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Short-term prediction of the influent quantity time series of wastewater treatment plant based on a chaos

LI Xiaodong, ZENG Guangming, HUANG Guohe, LI Jianbing, JIANG Ru

《环境科学与工程前沿（英文）》 2007年第1卷第3期页码 334-338 doi: 10.1007/s11783-007-0057-6

摘要： By predicting influent quantity, a wastewater treatment plant (WWTP) can be well controlled. The nonlinear dynamic characteristic of WWTP influent quantity time series was analyzed, with the assumption that the series was predictable. Based on this, a short-term forecasting chaos neural network model of WWTP influent quantity was built by phase space reconstruction. Reasonable forecasting results were achieved using this method.

关键词： nonlinear reconstruction WWTP influent characteristic Reasonable forecasting

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Experimental study for the stratified to slug flow regime transition mechanism of gas-oil two-phase flow in horizontal pipe

LIU Yiping, YANG Weilin, WANG Jing

《能源前沿（英文）》 2008年第2卷第2期页码 152-157 doi: 10.1007/s11708-008-0012-7

摘要： Theoretical relations that predict the transition from a stratified pattern to a slug pattern, including a one-dimensional wave model that contains less empiricism than the commonly used Taitel-Dukler model, and the ideal model for stratified flow for the gas-liquid flow in horizontal pipes are presented. Superficial velocities of each phase, as the onset of slugging occurs, were predicted, and theoretical analysis was conducted on the stratified to slug flow regime transition. The friction, existing between the fluid and pipe wall, and on the interface of two phases, was especially taken into account. A theoretical model was applied to an experiment about air-oil two-phase flow in a 50 mm horizontal pipe. The effect of pipe diameter on the transition was also studied. The results show that this approach gives a reasonable prediction over the whole range of flow rates, and better agreement has been achieved between predicted and measured critical parameters.

关键词： two-phase Superficial reasonable prediction one-dimensional gas-liquid

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大中功率节能调速传动的合理电压等级

马小亮

《中国工程科学》 2001年第3卷第11期页码 80-84

摘要：

大中功率风机和泵采用变频调速可节约大量电能，大部分功率在0.2～2 MW范围中。我国现在200 kW以上的电机多是中压，现行中压电网多为10 kV，选用10 kV直接变频从技术和经济角度看都不太合理。由于变频器输入侧都有变压器，因此电机和变频器没有必要和电网一致。文章讨论不同功率段的合理电压等级以及在电机电压和电网电压不同情况下当变频器出现故障时如何实现旁路工作。

关键词：大中功率节能调速合理电压等级低压变频中压变频旁路

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千米级斜拉桥空间非线性合理恒载索力分析

张建民,肖汝诚

《中国工程科学》 2004年第6卷第12期页码 37-42

摘要：

建立了斜拉桥索力调整的空间非线性有限元分析模型，以斜拉桥主梁和索塔的弯曲应变能为目标函数、结构应力及索力为约束条件，采用一阶最优化计算方法进行求解，用以确定成桥合理状态的索力。应用该法分析了某千米级斜拉桥的合理成桥状态，计算结果表明，该方法简单、有效。

关键词：斜拉桥一阶分析法合理成桥状态

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Aerodynamic challenges in span length of suspension bridges

XIANG Haifan, GE Yaojun

《结构与土木工程前沿（英文）》 2007年第1卷第2期页码 153-162 doi: 10.1007/s11709-007-0016-0

摘要： The potential requirement of extreme bridge spans is firstly discussed according to horizontal clearances for navigation and economical construction of deep-water foundation. To ensure the technological feasibility of suspension bridges with longer spans, the static estimation of feasible span length is then made based on current material strength and weight of cables and deck. After the performances of the countermeasures for raising the aerodynamic stability are reviewed, a trial design of a 5 000 m suspension bridge, which is estimated as a reasonable limitation of span length, is finally conducted to respond to the tomorrow s challenge in span length of suspension bridges with the particular aspects, including dynamic stiffness, aerodynamic flutter and aerostatic stability.

关键词： requirement reasonable limitation challenge particular economical construction

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基于多元数据的交通视角超大特大城市中心城区合理规模研究

陆化普,柏卓彤,吴洲豪,傅志寰

《中国工程科学》 2022年第24卷第6期页码 146-153 doi: 10.15302/J-SSCAE-2022.06.013

摘要：

超大特大城市中心城区高强度连片开发、人口密度大、城市功能集中，是我国城市问题表现最为突出的区域范围；着眼集中于中心城区的大城市病破解问题，开展超大特大城市中心城区的合理规模分析论证具有迫切性。本文提出了通勤出行时间是超大特大城市中心城区合理规模的核心控制因素这一基本判断；采用大数据分析及聚类分析方法，结合城市多类土地利用的兴趣点数据、街道行政边界的地理信息系统数据，识别了我国10 个超大特大城市的现状中心城区范围；基于网络地图路径规划、手机信令数据校核，分析评价了现状交通效率；以量化分析为基础，获得了特大城市中心城区合理规模的论证结果。研究表明，当前一些超大特大城市的中心城区范围不能满足以人为本的幸福通勤出行需求；结合未来交通运输领域技术发展、治理水平提高等因素，13~15 km当量半径是超大特大城市中心城区合理规模范围的上限。

关键词：合理规模；多元数据；中心城区；超大特大城市；幸福感

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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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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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Improved prediction of pile bending moment and deflection due to adjacent braced excavation

《结构与土木工程前沿（英文）》 doi: 10.1007/s11709-023-0961-2

摘要： Deep excavations in dense urban areas have caused damage to nearby existing structures in numerous past construction cases. Proper assessment is crucial in the initial design stages. This study develops equations to predict the existing pile bending moment and deflection produced by adjacent braced excavations. Influential parameters (i.e., the excavation geometry, diaphragm wall thickness, pile geometry, strength and small-strain stiffness of the soil, and soft clay thickness) were considered and employed in the developed equations. It is practically unfeasible to obtain measurement data; hence, artificial data for the bending moment and deflection of existing piles were produced from well-calibrated numerical analyses of hypothetical cases, using the three-dimensional finite element method. The developed equations were established through a multiple linear regression analysis of the artificial data, using the transformation technique. In addition, the three-dimensional nature of the excavation work was characterized by considering the excavation corner effect, using the plane strain ratio parameter. The estimation results of the developed equations can provide satisfactory pile bending moment and deflection data and are more accurate than those found in previous studies.

关键词： pile responses excavation prediction deflection bending moments

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

《结构与土木工程前沿（英文）》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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Prediction of the shear wave velocity

Amoroso SARA

《结构与土木工程前沿（英文）》 2014年第8卷第1期页码 83-92 doi: 10.1007/s11709-013-0234-6

摘要： The paper examines the correlations to obtain rough estimates of the shear wave velocity from non-seismic dilatometer tests (DMT) and cone penetration tests (CPT). While the direct measurement of is obviously preferable, these correlations may turn out useful in various circumstances. The experimental results at six international research sites suggest that the DMT predictions of from the parameters (material index), (horizontal stress index), (constrained modulus) are more reliable and consistent than the CPT predictions from (cone resistance), presumably because of the availability, by DMT, of the stress history index .

关键词： horizontal stress index shear wave velocity flat dilatometer test cone penetration test

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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