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1.
The objective of this study is to explore and compare the least square support vector machine (LSSVM) and multiclass alternating decision tree (MADT) techniques for the spatial prediction of landslides. The Luc Yen district in Yen Bai province (Vietnam) has been selected as a case study. LSSVM and MADT are effective machine learning techniques of classification applied in other fields but not in the field of landslide hazard assessment. For this, Landslide inventory map was first constructed with 95 landslide locations identified from aerial photos and verified from field investigations. These landslide locations were then divided randomly into two parts for training (70 % locations) and validation (30 % locations) processes. Secondly, landslide affecting factors such as slope, aspect, elevation, curvature, lithology, land use, distance to roads, distance to faults, distance to rivers, and rainfall were selected and applied for landslide susceptibility assessment. Subsequently, the LSSVM and MADT models were built to assess the landslide susceptibility in the study area using training dataset. Finally, receiver operating characteristic curve and statistical index-based evaluations techniques were employed to validate the predictive capability of these models. As a result, both the LSSVM and MADT models have high performance for spatial prediction of landslides in the study area. Out of these, the MADT model (AUC = 0.853) outperforms the LSSVM model (AUC = 0.803). From the landslide study of Luc Yen district in Yen Bai province (Vietnam), it can be conclude that the LSSVM and MADT models can be applied in other areas of world also for and spatial prediction. Landslide susceptibility maps obtained from this study may be helpful in planning, decision making for natural hazard management of the areas susceptible to landslide hazards.  相似文献   

2.
随着煤层气勘探的不断深入,对煤层含气量预测精度提出了更高的要求。基于煤层含气量测井响应特征,分析测井参数与含气量的相关性,提出MIV（Mean Impact Value）技术与LSSVM（Least Squares Support Vector Machine）结合的测井参数优选策略,优选最优测井参数作为网络建模的输入自变量组合,通过粒子群算法优化LSSVM网络核心参数,最后构建一套适用于煤层含气量预测的MIV-PSO-LSSVM模型。在此基础上,分别对比分析LSSVM、PSO-LSSVM、MIV-LSSVM和MIV-PSO-LSSVM模型对煤层含气量的预测性能,并与传统多元回归方法进行了对比,利用拟合优度和均方根误差对此5类模型进行评价。结果表明:PSO优化下的LSSVM模型预测精度得到有效提升,结合MIV方法优选测井参数可大幅度改善神经网络建模性能,MIV-PSO-LSSVM模型可实现煤层含气量高精度预测,为煤层气勘探及其储层评价提供新的技术支撑,且本研究的建模策略及思想可广泛应用于其他机器学习建模研究领域。   相似文献   

3.
周婷  温小虎  冯起  尹振良  杨林山 《冰川冻土》2022,44(5):1606-1619
准确可靠的径流预测对于水资源的科学管理与规划具有重要意义,特别是在水资源紧缺的干旱半干旱地区,径流预测对流域内水资源高效利用与水利工程经济运行具有重要现实意义。针对径流预测通常采用单一方法进行建模与预测,难以利用各预测模型优势的问题,本文利用极限学习机（ELM）模型、支持向量机（SVM）模型、多元自适应回归样条（MARS）等机器学习方法建立了疏勒河上游未来1~7日的径流预测模型。在此基础上,运用贝叶斯模型平均（BMA）方法对ELM、SVM、MARS模型的预测结果进行组合,构建了径流组合预测模型,以获取更可靠的预测结果,并采用蒙特卡洛抽样方法获取BMA的95%置信区间,对预测结果进行了不确定性分析。结果表明：ELM、SVM、MARS模型以及BMA组合模型均适用于干旱半干旱地区的中长期日径流预测;BMA的预测精度较单一模型更高,能够提供更准确的预测值;BMA的95%置信区间对实测值覆盖率高,同时能够提供较好的确定性预测和概率预测结果。表明BMA在资料有限的条件下,表现出较单一模型更高的预测性能,可以成为干旱半干旱地区中长期日径流预测的有效方法。  相似文献   

4.
The determination of liquefaction potential of soil is an imperative task in earthquake geotechnical engineering. The current research aims at proposing least square support vector machine (LSSVM) and relevance vector machine (RVM) as novel classification techniques for the determination of liquefaction potential of soil from actual standard penetration test (SPT) data. The LSSVM is a statistical learning method that has a self-contained basis of statistical learning theory and excellent learning performance. RVM is based on a Bayesian formulation. It can generalize well and provide inferences at low computational cost. Both models give probabilistic output. A comparative study has been also done between developed two models and artificial neural network model. The study shows that RVM is the best model for the prediction of liquefaction potential of soil is based on SPT data.  相似文献   

