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Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD

《结构与土木工程前沿（英文）》 2021年 第15卷 第1期   页码 80-98 doi: 10.1007/s11709-021-0682-3

摘要： Liquefaction-induced lateral displacement is responsible for considerable damage to engineered structures during major earthquakes. Therefore, an accurate estimation of lateral displacement in liquefaction-prone regions is an essential task for geotechnical experts for sustainable development. This paper presents a novel probabilistic framework for evaluating liquefaction-induced lateral displacement using the Bayesian belief network (BBN) approach based on an interpretive structural modeling technique. The BBN models are trained and tested using a wide-range case-history records database. The two BBN models are proposed to predict lateral displacements for free-face and sloping ground conditions. The predictive performance results of the proposed BBN models are compared with those of frequently used multiple linear regression and genetic programming models. The results reveal that the BBN models are able to learn complex relationships between lateral displacement and its influencing factors as cause–effect relationships, with reasonable precision. This study also presents a sensitivity analysis to evaluate the impacts of input factors on the lateral displacement.

关键词： Bayesian belief network     seismically induced soil liquefaction     interpretive structural modeling     lateral displacement    

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Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU

《结构与土木工程前沿（英文）》 2021年 第15卷 第2期   页码 490-505 doi: 10.1007/s11709-020-0669-5

摘要： This study investigates the performance of four machine learning (ML) algorithms to evaluate the earthquake-induced liquefaction potential of soil based on the cone penetration test field case history records using the Bayesian belief network (BBN) learning software Netica. The BBN structures that were developed by ML algorithms-K2, hill climbing (HC), tree augmented naive (TAN) Bayes, and Tabu search were adopted to perform parameter learning in Netica, thereby fixing the BBN models. The performance measure indexes, namely, overall accuracy ( ), precision, recall, , and area under the receiver operating characteristic curve, were used to evaluate the training and testing BBN models’ performance and highlight the capability of the K2 and TAN Bayes models over the Tabu search and HC models. The sensitivity analysis results showed that the cone tip resistance and vertical effective stress are the most sensitive factors, whereas the mean grain size is the least sensitive factor in the prediction of seismic soil liquefaction potential. The results of this study can provide theoretical support for researchers in selecting appropriate ML algorithms and improving the predictive performance of seismic soil liquefaction potential models.

关键词： seismic soil liquefaction     Bayesian belief network     cone penetration test     parameter learning     structural learning    

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A step forward towards a comprehensive framework for assessing liquefaction land damage vulnerability: Exploration from historical data

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU

《结构与土木工程前沿（英文）》 2020年 第14卷 第6期   页码 1476-1491 doi: 10.1007/s11709-020-0670-z

摘要： The unprecedented liquefaction-related land damage during earthquakes has highlighted the need to develop a model that better interprets the liquefaction land damage vulnerability (LLDV) when determining whether liquefaction is likely to cause damage at the ground’s surface. This paper presents the development of a novel comprehensive framework based on select case history records of cone penetration tests using a Bayesian belief network (BBN) methodology to assess seismic soil liquefaction and liquefaction land damage potentials in one model. The BBN-based LLDV model is developed by integrating multi-related factors of seismic soil liquefaction and its induced hazards using a machine learning (ML) algorithm-K2 and domain knowledge (DK) data fusion methodology. Compared with the C4.5 decision tree-J48 model, naive Bayesian (NB) classifier, and BBN-K2 ML prediction methods in terms of overall accuracy and the Cohen’s kappa coefficient, the proposed BBN K2 and DK model has a better performance and provides a substitutive novel LLDV framework for characterizing the vulnerability of land to liquefaction-induced damage. The proposed model not only predicts quantitatively the seismic soil liquefaction potential and its ground damage potential probability but can also identify the main reasons and fault-finding state combinations, and the results are likely to assist in decisions on seismic risk mitigation measures for sustainable development. The proposed model is simple to perform in practice and provides a step toward a more sophisticated liquefaction risk assessment modeling. This study also interprets the BBN model sensitivity analysis and most probable explanation of seismic soil liquefied sites based on an engineering point of view.

