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Unconfined compressive strength prediction of soils stabilized using artificial neural networks and supportvector machines

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

《结构与土木工程前沿（英文）》 2021年 第15卷 第2期   页码 520-536 doi: 10.1007/s11709-021-0689-9

摘要： This study aims to improve the unconfined compressive strength of soils using additives as well as by predicting the strength behavior of stabilized soils using two artificial-intelligence-based models. The soils used in this study are stabilized using various combinations of cement, lime, and rice husk ash. To predict the results of unconfined compressive strength tests conducted on soils, a comprehensive laboratory dataset comprising 137 soil specimens treated with different combinations of cement, lime, and rice husk ash is used. Two artificial-intelligence-based models including artificial neural networks and support vector machines are used comparatively to predict the strength characteristics of soils treated with cement, lime, and rice husk ash under different conditions. The suggested models predicted the unconfined compressive strength of soils accurately and can be introduced as reliable predictive models in geotechnical engineering. This study demonstrates the better performance of support vector machines in predicting the strength of the investigated soils compared with artificial neural networks. The type of kernel function used in support vector machine models contributed positively to the performance of the proposed models. Moreover, based on sensitivity analysis results, it is discovered that cement and lime contents impose more prominent effects on the unconfined compressive strength values of the investigated soils compared with the other parameters.

关键词： unconfined compressive strength     artificial neural network     support vector machine     predictive models     regression    

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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    

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Water quality prediction of copper-molybdenum mining-beneficiation wastewater based on the PSO-SVR model

《环境科学与工程前沿（英文）》 2023年 第17卷 第8期 doi: 10.1007/s11783-023-1698-9

摘要：

● Data acquisition and pre-processing for wastewater treatment were summarized.

关键词： Chemical oxygen demand     Mining-beneficiation wastewater treatment     Particle swarm optimization     Support vector regression     Artificial neural network    

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Timing decision-making method of engine blades for predecisional remanufacturing based on reliability analysis

Le CHEN, Xianlin WANG, Hua ZHANG, Xugang ZHANG, Binbin DAN

《机械工程前沿（英文）》 2019年 第14卷 第4期   页码 412-421 doi: 10.1007/s11465-019-0551-0

摘要： A timing decision-making method for predecisional remanufacturing is presented. The method can effectively solve the uncertainty problem of remanufacturing blanks. From the perspective of reliability, this study analyzes the timing decision-making interval for predecisional remanufacturing of mechanical products during the service period and constructs an optimal timing model based on energy consumption and cost. The mapping relationships between time and energy consumption are predicted by using the characteristic values of performance degradation of products combined with the least squares support vector regression algorithm. Application of game theory reveals that when the energy consumption and cost are comprehensively optimal, this moment is the best time for predecisional remanufacturing. Used engine blades are utilized as an example to demonstrate the validity and effectiveness of the proposed method.

关键词： predecisional remanufacturing     reliability     least squares support vector regression (LS-SVR)     game theory    

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用于电网节点重要度评估的一种基于网络嵌入和支持向量回归的人工智能方法 Research Papers

Hui-fang WANG, Chen-yu ZHANG, Dong-yang LIN, Ben-teng HE

《信息与电子工程前沿（英文）》 2019年 第20卷 第6期   页码 816-828 doi: 10.1631/FITEE.1800146

摘要： 重要节点识别对电网安全意义重大。但电网在规模、结构等方面差异较大，评价指标难以涵盖电网不同状态下所有信息，因此基于指标构建的传统评估方法，其效果视情况而定，通用性不足。由此，本文提出基于人工智能的电网节点重要度评估法。首先利用网络嵌入，提出综合考虑电网结构与电气量的电网节点特征选择法。然后对具体电网，进行各类运行方式下的稳态与节点故障暂态仿真，构建能反映节点特征与节点重要度内在关系的样本集。最后，根据优化后的样本集训练支持向量回归模型，模型成熟后可用于电网节点重要度在线评估。结果表明，本方法能根据从样本中学到的信息有效评估电网节点重要度。相比传统指标构建法，本方法规避了片面性和主观性。此外，基于该人工智能框架，本方法可针对每个具体电网建立特定样本集，具有通用性。