5.
This paper examines the potential of least‐square support vector machine (LSVVM) in the prediction of settlement of shallow foundation on cohesionless soil. In LSSVM, Vapnik's ε‐insensitive loss function has been replaced by a cost function that corresponds to a form of ridge regression. The LSSVM involves equality instead of inequality constraints and works with a least‐squares cost function. The five input variables used for the LSSVM for the prediction of settlement are footing width (B), footing length (L), footing net applied pressure (P), average standard penetration test value (N) and footing embedment depth (d). Comparison between LSSVM and some of the traditional interpretation methods are also presented. LSSVM has been used to compute error bar. The results presented in this paper clearly highlight that the LSSVM is a robust tool for prediction of settlement of shallow foundation on cohesionless soil. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

6.
Geospatial technology is increasing in demand for many applications in geosciences. Spatial variability of the bed/hard rock is vital for many applications in geotechnical and earthquake engineering problems such as design of deep foundations, site amplification, ground response studies, liquefaction, microzonation etc. In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 km2. In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, Geostatistical model based on Ordinary Kriging technique, Artificial Neural Network (ANN) and Support Vector Machine (SVM) models have been developed. In Ordinary Kriging, the knowledge of the semi-variogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of the Bangalore, where field measurements are not available. A new type of cross-validation analysis developed proves the robustness of the Ordinary Kriging model. ANN model based on multi layer perceptrons (MLPs) that are trained with Levenberg–Marquardt backpropagation algorithm has been adopted to train the model with 90% of the data available. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing loss function has been used to predict the reduced level of rock from a large set of data. In this study, a comparative study of three numerical models to predict reduced level of rock has been presented and discussed.  相似文献   

7.
A landslide located on the Quesnel River in British Columbia, Canada is used as a case study to demonstrate the utility of a multi-geophysical approach to subsurface mapping of unstable slopes. Ground penetrating radar (GPR), direct current (DC) resistivity and seismic reflection and refraction surveys were conducted over the landslide and adjacent terrain. Geophysical data were interpreted based on stratigraphic and geomorphologic observations, including the use of digital terrain models (DTMs), and then integrated into a 3-dimensional model. GPR surveys yielded high-resolution data that were correlated with stratigraphic units to a maximum depth of 25 m. DC electrical resistivity offered limited data on specific units but was effective for resolving stratigraphic relationships between units to a maximum depth of 40 m. Seismic surveys were primarily used to obtain unit boundaries up to a depth of >80 m. Surfaces of rupture and separation were successfully identified by GPR and DC electrical resistivity techniques.  相似文献   

8.
An essential task in the process of construction is the determination of compaction properties of soils. Many years of laboratory test experience strengthen our belief in the existence of predictive equations that govern the compaction characteristics of soils. An advanced mathematical model developed in this research in order to uncertain the governing equations. An advanced mathematical model developed in this research in order to uncertain the governing equations. Through a comparative study among a Multiple Linear Regression (MLR) model, an Artificial Neural Network (ANN) model, Extreme Learning Machine (ELM) and a Support Vector Machine (SVM) model, the best predicting model was determined. For this purpose, Six hundred and six (606) samples collected and split into a dataset used for training the models and another used for validation of the derived model. 8 neural networks with a varying number of hidden layers and a varying number of nodes in hidden layers were employed. In ELM 1 hidden layer with varying number of units were employed. It was found that the equations derived from the ELM models described the relationship with superiority over multiple regression, ANN and SVM models for Maximum Dry Density and MLR models described the relationship with superiority over ANN, ELM and SVM models for Optimum Moisture Content.  相似文献   

9.
Soil electrical resistivity (RE) is an important parameter for geotechnical engineering projects. This article employs Gaussian process regression (GPR) for prediction of RE of soil based on soil thermal resistivity (RT), percentage sum of the gravel and sand size fractions (F), and degree of saturation (Sr). GPR is derived from the perspective of Bayesian nonparametric regression. Two models (Model I and Model II) have been developed. The developed GPR has been compared with the artificial neural network. It gives the variance of the predicted RE. The results show the developed GPR is an efficient tool for prediction of RE of soil.  相似文献   