关键词： Bayesian belief network     liquefaction-induced damage potential     cone penetration test     soil liquefaction     structural learning and domain knowledge    

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A knowledge reasoning Fuzzy-Bayesian network for root cause analysis of abnormal aluminum electrolysis

Weichao Yue, Xiaofang Chen, Weihua Gui, Yongfang Xie, Hongliang Zhang

《化学科学与工程前沿（英文）》 2017年 第11卷 第3期   页码 414-428 doi: 10.1007/s11705-017-1663-x

摘要： Root cause analysis (RCA) of abnormal aluminum electrolysis cell condition has long been a challenging industrial issue due to its inherent complexity in analyzing based on multi-source knowledge. In addition, accurate RCA of abnormal aluminum electrolysis cell condition is the precondition of improving current efficiency. RCA of abnormal condition is a complex work of multi-source knowledge fusion, which is difficult to ensure the RCA accuracy of abnormal cell condition because of dwindling and frequent flow of experienced technicians. In view of this, a method based on Fuzzy-Bayesian network to construct multi-source knowledge solidification reasoning model is proposed. The method can effectively fuse and solidify the knowledge, which is used to analyze the cause of abnormal condition by technicians providing a clear and intuitive framework to this complex task, and also achieve the result of root cause automatically. The proposed method was verified under 20 sets of abnormal cell conditions, and implements root cause analysis by finding the abnormal state of root node, which has a maximum posterior probability by Bayesian diagnosis reasoning. The accuracy of the test results is up to 95%, which shows that the knowledge reasoning feasibility for RCA of aluminum electrolysis cell.

关键词： abnormal aluminum electrolysis cell condition     Fuzzy-Bayesian network     multi-source knowledge solidification and reasoning     root cause analysis    

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A case study on sample average approximation method for stochastic supply chain network design problem

Yuan WANG, Ruyan SHOU, Loo Hay LEE, Ek Peng CHEW

《工程管理前沿（英文）》 2017年 第4卷 第3期   页码 338-347 doi: 10.15302/J-FEM-2017032

摘要： This study aims to solve a typical long-term strategic decision problem on supply chain network design with consideration to uncertain demands. Existing methods for these problems are either deterministic or limited in scale. We analyze the impact of uncertainty on demand based on actual large data from industrial companies. Deterministic equivalent model with nonanticipativity constraints, branch-and-fix coordination, sample average approximation (SAA) with Bayesian bootstrap, and Latin hypercube sampling were adopted to analyze stochastic demands. A computational study of supply chain network with front-ends in Europe and back-ends in Asia is presented to highlight the importance of stochastic factors in these problems and the efficiency of our proposed solution approach.

关键词： supply chain network     stochastic demand     sampling average approximation     Bayesian bootstrap     Latin hypercube sampling    

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基于DBN模型的激光焊接状态在线监控研究 Article

张艳喜, 游德勇, 高向东, Seiji Katayama

《工程（英文）》 2019年 第5卷 第4期   页码 671-678 doi: 10.1016/j.eng.2019.01.016

摘要：

本文开发了集辅助激光成像系统、紫外/可见波段视觉成像系统（波长小于780 nm）、光谱测量仪、光电传感器的多传感器系统，用以观察和分析激光焊接过程中的焊接状态信息。本文采用小波包分解方法对通过光电传感器获得的可见光波段传感信号和激光反射传感信号进行分解并提取相关特征。光谱仪采集到的信号的主要波长为400~900 nm，将其分为25个子带并采用统计方法来提取光谱信号特征。利用紫外/可见波段视觉成像系统采集的图像获取金属蒸气和飞溅的特征，而辅助照明视觉传感器系统主要是用来采集匙孔特征。本文基于上述焊接过程的实时量化特征建立了焊接状态监测的深度信念网络（DBN），并采用遗传算法对所提出的DBN模型参数进行优化。与传统的反向传播神经网络（BPNN）模型相比，本文建立的DBN模型在焊接状态监测方面具有更高的精度和鲁棒性。最后，本文通过三个附加焊接试验验证了该方法在激光焊接状态监控中的有效性和泛化能力。

