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Slope stability analysis based on big data and convolutional neural network

Yangpan FU; Mansheng LIN; You ZHANG; Gongfa CHEN; Yongjian LIU

《结构与土木工程前沿（英文）》 2022年 第16卷 第7期   页码 882-895 doi: 10.1007/s11709-022-0859-4

摘要： The Limit Equilibrium Method (LEM) is commonly used in traditional slope stability analyses, but it is time-consuming and complicated. Due to its complexity and nonlinearity involved in the evaluation process, it cannot provide a quick stability estimation when facing a large number of slopes. In this case, the convolutional neural network (CNN) provides a better alternative. A CNN model can process data quickly and complete a large amount of data analysis in a specific situation, while it needs a large number of training samples. It is difficult to get enough slope data samples in practical engineering. This study proposes a slope database generation method based on the LEM. Samples were amplified from 40 typical slopes, and a sample database consisting of 20000 slope samples was established. The sample database for slopes covered a wide range of slope geometries and soil layers’ physical and mechanical properties. The CNN trained with this sample database was then applied to the stability prediction of 15 real slopes to test the accuracy of the CNN model. The results show that the slope stability prediction method based on the CNN does not need complex calculation but only needs to provide the slope coordinate information and physical and mechanical parameters of the soil layers, and it can quickly obtain the safety factor and stability state of the slopes. Moreover, the prediction accuracy of the CNN trained by the sample database for slope stability analysis reaches more than 99%, and the comparisons with the BP neural network show that the CNN has significant superiority in slope stability evaluation. Therefore, the CNN can predict the safety factor of real slopes. In particular, the combination of typical actual slopes and generated slope data provides enough training and testing samples for the CNN, which improves the prediction speed and practicability of the CNN-based evaluation method in engineering practice.

关键词： slope stability     limit equilibrium method     convolutional neural network     database for slopes     big data    

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Anensemble method for data stream classification in the presence of concept drift

Omid ABBASZADEH,Ali AMIRI,Ali Reza KHANTEYMOORI

《信息与电子工程前沿（英文）》 2015年 第16卷 第12期   页码 1059-1068 doi: 10.1631/FITEE.1400398

摘要： One recent area of interest in computer science is data stream management and processing. By ‘data stream’, we refer to continuous and rapidly generated packages of data. Specific features of data streams are immense volume, high production rate, limited data processing time, and data concept drift; these features differentiate the data stream from standard types of data. An issue for the data stream is classification of input data. A novel ensemble classifier is proposed in this paper. The classifier uses base classifiers of two weighting functions under different data input conditions. In addition, a new method is used to determine drift, which emphasizes the precision of the algorithm. Another characteristic of the proposed method is removal of different numbers of the base classifiers based on their quality. Implementation of a weighting mechanism to the base classifiers at the decision-making stage is another advantage of the algorithm. This facilitates adaptability when drifts take place, which leads to classifiers with higher efficiency. Furthermore, the proposed method is tested on a set of standard data and the results confirm higher accuracy compared to available ensemble classifiers and single classifiers. In addition, in some cases the proposed classifier is faster and needs less storage space.

关键词： Data stream     Classificaion     Ensemble classifiers     Concept drift    

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Shallow foundation response variability due to soil and model parameter uncertainty

Prishati RAYCHOWDHURY,Sumit JINDAL

《结构与土木工程前沿（英文）》 2014年 第8卷 第3期   页码 237-251 doi: 10.1007/s11709-014-0242-1

摘要： Geotechnical uncertainties may play crucial role in response prediction of a structure with substantial soil-foundation-structure-interaction (SFSI) effects. Since the behavior of a soil-foundation system may significantly alter the response of the structure supported by it, and consequently several design decisions, it is extremely important to identify and characterize the relevant parameters. Moreover, the modeling approach and the parameters required for the modeling are also critically important for the response prediction. The present work intends to investigate the effect of soil and model parameter uncertainty on the response of shallow foundation-structure systems resting on dry dense sand. The SFSI is modeled using a beam-on-nonlinear-winkler-foundation (BNWF) concept, where soil beneath the foundation is assumed to be an assembly of discrete, nonlinear elements composed of springs, dashpots and gap elements. The sensitivity of both soil and model input parameters on shallow foundation responses are investigated using first-order second-moment (FOSM) analysis and Monte Carlo simulation through Latin hypercube sampling technique. It has been observed that the degree of accuracy in predicting the responses of the shallow foundation is highly sensitive soil parameters, such as friction angle, Poisson’s ratio and shear modulus, rather than model parameters, such as stiffness intensity ratio and spring spacing; indicating the importance of proper characterization of soil parameters for reliable soil-foundation response analysis.

