China University of Geosciences (Beijing)

Hang Cen

Shanghai Ocean University

Zhiming Li

Changchun University

Cooperative Institutions

Shanghai Ocean University

Harbin Institute of Technology

Hunan University of Technology

Heilongjiang Institute of Technology

Chinese Academy of Sciences

Tianjin University

Ministry of Industry and Information Technology

China University of Geosciences (Beijing)

Peng Cheng Laboratory

Tsinghua University

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Fragility Study of Buried Pipeline Networks Based on Copula Function

Journal of Pipeline Systems Engineering and Practice (2022)

Delong Huang Qiang Liu Zhiming Li Aiping Tang Dianrui Mu

The seismic fragility of water supply networks is an important research component for lifeline engineering in earthquake-prone regions. An accurate computational model was established to rapidly and efficiently assess pipeline damage under seismic excitation. In this study, the fragility of the pipeline network is deduced based on Copula function technology and the series-parallel model. This solves the low accuracy problem caused by simplifying elbow, tee, and other nodes and ignores the correlation between connected components in the fragility calculation of the buried pipeline network. Then, the shaking table tests of the elbow pipe, tee pipe, and scale pipeline network are performed, and the prototype pipeline network’s finite element comparative calculation model is also established. Then, the optimal Copula function for the pipeline network, the Gumbel Copula function, is obtained by calculating the correlation between each component’s seismic demand. It was found that the seismic demand of pipe and elbow components in the sand is higher than that in the clay, while the opposite is valid for the tee component. After that, a two-parameter log-normal distribution is employed to obtain the seismic fragility of three types of components in different soil properties. It was also found that the maximum failure probability can be obtained from the elbow leakage. The fragility of the elbow pipe is greater than that of the tee pipe. The fragility of components is then applied to the fragility calculation of the scale pipeline network by a series-parallel model, and the obtained results are compared with numerical simulations. It is demonstrated that the series-parallel combination calculation-based results have a high safety reserve for the pipeline design.

10.1061/(asce)ps.1949-1204.0000686

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Citations (4)

Fatigue reliability assessment of rib-to-deck double-sided welded joints in orthotropic steel decks considering welding residual stress

Scientific Reports (2024)

Fanghuai Chen Qiang Liu Yida Lu Yuan Luo Xinhui Xiao

Fatigue cracking of rib-to-deck conventional single-sided welded joints is a prevalent issue in orthotropic steel decks (OSDs), significantly impacting their structural integrity and durability. Rib-to-deck innovative double-sided welded joints have the potential to enhance the fatigue resistance of OSD. However, Welding Residual Stresses (WRS) significantly influence the fatigue life of these joints, mandating its consideration in fatigue assessments. This study introduces a novel approach for assessing the fatigue reliability of rib-to-deck double-sided welded joints in OSDs, accounting for the effects of traffic vehicle loading and WRS. Initially, a comprehensive fatigue damage equation for welded joints of OSDs was formulated, integrating WRS and dynamic vehicle loads, utilizing fracture mechanics theory. Subsequently, a measurement-based random traffic model was utilized to derive the vehicle-induced stress spectra at the welded joints. The effects of deck thickness, fatigue crack depth and fatigue crack aspect ratio on the stress intensity factor (SIF) of the crack at the weld toe were analyzed. These three variables were considered as feature vectors for the construction of a polynomial model of the shape functions, which was utilized to calculate the SIF. Finally, the fatigue reliability of the rib-to-deck double-sided welded joints in OSDs subject to traffic vehicle loading considering the WRS was estimated using Monte Carlo simulation. The influence of traffic volume growth on fatigue reliability was discussed. This research underscores the critical role of WRS in the fatigue performance of welded joints in OSDs and offers an innovative framework for assessing the fatigue reliability of steel bridge welds.

