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Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

《结构与土木工程前沿（英文）》 2023年第17卷第7期页码 994-1010 doi: 10.1007/s11709-023-0942-5

摘要： The moving trajectory of the pipe-jacking machine (PJM), which primarily determines the end quality of jacked tunnels, must be controlled strictly during the entire jacking process. Developing prediction models to support drivers in performing rectifications in advance can effectively avoid considerable trajectory deviations from the designed jacking axis. Hence, a gated recurrent unit (GRU)-based deep learning framework is proposed herein to dynamically predict the moving trajectory of the PJM. In this framework, operational data are first extracted from a data acquisition system; subsequently, they are preprocessed and used to establish GRU-based multivariate multistep-ahead direct prediction models. To verify the performance of the proposed framework, a case study of a large pipe-jacking project in Shanghai and comparisons with other conventional models (i.e., long short-term memory (LSTM) network and recurrent neural network (RNN)) are conducted. In addition, the effects of the activation function and input time-step length on the prediction performance of the proposed framework are investigated and discussed. The results show that the proposed framework can dynamically and precisely predict the PJM moving trajectory during the pipe-jacking process, with a minimum mean absolute error and root mean squared error (RMSE) of 0.1904 and 0.5011 mm, respectively. The RMSE of the GRU-based models is lower than those of the LSTM- and RNN-based models by 21.46% and 46.40% at the maximum, respectively. The proposed framework is expected to provide an effective decision support for moving trajectory control and serve as a foundation for the application of deep learning in the automatic control of pipe jacking.

关键词： dynamic prediction moving trajectory pipe jacking GRU deep learning

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The prediction technology study of fatigue life for key parts of a tracked vehicle’s suspension system

WANG Hongyan, RUI Qiang, HE Xiaojun

《机械工程前沿（英文）》 2007年第2卷第1期页码 68-71 doi: 10.1007/s11465-007-0011-0

摘要： In allusion to fatigue life of a tracked vehicle torsion bar, a virtual prototype model of the tracked vehicle suspension system including a flexible torsion bar was built based on dynamic simulation software ADAMS. Node force and stress results of the torsion bar from last step simulation were acquired; taking into account the material characteristics and influential factors, fatigue life of the flexible body of the torsion bar was predicted. Engineering results can be acquired through the contrast of the result of virtual test and statistical fatigue.

关键词： dynamic simulation simulation software allusion influential material

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Prediction of bearing capacity of pile foundation using deep learning approaches

《结构与土木工程前沿（英文）》 2024年第18卷第6期页码 870-886 doi: 10.1007/s11709-024-1085-z

摘要： The accurate prediction of bearing capacity is crucial in ensuring the structural integrity and safety of pile foundations. This research compares the Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) algorithms utilizing a data set of 257 dynamic pile load tests for the first time. Also, this research illustrates the multicollinearity effect on DNN, CNN, RNN, LSTM, and BiLSTM models’ performance and accuracy for the first time. A comprehensive comparative analysis is conducted, employing various statistical performance parameters, rank analysis, and error matrix to evaluate the performance of these models. The performance is further validated using external validation, and visual interpretation is provided using the regression error characteristics (REC) curve and Taylor diagram. Results from the comparative analysis reveal that the DNN (Coefficient of determination (R²)_{training (TR)} = 0.97, root mean squared error (RMSE)_TR = 0.0413; R²_{testing (TS)} = 0.9, RMSE_TS = 0.08) followed by BiLSTM (R²_TR = 0.91, RMSE_TR = 0.782; R²_TS = 0.89, RMSE_TS = 0.0862) model demonstrates the highest performance accuracy. It is noted that the BiLSTM model is better than LSTM because the BiLSTM model, which increases the amount of information for the network, is a sequence processing model made up of two LSTMs, one of which takes the input in a forward manner, and the other in a backward direction. The prediction of pile-bearing capacity is strongly influenced by ram weight (having a considerable multicollinearity level), and the effect of the considerable multicollinearity level has been determined for the model based on the recurrent neural network approach. In this study, the recurrent neural network model has the least performance and accuracy in predicting the pile-bearing capacity.

