Application of Artificial Intelligence to Maximum Dry Density and Unconfined Compressive Strength of Cement Stabilized Soil

This paper describes two artificial intelligence techniques for prediction of maximum dry density (MDD) and unconfined compressive strength (UCS) of cement stabilized soil. The first technique uses various artificial neural network (ANN) models such as Bayesian regularization method (BRNN), Levenberg- Marquardt algorithm (LMNN) and differential evolution algorithm (DENN). The second technique uses the support vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses regression technique by introducing ε-insensitive loss function has been adopted. The inputs of both models are liquid limit (LL), plasticity index (PI), clay fraction (CF)%, sand (S)%, gravel Gr (%), moisture content (MC) and cement content (Ce). The sensitivity analyses of the input parameters have been also done for both models. Based on different statistical criteria the SVM models are found to be better than ANN models for the prediction of MDD and UCS of cement stabilized soil.     

Intelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms

Compression index Ccis an essential parameter in geotechnical design for which the effectiveness of correlation is still a challenge.This paper suggests a novel modelling approach using machine learning(ML)technique.The performance of five commonly used machine learning(ML)algorithms,i.e.back-propagation neural network(BPNN),extreme learning machine(ELM),support vector machine(SVM),random forest(RF)and evolutionary polynomial regression(EPR)in predicting Cc is comprehensively investigated.A database with a total number of 311 datasets including three input variables,i.e.initial void ratio e0,liquid limit water content wL,plasticity index Ip,and one output variable Cc is first established.Genetic algorithm(GA)is used to optimize the hyper-parameters in five ML algorithms,and the average prediction error for the 10-fold cross-validation(CV)sets is set as thefitness function in the GA for enhancing the robustness of ML models.The results indicate that ML models outperform empirical prediction formulations with lower prediction error.RF yields the lowest error followed by BPNN,ELM,EPR and SVM.If the ranges of input variables in the database are large enough,BPNN and RF models are recommended to predict Cc.Furthermore,if the distribution of input variables is continuous,RF model is the best one.Otherwise,EPR model is recommended if the ranges of input variables are small.The predicted correlations between input and output variables using five ML models show great agreement with the physical explanation.     

基于极限学习机的GF-2影像分类

遥感图像分类是提取图像有效信息过程中重要的一部分,为了探寻最优的分类方法,许多机器学习算法逐步应用于遥感分类中。极限学习机（extreme learning machine,ELM）以其高效、快速和良好的泛化性能在模式识别领域得到广泛应用。本文采用训练速度快、运算量小的极限学习机算法与支持向量机（support vector machines,SVM）算法和最大似然法进行分类对比,对高分辨率遥感图像进行分类,分析极限学习机算法对于遥感图像分类的准确度等性能。选取吉林省长春市部分区域的GF-2遥感数据,将融合后的影像设置为原始数据,利用3种方法进行分类。研究结果表明,极限学习机算法分类图像总体分类精度达到85%以上,kappa系数达到0.718,与其他分类方法相比分类准确度较高,且极限学习机运行时间比支持向量机运行时间约短2 480 s,约为支持向量机运行时间的1/8,因此具有良好的性能和实用价值。     

Prediction of Safe Charge to Protect Surrounding Structures Using Support Vector Machine

The present paper mainly with deals the prediction of safe explosive charge used per delay (Q_MAX) using support vector machine (SVM) incorporating peak particle velocity (PPV) and distance between blast face to monitoring point (D). 150 blast vibration data sets were monitored at different vulnerable and strategic locations in and around a major coal producing opencast coal mines in India. 120 blast vibrations records were used for the training of the SVM model vis-à-vis to determine site constants of various conventional vibration predictors. Rest 30 new randomly selected data sets were used to compare the SVM prediction results with widely used conventional predictors. Results were compared based on coefficient of correlation (R) between measured and predicted values of safe charge of explosive used per delay (Q_MAX). It was found that coefficient of correlation between measured and predicted Q_MAX by SVM was 0.997, whereas it was ranging from 0.063 to 0.872 by different conventional predictor equations.     