关键词： 电网；人工智能；节点重要度；TADW法；网络嵌入；支持向量回归    

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基于回归预测集成学习的交互式图像分割 Article

Jin ZHANG, Zhao-hui TANG, Wei-hua GUI, Qing CHEN, Jin-ping LIU

《信息与电子工程前沿（英文）》 2017年 第18卷 第7期   页码 1002-1020 doi: 10.1631/FITEE.1601401

摘要： 首先，基于已标记样本训练出两个在属性上互补的多元自适应回归样条学习器（multivariate adaptive regression splines, MARS）和薄板样条回归学习器（thin platespline regression, TPSR）；接着，提出一种基于聚类假设和半监督学习的回归器增强算法，该算法从未标记样本中抽选部分样本辅助训练MARS和TPSR；然后，引入支持向量回归方法（supportvector regression, SVR）集成MARS和TPSR的预测结果；最后，对SVR集成结果进行GraphCut图像分割。

关键词： 交互式图像分割；多元自适应回归样条；集成学习；薄板样条回归；半监督学习；支持向量回归    

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Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network and supportvector machine

Ali Reza GHANIZADEH, Hakime ABBASLOU, Amir Tavana AMLASHI, Pourya ALIDOUST

《结构与土木工程前沿（英文）》 2019年 第13卷 第1期   页码 215-239 doi: 10.1007/s11709-018-0489-z

摘要： Plastic concrete is an engineering material, which is commonly used for construction of cut-off walls to prevent water seepage under the dam. This paper aims to explore two machine learning algorithms including artificial neural network (ANN) and support vector machine (SVM) to predict the compressive strength of bentonite/sepiolite plastic concretes. For this purpose, two unique sets of 72 data for compressive strength of bentonite and sepiolite plastic concrete samples (totally 144 data) were prepared by conducting an experimental study. The results confirm the ability of ANN and SVM models in prediction processes. Also, Sensitivity analysis of the best obtained model indicated that cement and silty clay have the maximum and minimum influences on the compressive strength, respectively. In addition, investigation of the effect of measurement error of input variables showed that change in the sand content (amount) and curing time will have the maximum and minimum effects on the output mean absolute percent error (MAPE) of model, respectively. Finally, the influence of different variables on the plastic concrete compressive strength values was evaluated by conducting parametric studies.

关键词： bentonite/sepiolite plastic concrete     compressive strength     artificial neural network     support vector machine     parametric analysis    

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Diagnosis of sewer pipe defects on image recognition of multi-features and support vector machine in

Xiangyang Ye, Jian’e Zuo, Ruohan Li, Yajiao Wang, Lili Gan, Zhonghan Yu, Xiaoqing Hu

《环境科学与工程前沿（英文）》 2019年 第13卷 第2期 doi: 10.1007/s11783-019-1102-y

摘要：

An image-recognition-based diagnosis system of pipe defect types was established.

1043 practical pipe images were gathered by CCTV robot in a southern Chinese city.

The overall accuracy of the system is 84% and the highest accuracy is 99.3%.

The accuracy shows positive correlation to the number of training samples.

关键词： Sewer pipe defects     Defect diagnosing     Image recognition     Multi-features extraction     Support vector machine    

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基于核空间非线性特征提取的图像质量评价方法 Article

Yong DING,Nan LI,Yang ZHAO,Kai HUANG

《信息与电子工程前沿（英文）》 2016年 第17卷 第10期   页码 1008-1017 doi: 10.1631/FITEE.1500439

摘要： 概要：在实现对与人类视觉感知相一致的图像质量的客观评价中，如何提取图像的视觉感知特征至关重要。不同于传统方法中通过线性变换或模型表达图像的方式，本文采用高维空间的一种数学表达来揭示图像的统计特性，通过引入核独立分量分析（kernel independent component analysis, KICA）方法实现非线性转化和图像的高维特征提取。从而提出一种基于非线性特征提取的全参考图像质量评价方法。在LIVE、TID2008和CSIQ等图像质量评价数据库上的实验结果表明，图像的非线性特征更有利于图像内在质量的描述，并且本文提出的方法性能良好，与主观评价较为一致。

关键词： 图像质量评价；全参考方法；特征提取；核空间；支持向量回归    

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Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics

Gurmanik KAUR,Ajat Shatru ARORA,Vijender Kumar JAIN

《信息与电子工程前沿（英文）》 2015年 第16卷 第6期   页码 474-485 doi: 10.1631/FITEE.1400295

摘要： Accurate blood pressure (BP) measurement is essential in epidemiological studies, screening programmes, and research studies as well as in clinical practice for the early detection and prevention of high BP-related risks such as coronary heart disease, stroke, and kidney failure. Posture of the participant plays a vital role in accurate measurement of BP. Guidelines on measurement of BP contain recommendations on the position of the back of the participants by advising that they should sit with supported back to avoid spuriously high readings. In this work, principal component analysis (PCA) is fused with forward stepwise regression (SWR), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and the least squares support vector machine (LS-SVM) model for the prediction of BP reactivity to an unsupported back in normotensive and hypertensive participants. PCA is used to remove multi-collinearity among anthropometric predictor variables and to select a subset of components, termed ‘principal components’ (PCs), from the original dataset. The selected PCs are fed into the proposed models for modeling and testing. The evaluation of the performance of the constructed models, using appropriate statistical indices, shows clearly that a PCA-based LS-SVM (PCA-LS-SVM) model is a promising approach for the prediction of BP reactivity in comparison to others. This assessment demonstrates the importance and advantages posed by hybrid models for the prediction of variables in biomedical research studies.

关键词： Blood pressure (BP)     Principal component analysis (PCA)     Forward stepwise regression     Artificial neural network (ANN)     Adaptive neuro-fuzzy inference system (ANFIS)     Least squares support vector machine (LS-SVM)    

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最小二乘支持向量机的扩展及其在时间序列预测中的应用

向小东

《中国工程科学》 2008年 第10卷 第11期   页码 89-92

摘要：

根据时间序列近期数据较远期数据包含有更多未来信息的思想，对最小二乘支持向量机预测方法进行了扩展，得到了更具一般性的最小二乘支持向量机预测模型，给出了扩展后的预测模型具体算法。两个时间序列的预测实例表明，扩展后的预测方法获得了更好的预测效果，提升了最小二乘支持向量机预测方法的价值。

关键词： 最小二乘支持向量机     扩展     时间序列     预测    

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基于随机森林模型的滑动轨迹人机识别 Research Articles

Zhen-yi XU, Yu KANG, Yang CAO, Yu-xiao YANG

《信息与电子工程前沿（英文）》 2019年 第20卷 第7期   页码 925-929 doi: 10.1631/FITEE.1700442

摘要： 识别码在维护网络安全的人机身份验证中得到广泛应用。人机身份验证面临的挑战包括对人与机器滑动轨迹的正确检测。提出一种基于滑动轨迹数据集的人机识别随机森林模型。通过多维性能评价指标，包括识别准确率、识别召回率、识别误报率、识别漏报率、F值和加权准确率，验证该随机森林模型以及基准模型（逻辑回归模型和支持向量机）。随机森林模型多维性能评价指标优于基准模型。

关键词： 人机识别；随机森林；支持向量机；逻辑回归；多维性能评价指标    

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运用支持向量机的稳健智能音频水印设计 Article

Mohammad MOSLEH,Hadi LATIFPOUR,Mohammad KHEYRANDISH,Mahdi MOSLEH,Najmeh HOSSEINPOUR

《信息与电子工程前沿（英文）》 2016年 第17卷 第12期   页码 1320-1330 doi: 10.1631/FITEE.1500297

摘要： 本文提出了一种稳健、智能的音频水印方法，该方法有效地结合了奇异值分解（Singular value decomposition, SVD）和支持向量机（Support vector machine, SVM

关键词： 音频水印；版权保护；奇异值分解；机器学习；支持向量机    

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A comprehensive review and analysis of solar forecasting techniques

Pardeep SINGLA, Manoj DUHAN, Sumit SAROHA

《能源前沿（英文）》 2022年 第16卷 第2期   页码 187-223 doi: 10.1007/s11708-021-0722-7

摘要： In the last two decades, renewable energy has been paid immeasurable attention to toward the attainment of electricity requirements for domestic, industrial, and agriculture sectors. Solar forecasting plays a vital role in smooth operation, scheduling, and balancing of electricity production by standalone PV plants as well as grid interconnected solar PV plants. Numerous models and techniques have been developed in short, mid and long-term solar forecasting. This paper analyzes some of the potential solar forecasting models based on various methodologies discussed in literature, by mainly focusing on investigating the influence of meteorological variables, time horizon, climatic zone, pre-processing techniques, air pollution, and sample size on the complexity and accuracy of the model. To make the paper reader-friendly, it presents all-important parameters and findings of the models revealed from different studies in a tabular mode having the year of publication, time resolution, input parameters, forecasted parameters, error metrics, and performance. The literature studied showed that ANN-based models outperform the others due to their nonlinear complex problem-solving capabilities. Their accuracy can be further improved by hybridization of the two models or by performing pre-processing on the input data. Besides, it also discusses the diverse key constituents that affect the accuracy of a model. It has been observed that the proper selection of training and testing period along with the correlated dependent variables also enhances the accuracy of the model.