关键词： 在线监控     多传感     小波包分解     深信度网络    

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A Bayesian modeling approach to bi-directional pedestrian flows in carnival events

S. Q. XIE, S. C. WONG, William H. K. LAM

《工程管理前沿（英文）》 2017年 第4卷 第4期   页码 483-489 doi: 10.15302/J-FEM-2017023

摘要： Bi-directional pedestrian flows are common at crosswalks, footpaths, and shopping areas. However, the properties of pedestrian movement may vary in urban areas according to the type of walking facility. In recent years, crowd movements at carnival events have attracted the attention of researchers. In contrast to pedestrian behavior in other walking facilities, pedestrians whose attention is attracted by carnival displays or activities may slow down and even stop walking. The Lunar New Year Market is a traditional carnival event in Hong Kong held annually one week before the Lunar New Year. During the said event, crowd movements can be easily identified, particularly in Victoria Park, where the largest Lunar New Year Market in Hong Kong is hosted. In this study, we conducted a video-based observational survey to collect pedestrian flow and speed data at the Victoria Park Lunar New Year Market on the eve of the Lunar New Year. Using the collected data, an extant mathematical model was calibrated to capture the relationships between the relevant macroscopic quantities, thereby providing insight into pedestrian behavior at the carnival event. Bayesian inference was employed to calibrate the model by using prior data obtained from a previous controlled experiment. Results obtained enhance our understanding of crowd behavior under different conditions at carnival events, thus facilitating the improvement of the safety and efficiency of similar events in the future.

关键词： pedestrian flow model     bi-directional interactions     empirical studies     Bayesian inference    

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An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

《能源前沿（英文）》 2022年 第16卷 第2期   页码 277-291 doi: 10.1007/s11708-021-0731-6

摘要： An integrated and systematic database of sooting tendency with more than 190 kinds of fuels was obtained through a series of experimental investigations. The laser-induced incandescence (LII) method was used to acquire the 2D distribution of soot volume fraction, and an apparatus-independent yield sooting index (YSI) was experimentally obtained. Based on the database, a novel predicting model of YSI values for surrogate fuels was proposed with the application of a machine learning method, named the Bayesian multiple kernel learning (BMKL) model. A high correlation coefficient (0.986) between measured YSIs and predicted values with the BMKL model was obtained, indicating that the BMKL model had a reliable and accurate predictive capacity for YSI values of surrogate fuels. The BMKL model provides an accurate and low-cost approach to assess surrogate performances of diesel, jet fuel, and biodiesel in terms of sooting tendency. Particularly, this model is one of the first attempts to predict the sooting tendencies of surrogate fuels that concurrently contain hydrocarbon and oxygenated components and shows a satisfying matching level. During surrogate formulation, the BMKL model can be used to shrink the surrogate candidate list in terms of sooting tendency and ensure the optimal surrogate has a satisfying matching level of soot behaviors. Due to the high accuracy and resolution of YSI prediction, the BMKL model is also capable of providing distinguishing information of sooting tendency for surrogate design.

关键词： sooting tendency     yield sooting index     Bayesian multiple kernel learning     surrogate assessment     surrogate formulation    

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Identification of pollution sources in rivers using a hydrodynamic diffusion wave model and improved Bayesian-Markov

《环境科学与工程前沿（英文）》 2023年 第17卷 第7期 doi: 10.1007/s11783-023-1685-1

摘要：

● A hydrodynamic-Bayesian inference model was developed for water pollution tracking.