Active and online prediction of BOD5 in river systems using reduced-order support vector machine

Due to the limitations of hardware sensors for online measurement of the water quality parameters such as 5-day biochemical oxygen demand (BOD₅), the recent research efforts have focused on the software sensors for the rapid prediction of such parameters. The main objective in this research is to develop a reduced-order support vector machine (ROSVM) model based on the proper orthogonal decomposition to solve the time-consuming problem of the BOD₅ measurements. The performance of the newly developed methodology is tested on the Sefidrood River Basin, Iran. Subsequently, the predicted values of BOD₅, resulted from the selected developed ROSVM model, are compared with the results of support vector machine (SVM) model. According to the obtained results, selected ROSVM model seems to be more accurate, showing Person correlation coefficient (R) and root mean square error (RMSE) equal to 0.97 and 6.94, respectively. Further, the investigations based on developed discrepancy ratio (DDR) statistic for selection of the optimum model between the best accurate ROSVM and SVM models are carried out. Results of DDR statistic indicated superior performance of the selected ROSVM model comparing to the SVM technique for online prediction of BOD₅ in the Sefidrood River.     

集成极限学习机的中小河流洪水预报方法研究

为利用水文现象相似性和极限学习机(ELM)集成学习提高洪水预报精度,提出了一种基于相似度匹配的集成ELM洪水预报方法(SM-ELM)。方法首先从多个ELM模型中,为每一个训练样本找到最优的ELM模型,然后从训练集中,为测试样本匹配出最相似的前k个训练样本,最后利用这k个训练样本分别对应的最优ELM模型,对测试样本采用加权平均法进行集成预报。为证明提出方法的可行性和有效性,以昌化流域的历史洪水为例进行了验证。结果表明,相对于单个ELM,集成ELM模型能有效地提高预测精度。从均方根误差上看,集成ELM模型性能比单个ELM模型提升了10%~15%。在三种集成方法中,SM-ELM能够以较少的模型数量获得较高且稳定的预报精度。     

Landslide susceptibility assessment using object mapping units,decision tree,and support vector machine models in the Three Gorges of China

Due to the particular geographical location and complex geological conditions, the Three Gorges of China suffer from many landslide hazards that often result in tragic loss of life and economic devastation. To reduce the casualty and damages, an effective and accurate method of assessing landslide susceptibility is necessary. Object-based data mining methods were applied to a case study of landslide susceptibility assessment on the Guojiaba Town of the Three Gorges. The study area was partitioned into object mapping units derived from 30 m resolution Landsat TM images using multi-resolution segmentation algorithm based on the landslide factors of engineering rock group, homogeneity, and reservoir water level. Landslide locations were determined by interpretation of Landsat TM images and extensive field surveys. Eleven primary landslide-related factors were extracted from the topographic and geologic maps, and satellite images. Those factors were selected as independent variables using significance testing and correlation coefficient analysis, including slope, profile curvature, engineering rock group, slope structure, distance from faults, land cover, tasseled cap transformation wetness index, reservoir water level, homogeneity, and first and second principal components of the images. Decision tree and support vector machine (SVM) models with the optimal parameters were trained and then used to map landslide susceptibility, respectively. The analytical results were validated by comparing them with known landslides using the success rate and prediction rate curves and classification accuracy. The object-based SVM model has the highest correct rate of 89.36 % and a kappa coefficient of 0.8286 and outperforms the pixel-based SVM, object-based C5.0, and pixel-based SVM models.     