10.
通过机械比能对煤矿瓦斯抽采钻孔过程中的围岩进行可钻性分级,可为钻机调整钻进参数提供依据。针对瓦斯抽采钻孔过程中人工判层难度大、效率低的问题,提出一种以机械比能为可钻性评价指标,结合极限学习机的煤岩可钻性分级方法。采用ABAQUS建立了PDC钻头破岩仿真模型,从材料类型、钻头转速和钻压力三个方面研究了PDC钻头破岩过程中钻进速度和机械比能的变化规律。同时,获得了钻进参数及机械比能的训练数据,采用极限学习机分别对钻进参数和机械比能数据进行学习,最后,对这两种可钻性分级指标下的分级准确率进行对比。结果表明:以机械比能作为可钻性指标时的分级准确率达到90%以上,高于以钻进参数作为可钻性指标时的准确率。分级结果可以为钻机调整钻进参数、实现自适应钻进提供理论依据。   相似文献   

11.
Rock masses are commonly used as the underlying layer of important structures such as bridges, dams and transportation constructions. The success of a foundation design for such structures mainly depends on the accuracy of estimating the bearing capacity of rock beneath them. Several traditional numerical approaches are proposed for the estimation of the bearing capacity of foundations resting on rock masses to avoid performing elaborate and expensive experimental studies. Despite this fact, there still exists a serious need to develop more robust predictive models. This paper proposes new nonlinear prediction models for the ultimate bearing capacity of shallow foundations resting on non-fractured rock masses using a novel evolutionary computational approach, called linear genetic programming. A comprehensive set of rock socket, centrifuge rock socket, plate load and large-scaled footing load test results is used to develop the models. In order to verify the validity of the models, the sensitivity analysis is conducted and discussed. The results indicate that the proposed models accurately characterize the bearing capacity of shallow foundations. The correlation coefficients between the experimental and predicted bearing capacity values are equal to 0.95 and 0.96 for the best LGP models. Moreover, the derived models reach a notably better prediction performance than the traditional equations.  相似文献   

12.
This paper presents slope stability evaluation and prediction with the approach of a fast robust neural network named the extreme learning machine (ELM). The circular failure mechanism of a slope is formulated based on its material, geometrical and environmental parameters such as the unit weight, the cohesion, the internal friction angle, the slope inclination, slope height and the pore water ratio. The ELM is proposed to evaluate the stability of slopes subjected to potential circular failures by means of prediction of the factor of safety (FS). Substantial slope cases collected worldwide are utilized to illustrate and assess the capability and predictability of the ELM on slope stability analysis. Based on the mean absolute percentage errors and the correlation coefficients between the original and predicted FS values, comparisons are demonstrated between the ELM and the generalized regression neural network (GRNN) as well as the prediction models generated from the genetic algorithms. Moreover, sensitivity analysis of the slope parameters and the ELM model parameters is carried out based on the two utilized evaluation functions. The time expense of the ELM on slope stability analysis is also investigated. The results prove that the ELM is advantageous to the GRNN and the genetic algorithm based models in the analysis of slope stability. Hence, the ELM can be a promising technique for approaching the problems in geotechnical engineering.  相似文献   

13.
Stability with first time or reactivated landslides depends upon the residual shear strength of soil. This paper describes prediction of the residual strength of soil based on index properties using two machine learning techniques. Different Artificial Neural Network (ANN) models and Support Vector Machine (SVM) techniques have been used. SVM aims at minimizing a bound on the generalization error of a model rather than at minimizing the error on the training data only. The ANN models along with their generalizations capabilities are presented here for comparisons. This study also highlights the capability of SVM model over ANN models for the prediction of the residual strength of soil. Based on different statistical parameters, the SVM model is found to be better than the developed ANN models. A model equation has been developed for prediction of the residual strength based on the SVM for practicing geotechnical engineers. Sensitivity analyses have been also performed to investigate the effects of different index properties on the residual strength of soil.  相似文献   

14.
In many rock engineering applications such as foundations, slopes and tunnels, the intact rock properties are not actually determined by laboratory tests, due to the requirements of high quality core samples and sophisticated test equipments. Thus, predicting the rock properties by using empirical equations has been an attractive research topic relating to rock engineering practice for many years. Soft computing techniques are now being used as alternative statistical tools. In this study, artificial neural network models were developed to predict the rock properties of the intact rock, by using sound level produced during rock drilling. A database of 832 datasets, including drill bit diameter, drill bit speed, penetration rate of the drill bit and equivalent sound level (Leq) produced during drilling for input parameters, and uniaxial compressive strength (UCS), Schmidt rebound number (SRN), dry density (ρ), P-wave velocity (Vp), tensile strength (TS), modulus of elasticity (E) and percentage porosity (n) of intact rock for output, was established. The constructed models were checked using various prediction performance indices. Goodness of the fit measures revealed that recommended ANN model fitted the data as accurately as experimental results, indicating the usefulness of artificial neural networks in predicting rock properties.  相似文献   