关键词： deep learning algorithms high-strain dynamic pile test bearing capacity of the pile

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煤岩灾害动力现象危险性预测中的定向定位研究

肖红飞,何学秋,冯涛,王恩元

《中国工程科学》 2005年第7卷第11期页码 81-86

摘要：

为了更好地应用非接触电磁辐射方法预测预报煤岩灾害动力现象的危险性，借助于力电耦合方程计算出在天线的各个朝向电磁辐射信号的变化规律及对某一个固定监测点结果的比较，就可以确定出某一个监测点电磁辐射信号的最佳监测方向以及应力变化最大（即危险性最大）的方向；在巷道掘进过程中进行巷道迎头电磁辐射动态监测时，天线测定的位置应该放置在巷道高度方向的中部，并对着煤层层理方向，通过不断改变天线朝向来监测巷道迎头和两帮煤岩层的突出危险性；可以通过应力场数值模拟得出的应力集中区来确定有效预测距离的范围。现场电磁辐射信号测定结果表明，利用力电耦合研究煤岩灾害动力现象危险性预测中危险区域的定向定位是可行的。

关键词：煤岩动力灾害危险性预测定向定位煤岩变形破裂力电耦合电磁辐射（EME）

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从汶川地震震前现象认识其发震动力应具有的大尺度与深层次性

许绍燮

《中国工程科学》 2009年第11卷第6期页码 16-18

摘要：

认为汶川地震发震动力具有大尺度与深层次性，并就汶川地震的震前地震活动性进行了分析研究；大小环圆的交切，全球尺度条带的交会，深部地震活动的加强这几项地震活动性图像是汶川地震震前现象的重要特征，这些特征图像在1976年的唐山地震前也有所显示；条带交会，环圆交会，带圆交会处质点运动各异，相互闭锁，质点运动不易解耦，形成应力集中，常常可成为强震发生的场所；深部闭锁活动的特色，在地表观测中易于失察，成为巨大地震更不易预察的一种原因。鉴于巨大地震灾害的严重性，今后在地震预测监测中应加强对大尺度与深层次信息的监测、收集与分析研究。

关键词：汶川地震发震动力的大尺度与深层次性地震预测

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一种用于自动驾驶的车辆概率性长期轨迹预测框架 Article

刘金鑫, 罗禹贡, 钟志华, 李克强, 黄荷叶, 熊辉

《工程（英文）》 2022年第19卷第12期页码 228-239 doi: 10.1016/j.eng.2021.12.020

摘要：

在混合动态交通环境中，准确地预测周围车辆长期范围内的运动轨迹是自动驾驶车辆（AV）实现合理行为决策和保障行车安全不可或缺的前提条件之一。本文提出了一种车辆长期轨迹预测的概率框架，由驾驶意图推理模型（DIM）和轨迹预测模型（TPM）组成。DIM基于动态贝叶斯网络进行设计和应用，用于准确推断车辆潜在的驾驶意图。文中所提出的DIM结合了基本的交通规则和车辆多维运动信息。为了进一步提高轨迹预测精度并实现预测不确定性识别，本文开发了基于高斯过程（GP）的TPM，综合考虑了车辆模型的短期预测结果和运动特性。最后，在高速换道场景下进行仿真验证，说明了新方法的有效性。通过与其他先进方法进行对比，展示并验证了该框架在车辆长期轨迹预测任务中的优异性能。

关键词：自动驾驶动态贝叶斯网络驾驶意图识别高斯过程车辆轨迹预测

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华中生：基于个性化动态退化特征的重要设备故障预测方法（2019年12月22日）

华中生(高级职称)

2021年01月20日

关键词：设备故障预测

Spatial prediction of soil contamination based on machine learning: a review

《环境科学与工程前沿（英文）》 2023年第17卷第8期 doi: 10.1007/s11783-023-1693-1

摘要：

● A review of machine learning (ML) for spatial prediction of soil contamination.

关键词： Soil contamination Machine learning Prediction Spatial distribution

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

《医学前沿（英文）》 2022年第16卷第3期页码 496-506 doi: 10.1007/s11684-021-0828-7

摘要： The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients’ physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.

关键词： XGBoost deep neural network healthcare risk prediction

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我国自主研制的全球/区域一体化数值天气预报系统GRAPES的应用与展望

陈德辉,薛纪善,沈学顺,孙健,万齐林,金之雁,李兴良

《中国工程科学》 2012年第14卷第9期页码 46-54

摘要：

介绍了中国气象局自主研制的新一代全球与区域一体化数值天气预报系统（GRAPES）,着重讨论了该系统的全可压/非静力平衡动力框架，全球模式/区域模式一体化设计，半隐式-半拉格朗日差分方案，标准化、模块化、并行化、模式程序软件体系等核心技术特点。GRAPES系统已在国家级、区域级气象业务中心，以及一些大学和研究所得到应用，并在不断地完善和发展。

关键词：数值预报天气预报全球/区域一体化模式动力框架资料同化物理过程参数化

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Online machine learning for stream wastewater influent flow rate prediction under unprecedented emergencies

《环境科学与工程前沿（英文）》 2023年第17卷第12期 doi: 10.1007/s11783-023-1752-7

摘要：

● Online learning models accurately predict influent flow rate at wastewater plants.