关键词： shallow foun dation     sensitivity analysis     centrifuge data     first-order-second-moment (FOSM) method     parameter uncertainty    

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Predicting beach profile evolution with group method data handling-type neural networks on beaches with

M. A. LASHTEH NESHAEI, M. A. MEHRDAD, N. ABEDIMAHZOON, N. ASADOLLAHI

《结构与土木工程前沿（英文）》 2013年 第7卷 第2期   页码 117-126 doi: 10.1007/s11709-013-0205-y

摘要： A major goal of coastal engineering is to develop models for the reliable prediction of short- and long-term near shore evolution. The most successful coastal models are numerical models, which allow flexibility in the choice of initial and boundary conditions. In the present study, evolutionary algorithms (EAs) are employed for multi-objective Pareto optimum design of group method data handling (GMDH)-type neural networks that have been used for bed evolution modeling in the surf zone for reflective beaches, based on the irregular wave experiments performed at the Hydraulic Laboratory of Imperial College (London, UK). The input parameters used for such modeling are significant wave height, wave period, wave action duration, reflection coefficient, distance from shoreline and sand size. In this way, EAs with an encoding scheme are presented for evolutionary design of the generalized GMDH-type neural networks, in which the connectivity configurations in such networks are not limited to adjacent layers. Also, multi-objective EAs with a diversity preserving mechanism are used for Pareto optimization of such GMDH-type neural networks. The most important objectives of GMDH-type neural networks that are considered in this study are training error (TE), prediction error (PE), and number of neurons ( ). Different pairs of these objective functions are selected for two-objective optimization processes. Therefore, optimal Pareto fronts of such models are obtained in each case, which exhibit the trade-offs between the corresponding pair of the objectives and, thus, provide different non-dominated optimal choices of GMDH-type neural network model for beach profile evolution. The results showed that the present model has been successfully used to optimally prediction of beach profile evolution on beaches with seawalls.

关键词： beach profile evolution     genetic algorithms     group method of data handling     Pareto     reflective beaches    

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改进的R/S方法与中国火灾数据的分析预测

付昱华,付安捷

《中国工程科学》 2004年 第6卷 第5期   页码 39-44

摘要：

讨论工程和经济学领域中的R/S分析方法（重标极差方法）的若干改进及应用。对于全国火灾起数的分析，计算赫斯特指数H时应用2种新的数据分组方法；引入赫斯特指数的差值ΔH以利于判断下一年的火灾起数是否会激增；对于已计算出的赫斯特指数H进行R/S分析，得到一组新的赫斯特指数H1，即赫斯特指数的赫斯特指数，以及相应的ΔH1，依此类推可以得到高阶赫斯特指数及其差值H2，ΔH2，H3，ΔH3等；根据1950—1999年全国火灾起数，用R/S方法预测2000年全国火灾起数。

关键词： R/S分析     重标极差方法     高阶赫斯特指数     全国火灾起数     预测    

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Answer for questions of repeated measurements of variance analysis and distribution test of data — Authors

《医学前沿（英文）》 2022年 第16卷 第4期   页码 661-664 doi: 10.1007/s11684-021-0907-9

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Comparison of prechilling stratification and sulfuric acid scarification on seed germination of

Nan WANG, Jing GAO, Suiqi ZHANG, Feng YAN

《农业科学与工程前沿（英文）》 2017年 第4卷 第2期   页码 220-227 doi: 10.15302/J-FASE-2017146

摘要： In semi-arid regions of the Loess Plateau, water deficiency restricts plant performance. (switchgrass), which is a highly versatile grass, had been introduced to the Plateau as a restoration species. To determine if prechilling stratification (PCS) and sulfuric acid scarification (SAS) can optimize establishment, cvs Pathfinder, Trailblazer and Alamo were tested under different ambient water potentials by measuring germination and root and shoot growth along water potential gradients under laboratory conditions. Both PCS and SAS improved total germination percentage (TGP), with PCS being more beneficial. The effect of PCS and SAS on mean germination time (MGT) weakened gradually with increasing drought stress. Both PCS and SAS showed no obvious effect on promoting root and shoot growth. Both PCS and SAS reduced base water potential requirement for reaching 50% germination of Pathfinder and Trailblazer, with this effect greater for PCS. These results indicate that embryo dormancy may be a major factor limiting germination of under drought conditions. Pathfinder appears to be more suitable for a semi-arid environment, whereas Alamo appears to be unsuitable for drought conditions. Given the large difference between predicted value and measured value, the reliability and applicable scope of linear regression estimated Y needs further investigation, specification and optimization.