基于邻域粗糙集和支持向量机的固结系数预测

采用邻域粗糙集和支持向量机建立滹沱河某地区软土固结系数的预测模型。基于自行改装的渗透固结仪,利用公式法确定不同压力下的固结系数。通过室内试验确定土体的指标参数,采用邻域粗糙集对该指标参数进行属性约简,将约简后的指标参数作为影响因素,分别建立支持向量机和神经网络的固结系数预测模型,预测未知样本的固结系数,并与实测值进行对比。结果表明:公式法可以准确客观地确定固结系数;支持向量机和BP神经网络建立的该地区软土固结系数预测模型均可以预测区域内未知点的固结系数,且支持向量机方法的预测精度比神经网络方法的预测精度提高了约10%。本文提出的方法直接从实验数据出发,通过易获取的影响因素建立特定地区固结系数预测模型,并可预测该区域其余未知点的固结系数。     

Comparison of landslide susceptibility mapping methodologies for Koyulhisar,Turkey: conditional probability,logistic regression,artificial neural networks,and support vector machine

This case study presented herein compares the GIS-based landslide susceptibility mapping methods such as conditional probability (CP), logistic regression (LR), artificial neural networks (ANNs) and support vector machine (SVM) applied in Koyulhisar (Sivas, Turkey). Digital elevation model was first constructed using GIS software. Landslide-related factors such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index, stream power index, normalized difference vegetation index, distance from settlements and roads were used in the landslide susceptibility analyses. In the last stage of the analyses, landslide susceptibility maps were produced from ANN, CP, LR, SVM models, and they were then compared by means of their validations. However, area under curve values obtained from all four methodologies showed that the map obtained from ANN model looks like more accurate than the other models, accuracies of all models can be evaluated relatively similar. The results also showed that the CP is a simple method in landslide susceptibility mapping and highly compatible with GIS operating features. Susceptibility maps can be easily produced using CP, because input process, calculation and output processes are very simple in CP model when compared with the other methods considered in this study.     

Numerical Analysis of Elastoplastic Behavior of Stone Column Foundation

This paper deals with the influence of the earth pressure at rest coefficient K ₀ on stone columns behavior. This parameter is related to the initial stresses caused by the compression of in situ soil and by the installation (expansion of column material). Numerical calculations based on finite element method are compared to analytical methods and conventional engineering methods addressed to the design of stone columns. The presented results led to a better comprehension of previous methods for the design of stone column and highlighted the importance of well defining the initial stress level due to stone columns installation.     

基于高分遥感影像的泥石流潜在形成区识别方法探讨

提前对泥石流可能发生和造成影响的区域进行预测和防范,一直是地质灾害预测中的重要课题。为充分发挥国产高分影像的空间分辨率优势,利用NNDiffuse和Gram-Schmidt两种融合方法实现多光谱和全色波段的融合并作为研究数据,结合常用的支持向量机（SVM）和基于土壤亮度指数特征的动态聚类（ISODATA）两种分类方法对泥石流潜在形成区的自然地表覆盖和人类影响区域进行识别和提取,再利用泥石流隐患沟和集水区的空间和属性关系预测泥石流形成区。研究表明,不同融合方法会对泥石流形成区的预测产生影响,本文基于NNDiffuse融合方法进行预测的总体效果最好;SVM方法有最好的效果,表明先验知识对预测形成区的重要意义,但无先验知识的ISODATA方法结合有效的指数特征在泥石流形成区识别和预测中有较好的表现,预期未来能在测绘部门有很大的应用潜力。     

Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines,artificial neural networks,kernel logistic regression,and logistic model tree

Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping.     

Landslide identification using machine learning

Landslide identification is critical for risk assessment and mitigation.This paper proposes a novel machinelearning and deep-learning method to identify natural-terrain landslides using integrated geodatabases.First,landslide-related data are compiled,including topographic data,geological data and rainfall-related data.Then,three integrated geodatabases are established;namely,Recent Landslide Database(Rec LD),Relict Landslide Database(Rel LD)and Joint Landslide Database(JLD).After that,five machine learning and deep learning algorithms,including logistic regression(LR),support vector machine(SVM),random forest(RF),boosting methods and convolutional neural network(CNN),are utilized and evaluated on each database.A case study in Lantau,Hong Kong,is conducted to demonstrate the application of the proposed method.From the results of the case study,CNN achieves an identification accuracy of 92.5%on Rec LD,and outperforms other algorithms due to its strengths in feature extraction and multi dimensional data processing.Boosting methods come second in terms of accuracy,followed by RF,LR and SVM.By using machine learning and deep learning techniques,the proposed landslide identification method shows outstanding robustness and great potential in tackling the landslide identification problem.     