关键词： Wastewater prediction Data stream Online learning Batch learning Influent flow rates

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Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

《能源前沿（英文）》 2016年第10卷第4期页码 479-488 doi: 10.1007/s11708-016-0425-7

摘要： In this paper a novel method for reliability prediction and validation of nuclear power units in service is proposed. The equivalent availability factor is used to measure the reliability, and the equivalent availability factor deducting planed outage hours from period hours and maintenance factor are used for the measurement of inherent reliability. By statistical analysis of historical reliability data, the statistical maintenance factor and the undetermined parameter in its numerical model can be determined. The numerical model based on the maintenance factor predicts the equivalent availability factor deducting planed outage hours from period hours, and the planed outage factor can be obtained by using the planned maintenance days. Using these factors, the equivalent availability factor of nuclear power units in the following 3 years can be obtained. Besides, the equivalent availability factor can be predicted by using the historical statistics of planed outage factor and the predicted equivalent availability factor deducting planed outage hours from period hours. The accuracy of the reliability prediction can be evaluated according to the comparison between the predicted and statistical equivalent availability factors. Furthermore, the reliability prediction method is validated using the nuclear power units in North American Electric Reliability Council (NERC) and China. It is found that the relative errors of the predicted equivalent availability factors for nuclear power units of NERC and China are in the range of –2.16% to 5.23% and –2.15% to 3.71%, respectively. The method proposed can effectively predict the reliability index in the following 3 years, thus providing effective reliability management and maintenance optimization methods for nuclear power units.

关键词： nuclear power units in service reliability reliability prediction equivalent availability factors

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Position-varying surface roughness prediction method considering compensated acceleration in milling

《机械工程前沿（英文）》 2021年第16卷第4期页码 855-867 doi: 10.1007/s11465-021-0649-z

摘要： Machined surface roughness will affect parts’ service performance. Thus, predicting it in the machining is important to avoid rejects. Surface roughness will be affected by system position dependent vibration even under constant parameter with certain toolpath processing in the finishing. Aiming at surface roughness prediction in the machining process, this paper proposes a position-varying surface roughness prediction method based on compensated acceleration by using regression analysis. To reduce the stochastic error of measuring the machined surface profile height, the surface area is repeatedly measured three times, and Pauta criterion is adopted to eliminate abnormal points. The actual vibration state at any processing position is obtained through the single-point monitoring acceleration compensation model. Seven acceleration features are extracted, and valley, which has the highest R-square proving the effectiveness of the filtering features, is selected as the input of the prediction model by mutual information coefficients. Finally, by comparing the measured and predicted surface roughness curves, they have the same trends, with the average error of 16.28% and the minimum error of 0.16%. Moreover, the prediction curve matches and agrees well with the actual surface state, which verifies the accuracy and reliability of the model.

关键词： surface roughness prediction compensated acceleration milling thin-walled workpiece

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Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

《机械工程前沿（英文）》 2010年第5卷第2期页码 171-175 doi: 10.1007/s11465-009-0091-0

摘要： Trend prediction technology is the key technology to achieve condition-based maintenance of mechanical equipment. Large-sized water injection units are key equipment in oilfields. The traditional preventive maintenance is not economical and cannot completely avoid vicious accidents. To ensure the normal operation of units and save maintenance costs, trend prediction technology is studied to achieve condition-based maintenance for water injection units. The main methods of the technology are given, the trend prediction method based on neural network is put forward, and the expert system based on the knowledge is developed. The industrial site verification shows that the proposed trend prediction technology can reflect the operating condition trend change of the water injection units and provide technical means to achieve condition-based predictive maintenance.

关键词： water injection units condition-based maintenance trend prediction

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Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

《结构与土木工程前沿（英文）》 2013年第7卷第1期页码 72-82 doi: 10.1007/s11709-013-0185-y

摘要： A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibility as a classification problem, which is an imperative task in earthquake engineering. This paper examines the potential of SVM model in prediction of liquefaction using actual field cone penetration test (CPT) data from the 1999 Chi-Chi, Taiwan earthquake. The SVM, a novel learning machine based on statistical theory, uses structural risk minimization (SRM) induction principle to minimize the error. Using cone resistance ( ) and cyclic stress ratio ( ), model has been developed for prediction of liquefaction using SVM. Further an attempt has been made to simplify the model, requiring only two parameters ( and maximum horizontal acceleration ), for prediction of liquefaction. Further, developed SVM model has been applied to different case histories available globally and the results obtained confirm the capability of SVM model. For Chi-Chi earthquake, the model predicts with accuracy of 100%, and in the case of global data, SVM model predicts with accuracy of 89%. The effect of capacity factor ( ) on number of support vector and model accuracy has also been investigated. The study shows that SVM can be used as a practical tool for prediction of liquefaction potential, based on field CPT data.

关键词： earthquake cone penetration test liquefaction support vector machine (SVM) prediction