关键词： base water potential     data analysis method     embryo growth     germination    

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efficient two-stage approach for structural damage detection using meta-heuristic algorithms and group methodof data handling surrogate model

Hamed FATHNEJAT, Behrouz AHMADI-NEDUSHAN

《结构与土木工程前沿（英文）》 2020年 第14卷 第4期   页码 907-929 doi: 10.1007/s11709-020-0628-1

摘要： In this study, the performance of an efficient two-stage methodology which is applied in a damage detection system using a surrogate model of the structure has been investigated. In the first stage, in order to locate the damage accurately, the performance of the modal strain energy based index for using different numbers of natural mode shapes has been evaluated using the confusion matrix. In the second stage, to estimate the damage extent, the sensitivity of most used modal properties due to damage, such as natural frequency and flexibility matrix is compared with the mean normalized modal strain energy (MNMSE) of suspected damaged elements. Moreover, a modal property change vector is evaluated using the group method of data handling (GMDH) network as a surrogate model during damage extent estimation by optimization algorithm; in this part of methodology, the performance of the three popular optimization algorithms including particle swarm optimization (PSO), bat algorithm (BA), and colliding bodies optimization (CBO) is examined and in this regard, root mean square deviation ( ) based on the modal property change vector has been proposed as an objective function. Furthermore, the effect of noise in the measurement of structural responses by the sensors has also been studied. Finally, in order to achieve the most generalized neural network as a surrogate model, GMDH performance is compared with a properly trained cascade feed-forward neural network (CFNN) with log-sigmoid hidden layer transfer function. The results indicate that the accuracy of damage extent estimation is acceptable in the case of integration of PSO and MNMSE. Moreover, the GMDH model is also more efficient and mimics the behavior of the structure slightly better than CFNN model.

关键词： two-stage method     modal strain energy     surrogate model     GMDH     optimization damage detection    

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Integrating storm surge modeling with traffic data analysis to evaluate the effectiveness of hurricane

《结构与土木工程前沿（英文）》 2021年 第15卷 第6期   页码 1301-1316 doi: 10.1007/s11709-021-0765-1

摘要： An integrated storm surge modeling and traffic analysis were conducted in this study to assess the effectiveness of hurricane evacuations through a case study of Hurricane Irma. The Category 5 hurricane in 2017 caused a record evacuation with an estimated 6.8 million people relocating statewide in Florida. The Advanced Circulation (ADCIRC) model was applied to simulate storm tides during the hurricane event. Model validations indicated that simulated pressures, winds, and storm surge compared well with observations. Model simulated storm tides and winds were used to estimate the area affected by Hurricane Irma. Results showed that the storm surge and strong wind mainly affected coastal counties in south-west Florida. Only moderate storm tides (maximum about 2.5 m) and maximum wind speed about 115 mph were shown in both model simulations and Federal Emergency Management Agency (FEMA) post-hurricane assessment near the area of hurricane landfall. Storm surges did not rise to the 100-year flood elevation level. The maximum wind was much below the design wind speed of 150–170 mph (Category 5) as defined in Florida Building Code (FBC) for south Florida coastal areas. Compared with the total population of about 2.25 million in the six coastal counties affected by storm surge and Category 1–3 wind, the statewide evacuation of approximately 6.8 million people was found to be an over-evacuation due mainly to the uncertainty of hurricane path, which shifted from south-east to south-west Florida. The uncertainty of hurricane tracks made it difficult to predict the appropriate storm surge inundation zone for evacuation. Traffic data were used to analyze the evacuation traffic patterns. In south-east Florida, evacuation traffic started 4 days before the hurricane’s arrival. However, the hurricane path shifted and eventually landed in south-west Florida, which caused a high level of evacuation traffic in south-west Florida. Over-evacuation caused Evacuation Traffic Index (ETI) to increase to 200% above normal conditions in some sections of highways, which reduced the effectiveness of evacuation. Results from this study show that evacuation efficiency can be improved in the future by more accurate hurricane forecasting, better public awareness of real-time storm surge and wind as well as integrated storm surge and evacuation modeling for quick response to the uncertainty of hurricane forecasting.