Development of machine vision-based ore classification model using support vector machine (SVM) algorithm

The product of the mining industry (ore) is considered to be the raw material for the metal industry. The destination policy of the raw materials of iron mine is highly dependent on the class of iron ores. Thus, regular monitoring of iron ore class is the urgent need at the mine for accurately assigning the destination policy of raw materials. In most of the iron ore mines, decisions on ore class are made based on either visual inspection by the geologist or laboratory analyses of the ores. This process of ore class estimation is time consuming and also challenging for continuous monitoring. Thus, the present study attempts to develop an online vision-based technology for classification of iron ores. A laboratory-scale transportation system is designed using conveyor belt for online image acquisition. A multiclass support vector machine (SVM) model was developed to classify the iron ores. A total of 2200 images were captured for developing the ore classification model. A set of 18 features (9-histogram-based colour features in red, green and blue (RGB) colour space and 9-texture features based on intensity (I) component of hue, saturation and intensity (HSI) colour space) were extracted from each image. The performance of the SVM model was evaluated using four confusion matrix parameters (sensitivity, accuracy, misclassification and specificity). The SVM model performance was also compared with the other methods like K-nearest neighbour, classification discriminant, Naïve Bayes, classification tree and probabilistic neural network. It was observed that the SVM classification model performs better than the other classification methods.     

Introducing a new approach of geochemical anomaly intensity index (GAII) for increasing the probability of exploration of shear zone gold mineralization

Increasing the prediction rate in the identification of mineralization zones using the stream sediment geochemical data is an essential issue in the regional exploration stage. The various univariate (such as fractal and probability plot (PP) methods) and multivariate methods (such as principal component analysis (PCA)) have been performed for interpreting the geochemical data and detecting the mineralization areas. In this study, a new geochemical criterion named geochemical anomaly intensity index (GAII) was proposed for geochemical anomaly mapping. This approach was developed based on the PCA method and the catchment basin coefficient (CBC). The GAII as a weighted geochemical index is calculated using the mineralization principal component (MPC) scores and CBC. GAII can be mapped and utilized for geochemical anomaly mapping and detecting the mineralization areas. Besides, GAII can identify paragenesis elements better than the current methods. In this research, GAII was successfully used to generate geochemical anomaly maps on shear zone gold mineralization in the southwest of Saqqez, NW Iran. The geochemical data have been divided into three groups based on catchment basins and the host rock type. Then the MPCs and paragenesis elements of Au mineralization have been obtained individually using PCA. Three mineralization paragenesis groups consisting of (Au, Sn), (Au, W), and (Au, As, Sb and Ba) have been recognized for different catchment basins of the southwest of Saqqez district using PCA. GAII was calculated and mapped based on the CBC_{(Au, Sn)}, CBC_{(Au, W)}, CBC_{(Au, As, Sb, Ba)}, and their MPC scores. GAII accurately detected the Au mineralization zones and improved the geochemical anomaly map in this area compared to the PP method, concentration-area fractal model, and U-spatial statistics method. The results demonstrated that GAII was successfully used for (a) identifying the mineralization paragenesis elements, (b) intensifying the geochemical anomaly, and (c) increasing the prediction rate of mineralization zones. The shear zone gold mineralization areas in the southwest of Saqqez district were effectively detected using this new data analysis approach. GAII has provided better results than the current PP method, concentration-area fractal model, and U-spatial statistics method.     