关键词： Identification of pollution sources     Water quality restoration     Bayesian inference     Hydrodynamic model     Inverse problem    

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反应式容侵系统入侵预测的混合式贝叶斯网络方法

王良民,马建峰

《中国工程科学》 2008年 第10卷 第8期   页码 87-96

摘要： 提出了基于入侵模型的混合式贝叶斯网络(HyBN, hybrid bayesian network)模型，将入侵模型中攻击行为和系统安全状态节点分离为攻击层和状态层两个网络层次，两层间使用收敛连接，而两层内部的节点间使用连续连接

关键词： 容忍入侵     警报关联     入侵模型     入侵预测    

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基于可观测节点信息的控制器局域网节点可靠性评估 Article

Lei-ming ZHANG, Long-hao TANG, Yong LEI

《信息与电子工程前沿（英文）》 2017年 第18卷 第5期   页码 615-626 doi: 10.1631/FITEE.1601029

摘要： 基于控制器局域网的总线技术广泛应用于网络化制造系统。网络作为系统的信息通道，其可靠性对系统的吞吐量、产品质量以及工作人员的安全至关重要。然而，由于节点内部状态的不可访问性，因此使用节点内置的错误计数器值直接评估控制器局域网节点的可靠性是难以进行的。本文提出一种新颖的控制器局域网节点可靠性评估方法，该方法使用节点脱离总线时间作为可靠性测度。基于网络错误日志和错误计数器值可访问的可观测节点信息，该方法可以估计网络中节点的发送错误计数器值。首先，本文基于分段马尔科夫链建立了估计节点发送错误计数器值的模型，该模型考虑了网络中错误分布的稀疏特性。其次，通过学习可观测节点的模型估计值和实际测量值之间的偏差，建立了贝叶斯网络以表述可观测节点的模型估计值更新机制。然后，将该更新机制应用到网络中发送错误计数器值不可访问的节点，完成其模型估计值的更新。最后，建立了节点可靠性评估方法以预测节点的脱离总线时间。为表明文中方法的有效性，进行了多组实验。实验结果表明由文中方法得到的估计值与实际观测值相一致。

关键词： 控制器局域网；发送错误计数器；发送错误计数器值估计；贝叶斯网络；脱离总线时间    

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基于含隐变量的贝叶斯网络质量相关局部加权的非平稳过程软测量方法 Research Articles

《信息与电子工程前沿（英文）》 2021年 第22卷 第9期   页码 1234-1246 doi: 10.1631/FITEE.2000426

摘要：

在工业过程中，软测量技术被广泛用于预测难以测量的质量变量。构建一个应对过程非平稳性的自适应模型非常必要。本文针对非平稳过程，设计了一种基于含有隐变量贝叶斯网络的质量相关局部加权软测量方法。提出一种有监督贝叶斯网络提取质量相关的隐变量，并应用于一种双层相似度测量算法。所提软测量方法试图通过质量相关信息为非平稳过程寻找到一般方法，且详细解释了局部相似度和窗口置信度的概念。通过一个数值算例和脱丁烷塔的应用验证了所提方法的性能。结果表明所提方法预测关键质量变量的精确度优于竞争方法。

关键词： 软测量；有监督贝叶斯网络；隐变量；局部加权建模；质量预测    

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应用神经网络进行短期负荷预测

罗枚

《中国工程科学》 2007年 第9卷 第5期   页码 77-80

摘要：

以某地区购网有功功率的负荷数据为背景，建立了３个BP神经网络负荷预测模型———SDBP，LMBP 及BRBP模型进行短期负荷预测工作，并对其结果进行比较。针对传统的BP算法具有训练速度慢，易陷入局部 最小点的缺点，采用具有较快收敛速度及稳定性的L-M（Levenberg-Marquardt）优化算法进行预测，使平均相对误 差有了很大改善，而采用贝叶斯正则化算法可以解决网络过度拟合，提高网络的推广能力。

关键词： 短期负荷预测     人工神经网络     L唱M算法     贝叶斯正则化算法     优化算法    

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融合深度置信网络的串联隐马尔科夫模型及其在脱机手写识别中的应用 Article