Orthotropic material

Fatigue limit

10.1038/s41598-024-83091-2

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Discussion on the tree-based machine learning model in the study of landslide susceptibility

Natural Hazards (2022)

Qiang Liu Aiping Tang Ziyuan Huang Lixin Sun Xiaosheng Han

Boosting

Tree (set theory)

Gradient boosting

10.1007/s11069-022-05329-4

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Citations (12)

Landslide risk of regional roads: Consider the road mileage of expected losses

Transportation Research Part D Transport and Environment (2023)

Qiang Liu Bin Zhang Aiping Tang

Vulnerability

10.1016/j.trd.2023.103771

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Citations (6)

Exploring aspects affecting the predicted capacity of landslide susceptibility based on machine learning technology

Geocarto International (2022)

Qiang Liu Aiping Tang

This study reported the causes of prediction uncertainty in landslide susceptibility, based on machine learning. The landslide inventory and conditioning factors were first prepared. Thereafter, factor analysis, including the effectiveness, multicollinearity, and importance was performed for the subsequent susceptibility modeling. Finally, the predicted capacity was explored in terms of model performance and map performance. Investigation indicates that landslides are focused on the central and southeastern part of the study area, displaying the characteristics of small-scale and shallow sliding. The difference in the ML algorithm, kernel functions, the selection of base-classifier, the evaluation indicators of performance, and even the factor zonation results in difficulties in determining the optimal ML model. Besides, the DT-Boosting keeps the high AUC value, 0.981 and 0.862 respectively in the training and test phases, while the RF holds a highly robust in terms of fluctuation characteristics. Indeed, the susceptibility map generated by RBF-ANN can better capture the difference in landslide susceptibility, with the D-value of 7.9. This study provides an in-depth understanding of the uncertainty of the predicted capacity in ML-based landslide susceptibility.

Boosting

10.1080/10106049.2022.2088863

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Citations (7)

Model performance analysis for landslide susceptibility in cold regions using accuracy rate and fluctuation characteristics

Natural Hazards (2021)

Qiang Liu Delong Huang Aiping Tang Xiaosheng Han

10.1007/s11069-021-04719-4

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Citations (22)

Modeling of landslides susceptibility prediction using deep belief networks with optimized learning rate control

Geocarto International (2024)

Qiang Liu Namkha Norbu

To overcome critical issues in landslide susceptibility modeling, a multifactor landslide susceptibility prediction model based on deep belief networks (DBN) with optimized learning rate control (LRC-DBN) was introduced in this study. The LRC-DBN model was applied to predict landslide susceptibility in Zhidan County, Shaanxi and its performance was compared against random forest, support vector machine and logistic regression models. The results show that the LRC-DBN model achieves a maximum AUC value of 0.941, which demonstrating higher predictive performance. The interpretability of LRC-DBN reveals that the development of loess landslides is more likely in areas with elevations ranging from 894.7 m to 998.5 m, slopes ranging from 49.6° to 64°, terrain undulations spanning 31.7 m to 91.0 m, rock type T3y and terrain humidity ranging from 2.4 to 3.9. The result provides invaluable insights for local landslide prevention endeavors and can assist decision-making in urban planning.

Interpretability

Deep belief network

Ranging

10.1080/10106049.2024.2322060

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Citations (1)

Exploring the mechanisms of chronic obstructive pulmonary disease and Crohn’s disease: a bioinformatics-based study

Scientific Reports (2024)

Xinxin Zhang Caiping Liu L CAO Hongguang Tang Haiyun Jiang

This study explored the comorbid mechanisms between Crohn's disease (CD) and chronic obstructive pulmonary disease (COPD) using bioinformatics analysis. From the Gene Expression Omnibus (GEO) microarray dataset, 349 common differentially expressed genes (coDEGs) were identified, and 8 shared hub genes were found: CCL2, CXCL1, TLR2, ICAM1, PTPRC, ITGAX, PTGS2, and MMP9, which were vital for immune function and regulation of inflammatory responses. In addition, the study also analyzed the association between coDEGs and immune cell infiltration using the single-sample gene set enrichment algorithm (ssGSEA). Potential drugs related to these genes were identified using the connectivity map (CMap). These findings provided new perspectives for understanding the interaction between CD and COPD.

CXCL1

Immune Dysregulation

10.1038/s41598-024-78697-5

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Exploring the uncertainty of landslide susceptibility assessment caused by the number of non–landslides

CATENA (2023)

Qiang Liu Aiping Tang Delong Huang

10.1016/j.catena.2023.107109

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Citations (34)

Probabilistic Analysis of the Landslide Hazard in Cold Regions: Considering Multiple Triggering Factors and Their Interdependence