基于多源遥感影像的长春市城市建成区提取

夜光遥感影像记录的城市灯光与人类活动密切相关,已广泛应用于城市信息提取。珞珈一号作为新一代夜光遥感数据源,比以往的夜光数据具有更高的空间分辨率和光谱分辨率,可以更清晰地表达城市建成区范围和内部结构。本文利用珞珈一号夜光遥感影像,通过人类居住指数（human settlement index, HSI）、植被覆盖和建筑共同校正的城市夜光指数（vegetation and build adjusted nighttime light urban index, VBANUI）及支持向量机（support vector machine, SVM）监督分类3种方法对长春市城市建成区进行提取,并与利用NPP/VIIRS（suomi national polar-orbiting partnership/visible infrared imaging radiometer suite）夜光遥感影像、采用同样方法得到的结果对比。结果显示：本文提出的VBANUI提高了传统植被覆盖校正的城市夜光指数（vegetation adjusted nighttime light urban index, VANUI）的提取精度,使用珞珈一号夜光遥感影像通过VBANUI提取的城市建成区结果最优,其Kappa系数为0.80,总体分类精度为90.74%;使用珞珈一号和NPP/VIIRS夜光遥感影像通过HSI按最佳阈值提取城市建成区的Kappa系数分别为0.75和0.72,总体分类精度分别为88.27%和86.54%;复合数据的SVM监督分类法中Landsat-NDBI、Landsat-NDBI-VIIRS、Landsat-NDBI-LJ和Landsat-NDBI-LJlog的Kappa系数分别为0.602、0.627、0.643和0.681,总体分类精度分别为81.11%、81.52%、82.25%和84.48%。研究结果表明：3种提取方法下,均为使用珞珈一号夜光遥感影像的结果优于使用NPP/VIIRS夜光遥感影像的结果,证明相比于NPP/VIIRS夜光遥感影像,珞珈一号夜光遥感影像更适用于城市尺度的建成区范围提取。     

Prediction of ultimate axial load-carrying capacity of piles using a support vector machine based on CPT data

The support vector machine (SVM) is a relatively new artificial intelligence technique which is increasingly being applied to geotechnical problems and is yielding encouraging results. In this paper SVM models are developed for predicting the ultimate axial load-carrying capacity of piles based on cone penetration test (CPT) data. A data set of 108 samples is used to develop the SVM models. These data were obtained from the literature containing pile load tests and each sample contains information regarding pile geometry, full-scale static pile load tests and CPT results. Moreover, a sensitivity analysis is carried out to examine the relative significance of each input variable with respect to ultimate strength prediction. Finally, a statistical analysis is conducted to make comparisons between predictions obtained from the SVM models and three traditional CPT-based methods for determining pile capacity. The comparison confirms that the SVM models developed in this paper outperform the traditional methods.     

Applicability of Statistical Learning Algorithms for Spatial Variability of Rock Depth

Two algorithms are outlined, each of which has interesting features for modeling of spatial variability of rock depth. In this paper, reduced level of rock at Bangalore, India, is arrived from the 652 boreholes data in the area covering 220 sq⋅km. Support vector machine (SVM) and relevance vector machine (RVM) have been utilized to predict the reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth. The support vector machine (SVM) that is firmly based on the theory of statistical learning theory uses regression technique by introducing ε-insensitive loss function has been adopted. RVM is a probabilistic model similar to the widespread SVM, but where the training takes place in a Bayesian framework. Prediction results show the ability of learning machine to build accurate models for spatial variability of rock depth with strong predictive capabilities. The paper also highlights the capability of RVM over the SVM model.     

公路软基沉降预测的支持向量机模型

提出了基于支持向量机（SVM）模型对公路软基沉降进行预测的一种新方法,工程实例预测结果表明,在同样的训练均方误差下,SVM模型预测能力要优于BP神经网络模型,同时该模型能够综合利用分级加载过程中的沉降观测数据作为训练样本集,比仅依靠预压期内部分实测沉降数据的双曲线法更能反映地基土的变形趋势。因此,将建立的SVM模型应用于公路软基沉降预测能够更准确地反映实际沉降过程