关键词： storm surge modeling     traffic     evacuation     Hurricane Irma    

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Effectiveness of state incentives for promoting wind energy: A panel data examination

Deepak SANGROYA,Jogendra NAYAK

《能源前沿（英文）》 2015年 第9卷 第3期   页码 247-258 doi: 10.1007/s11708-015-0364-8

摘要： Over the last decade, India has started to concentrate earnestly on renewable energy. The Indian government, as well as different state governments, are adopting policy instruments such as feed in tariff, captive consumption, renewable purchase obligation and generation based incentive etc. aimed at renewable energy development. This paper evaluates the effectiveness of state level incentives for the development of wind energy in India. Fixed effect panel data modelling technique of econometric analysis is used to analyse the data of 26 Indian states in 11 years. The results show that feed in tariff and captive consumption are the significant predictors of wind energy development. However, renewable purchase obligation does not affect wind energy significantly.

关键词： India     wind energy development     state incentives     econometric analysis     panel data    

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An adaptive data-driven method for accurate prediction of remaining useful life of rolling bearings

Yanfeng PENG, Junsheng CHENG, Yanfei LIU, Xuejun LI, Zhihua PENG

《机械工程前沿（英文）》 2018年 第13卷 第2期   页码 301-310 doi: 10.1007/s11465-017-0449-7

摘要：

A novel data-driven method based on Gaussian mixture model (GMM) and distance evaluation technique (DET) is proposed to predict the remaining useful life (RUL) of rolling bearings. The data sets are clustered by GMM to divide all data sets into several health states adaptively and reasonably. The number of clusters is determined by the minimum description length principle. Thus, either the health state of the data sets or the number of the states is obtained automatically. Meanwhile, the abnormal data sets can be recognized during the clustering process and removed from the training data sets. After obtaining the health states, appropriate features are selected by DET for increasing the classification and prediction accuracy. In the prediction process, each vibration signal is decomposed into several components by empirical mode decomposition. Some common statistical parameters of the components are calculated first and then the features are clustered using GMM to divide the data sets into several health states and remove the abnormal data sets. Thereafter, appropriate statistical parameters of the generated components are selected using DET. Finally, least squares support vector machine is utilized to predict the RUL of rolling bearings. Experimental results indicate that the proposed method reliably predicts the RUL of rolling bearings.

关键词： Gaussian mixture model     distance evaluation technique     health state     remaining useful life     rolling bearing    

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A study on specialist or special disease clinics based on big data

null

《医学前沿（英文）》 2014年 第8卷 第3期   页码 376-381 doi: 10.1007/s11684-014-0356-9

摘要：

Correlation analysis and processing of massive medical information can be implemented through big data technology to find the relevance of different factors in the life cycle of a disease and to provide the basis for scientific research and clinical practice. This paper explores the concept of constructing a big medical data platform and introduces the clinical model construction. Medical data can be collected and consolidated by distributed computing technology. Through analysis technology, such as artificial neural network and grey model, a medical model can be built. Big data analysis, such as Hadoop, can be used to construct early prediction and intervention models as well as clinical decision-making model for specialist and special disease clinics. It establishes a new model for common clinical research for specialist and special disease clinics.

关键词： big data     correlation analysis     medical information     integration     data analysis     clinical model    

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Industrial eco-efficiency and its spatial-temporal differentiation in China

Wei YANG, Fengjun JIN, Chengjin WANG, Chen LV

《环境科学与工程前沿（英文）》 2012年 第6卷 第4期   页码 559-568 doi: 10.1007/s11783-012-0400-4

摘要： The aim of this paper is to study the spatial-temporal differentiation of industrial eco-efficiency in China. Using methods based on the data envelopment analysis (DEA) model and exploratory spatial data analysis (ESDA) and data from 1985, 1995, 2005, and 2008 of 30 provinces in China, the spatial-temporal pattern changes in industrial eco-efficiency are discussed. The results show that: first, the patterns of industrial eco-efficiency are dominated by clustering of relatively low efficiency provinces; second, spatial relationships between the industrial eco-efficiencies of different provinces changed slightly throughout the period and the provinces persistently exhibit spatial concentration of relatively low industrial eco-efficiency; finally, there is an obvious trend in the polarization of industrial eco-efficiency, i.e., the higher level spatial units are concentrated in eastern China, and the lower level spatial units are mainly in western and central China.