Partha Pratim ROY, Guoqiang ZHONG, Mohamed CHERIET

《信息与电子工程前沿（英文）》 2017年 第18卷 第7期   页码 978-988 doi: 10.1631/FITEE.1600996

摘要： 在文档分析和模式识别领域，自由书写的脱机手写识别是一个非常具有挑战性的研究课题。近年来，为了充分探索隐藏在文档图像中的监督信息，许多研究工作试图将多层感知机以一种混合或串联的形式嵌入隐马尔科夫模型当中。然而，因为多层感知机学习能力的不足，学习到的特征对于后续的识别任务不一定是最优的。在本文中，我们针对自由书写的脱机手写识别提出一种基于深度结构的串联方法。在提出的模型中，深度置信网络被用于学习序列数据的紧致表示，隐马尔科夫模型被用于（子-）词的识别。我们在两个公开的数据集上验证了所提出的模型，这两个数据集是分别基于拉丁和阿拉伯语的RIMES和IFN/ENIT；我们还在Devanagari数据集上验证了所提出的模型，这个数据集是基于印度语的。大量的实验展示了所提出模型的优势，特别是相对于多层感知机-隐马尔科夫模型的串联方法。

关键词： 手写识别；隐马尔科夫模型；深度学习；深度置信网络；串联方法    

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A novel multimode process monitoring method integrating LDRSKM with Bayesian inference

Shi-jin REN,Yin LIANG,Xiang-jun ZHAO,Mao-yun YANG

《信息与电子工程前沿（英文）》 2015年 第16卷 第8期   页码 617-633 doi: 10.1631/FITEE.1400263

摘要： A local discriminant regularized soft -means (LDRSKM) method with Bayesian inference is proposed for multimode process monitoring. LDRSKM extends the regularized soft -means algorithm by exploiting the local and non-local geometric information of the data and generalized linear discriminant analysis to provide a better and more meaningful data partition. LDRSKM can perform clustering and subspace selection simultaneously, enhancing the separability of data residing in different clusters. With the data partition obtained, kernel support vector data description (KSVDD) is used to establish the monitoring statistics and control limits. Two Bayesian inference based global fault detection indicators are then developed using the local monitoring results associated with principal and residual subspaces. Based on clustering analysis, Bayesian inference and manifold learning methods, the within and cross-mode correlations, and local geometric information can be exploited to enhance monitoring performances for nonlinear and non-Gaussian processes. The effectiveness and efficiency of the proposed method are evaluated using the Tennessee Eastman benchmark process.

关键词： Multimode process monitoring     Local discriminant regularized soft k-means clustering     Kernel support vector data description     Bayesian inference     Tennessee Eastman process    

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标题 作者 时间 类型 操作

Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD

期刊论文

Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU

期刊论文

A step forward towards a comprehensive framework for assessing liquefaction land damage vulnerability: Exploration from historical data

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU

期刊论文

A knowledge reasoning Fuzzy-Bayesian network for root cause analysis of abnormal aluminum electrolysis

Weichao Yue, Xiaofang Chen, Weihua Gui, Yongfang Xie, Hongliang Zhang

期刊论文

A case study on sample average approximation method for stochastic supply chain network design problem

Yuan WANG, Ruyan SHOU, Loo Hay LEE, Ek Peng CHEW

期刊论文

基于DBN模型的激光焊接状态在线监控研究

张艳喜, 游德勇, 高向东, Seiji Katayama

期刊论文

A Bayesian modeling approach to bi-directional pedestrian flows in carnival events

S. Q. XIE, S. C. WONG, William H. K. LAM

期刊论文

An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

期刊论文

Identification of pollution sources in rivers using a hydrodynamic diffusion wave model and improved Bayesian-Markov

期刊论文

反应式容侵系统入侵预测的混合式贝叶斯网络方法

王良民,马建峰

期刊论文

基于可观测节点信息的控制器局域网节点可靠性评估

Lei-ming ZHANG, Long-hao TANG, Yong LEI

期刊论文

基于含隐变量的贝叶斯网络质量相关局部加权的非平稳过程软测量方法

期刊论文

应用神经网络进行短期负荷预测

罗枚

期刊论文

融合深度置信网络的串联隐马尔科夫模型及其在脱机手写识别中的应用

Partha Pratim ROY, Guoqiang ZHONG, Mohamed CHERIET

期刊论文

A novel multimode process monitoring method integrating LDRSKM with Bayesian inference

Shi-jin REN,Yin LIANG,Xiang-jun ZHAO,Mao-yun YANG

期刊论文