关键词： forecasting techniques     hybrid models     neural network     solar forecasting     error metric     support vector machine (SVM)    

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LSSVM-based approach for refining soil failure criteria and calculating safety factor of slopes

Shiguo XIAO; Shaohong LI

《结构与土木工程前沿（英文）》 2022年 第16卷 第7期   页码 871-881 doi: 10.1007/s11709-022-0863-8

摘要： The failure criteria of practical soil mass are very complex, and have significant influence on the safety factor of slope stability. The Coulomb strength criterion and the power-law failure criterion are classically simplified. Each one has limited applicability owing to the noticeable difference between calculated predictions and actual results in some cases. In the work reported here, an analysis method based on the least square support vector machine (LSSVM), a machine learning model, is purposefully provided to establish a complex nonlinear failure criterion via iteration computation based on strength test data of the soil, which is of more extensive applicability to many problems of slope stability. In particular, three evaluation indexes including coefficient of determination, mean absolute percentage error, and mean square error indicate that fitting precision of the machine learning-based failure criterion is better than those of the linear Coulomb criterion and nonlinear power-law criterion. Based on the proposed LSSVM approach to determine the failure criterion, the limit equilibrium method can be used to calculate the safety factor of three-dimensional slope stability. Analysis of results of the safety factor of two three-dimensional homogeneous slopes shows that the maximum relative errors between the proposed approach and the linear failure criterion-based method and the power-law failure criterion-based method are about 12% and 7%, respectively.

关键词： slope stability     safety factor     failure criterion     least square support vector machine    

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标题 作者 时间 类型 操作

Unconfined compressive strength prediction of soils stabilized using artificial neural networks and supportvector machines

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

期刊论文

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

Pijush SAMUI

期刊论文

Water quality prediction of copper-molybdenum mining-beneficiation wastewater based on the PSO-SVR model

期刊论文

Timing decision-making method of engine blades for predecisional remanufacturing based on reliability analysis

Le CHEN, Xianlin WANG, Hua ZHANG, Xugang ZHANG, Binbin DAN

期刊论文

用于电网节点重要度评估的一种基于网络嵌入和支持向量回归的人工智能方法

Hui-fang WANG, Chen-yu ZHANG, Dong-yang LIN, Ben-teng HE

期刊论文

基于回归预测集成学习的交互式图像分割

Jin ZHANG, Zhao-hui TANG, Wei-hua GUI, Qing CHEN, Jin-ping LIU

期刊论文

Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network and supportvector machine

Ali Reza GHANIZADEH, Hakime ABBASLOU, Amir Tavana AMLASHI, Pourya ALIDOUST

期刊论文

Diagnosis of sewer pipe defects on image recognition of multi-features and support vector machine in

Xiangyang Ye, Jian’e Zuo, Ruohan Li, Yajiao Wang, Lili Gan, Zhonghan Yu, Xiaoqing Hu

期刊论文

基于核空间非线性特征提取的图像质量评价方法

Yong DING,Nan LI,Yang ZHAO,Kai HUANG

期刊论文

Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics

Gurmanik KAUR,Ajat Shatru ARORA,Vijender Kumar JAIN

期刊论文

最小二乘支持向量机的扩展及其在时间序列预测中的应用

向小东

期刊论文

基于随机森林模型的滑动轨迹人机识别

Zhen-yi XU, Yu KANG, Yang CAO, Yu-xiao YANG

期刊论文

运用支持向量机的稳健智能音频水印设计

Mohammad MOSLEH,Hadi LATIFPOUR,Mohammad KHEYRANDISH,Mahdi MOSLEH,Najmeh HOSSEINPOUR

期刊论文

A comprehensive review and analysis of solar forecasting techniques

Pardeep SINGLA, Manoj DUHAN, Sumit SAROHA

期刊论文

LSSVM-based approach for refining soil failure criteria and calculating safety factor of slopes

Shiguo XIAO; Shaohong LI

期刊论文