关键词： industrial eco-efficiency     data envelopment analysis (DEA) model     exploratory spatial data analysis (ESDA)    

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Static-based early-damage detection using symbolic data analysis and unsupervised learning methods

João Pedro SANTOS,Christian CREMONA,André D. ORCESI,Paulo SILVEIRA,Luis CALADO

《结构与土木工程前沿（英文）》 2015年 第9卷 第1期   页码 1-16 doi: 10.1007/s11709-014-0277-3

摘要： A large amount of researches and studies have been recently performed by applying statistical and machine learning techniques for vibration-based damage detection. However, the global character inherent to the limited number of modal properties issued from operational modal analysis may be not appropriate for early-damage, which has generally a local character. The present paper aims at detecting this type of damage by using static SHM data and by assuming that early-damage produces dead load redistribution. To achieve this objective a data driven strategy is proposed, consisting of the combination of advanced statistical and machine learning methods such as principal component analysis, symbolic data analysis and cluster analysis. From this analysis it was observed that, under the noise levels measured on site, the proposed strategy is able to automatically detect stiffness reduction in stay cables reaching at least 1%.

关键词： structural health monitoring     early-damage detection     principal component analysis     symbolic data     symbolic dissimilarity measures     cluster analysis     numerical model     damage simulations    

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Characteristics of plankton Hg bioaccumulations based on a global data set and the implications for aquatic

《环境科学与工程前沿（英文）》 2022年 第16卷 第3期 doi: 10.1007/s11783-021-1471-x

摘要：

• Hg bioaccumulation by phytoplankton varies among aquatic ecosystems.

关键词： Plankton     Hg bioaccumulation     Physiological characteristics     A cross-system analysis     Nutrient compositions     Global data set    

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标题 作者 时间 类型 操作

Slope stability analysis based on big data and convolutional neural network

Yangpan FU; Mansheng LIN; You ZHANG; Gongfa CHEN; Yongjian LIU

期刊论文

Anensemble method for data stream classification in the presence of concept drift

Omid ABBASZADEH,Ali AMIRI,Ali Reza KHANTEYMOORI

期刊论文

Shallow foundation response variability due to soil and model parameter uncertainty

Prishati RAYCHOWDHURY,Sumit JINDAL

期刊论文

Predicting beach profile evolution with group method data handling-type neural networks on beaches with

M. A. LASHTEH NESHAEI, M. A. MEHRDAD, N. ABEDIMAHZOON, N. ASADOLLAHI

期刊论文

改进的R/S方法与中国火灾数据的分析预测

付昱华,付安捷

期刊论文

Answer for questions of repeated measurements of variance analysis and distribution test of data — Authors

期刊论文

Comparison of prechilling stratification and sulfuric acid scarification on seed germination of

Nan WANG, Jing GAO, Suiqi ZHANG, Feng YAN

期刊论文

efficient two-stage approach for structural damage detection using meta-heuristic algorithms and group methodof data handling surrogate model

Hamed FATHNEJAT, Behrouz AHMADI-NEDUSHAN

期刊论文

Integrating storm surge modeling with traffic data analysis to evaluate the effectiveness of hurricane

期刊论文

Effectiveness of state incentives for promoting wind energy: A panel data examination

Deepak SANGROYA,Jogendra NAYAK

期刊论文

An adaptive data-driven method for accurate prediction of remaining useful life of rolling bearings

Yanfeng PENG, Junsheng CHENG, Yanfei LIU, Xuejun LI, Zhihua PENG

期刊论文

A study on specialist or special disease clinics based on big data

null

期刊论文

Industrial eco-efficiency and its spatial-temporal differentiation in China

Wei YANG, Fengjun JIN, Chengjin WANG, Chen LV

期刊论文

Static-based early-damage detection using symbolic data analysis and unsupervised learning methods

João Pedro SANTOS,Christian CREMONA,André D. ORCESI,Paulo SILVEIRA,Luis CALADO

期刊论文

Characteristics of plankton Hg bioaccumulations based on a global data set and the implications for aquatic

期刊论文