15.
徐坤  王志杰  孟祥磊  孙长升 《岩土力学》2013,34(Z2):464-470
围岩松动圈支护理论已被工程界广泛认同和接受,如何准确快速地探测松动圈深度更好的为工程服务,成为大家所关注的问题。以新建兰新铁路大梁隧道现场试验为依托,对测试断面围岩松动圈深度采用单孔声波测试法、地质雷达法进行探测,结合现场地应力及岩体物理力学参数实测结果进行数值模拟分析可知,以单孔声波测试法结果为基准,地质雷达测试结果与声波法测试结果基本一致,在围岩含水区域测试结果存在一定偏差,地质雷达发出的电磁波对含水区域比较敏感,发射和接收干扰较大,但地质雷达作为一种快速、无损的检测方法应该得到大力推广应用。由于数值计算时没有考虑爆破对围岩松动圈的影响,其计算结果与声波法探测结果相比偏小,但是两者的变化趋势基本上是一致的。数值计算应以现场地应力及岩体物理力学参数测试结果为依据,使计算结果更加真实,更好地为工程决策服务。  相似文献   

16.
Applications of NTNU/SINTEF Drillability Indices in Hard Rock Tunneling   总被引：1，自引：1，他引：0  
Drillability indices, i.e., the Drilling Rate Index? (DRI), Bit Wear Index? (BWI), Cutter Life Index? (CLI), and Vickers Hardness Number Rock (VHNR), are indirect measures of rock drillability. These indices are recognized as providing practical characterization of rock properties used in the Norwegian University of Science and Technology (NTNU) time and cost prediction models available for hard rock tunneling and surface excavation. The tests form the foundation of various hard rock equipment capacity and performance prediction methods. In this paper, application of the tests for tunnel boring machine (TBM) and drill and blast (D&B) tunneling is investigated and the impact of the indices on excavation time and costs is presented.  相似文献   

17.
李华  王东辉 《工程地质学报》2017,25(4):1057-1064
利用改进的时域有限差分算法,对不同物理和几何参数条件下各种滑坡要素组成的综合模型进行数值模拟,研究滑带厚度、滑带充填介质、滑体岩土类型、滑坡裂缝等的地质雷达探测响应效果。研究结果表明,具有各自物理和几何参数的滑坡要素与地质雷达响应特征之间存在一一对应的数学关联,通过这种关联可以从野外实测数据中有效提取和判读滑坡要素的类型及物理几何特性,为滑坡稳定性评价提供地球物理依据。为了展示该研究成果的适用性,以三峡库区将军滩滑坡的地质雷达野外实测数据为例进行解释推断,成功识别出滑坡体的滑带埋深及分布、滑体的裂缝发育程度等情况,为评价滑坡稳定性,合理优化滑坡治理方案提供了科学参考。  相似文献   

18.
Penetration rate prediction of Tunnel Boring Machine (TBM) is the first step to advance prediction process of mechanized tunnelling. In this research, influence of effective parameters on TBM penetration rate is investigated by sensitivity analysis of three main TBM performance prediction methods; Norwegian University of Science and Technology (NTNU), rock mass index (RMi) and QTBM. Based on these analyses, it is shown that applied thrust per disc and joint spacing in NTNU and RMi models have more influence on penetration rate. In QTBM model, Q value, applied thrust per disc and induced biaxial stress are more effective.  相似文献   

19.
Frequent failures of monsoons have forced to opt the groundwater as the only source of irrigation in non-command areas. Groundwater exploration in granitic terrain of dry land agriculture has been a major concern for farmers and water resource authorities. The hydrogeological complexities and lack of understanding of the aquifer systems have resulted in the failure of a majority of the borehole drillings in India. Hence, a combination of geophysical tools comprising ground-penetrating radar (GPR), multielectrode resistivity imaging (MERI), and vertical electrical sounding (VES) has been employed for pinpointing the groundwater potential zones in dry land agricultural of granitic terrain in India. Results obtained and verified with each other led to the detection of a saturated fracture within the environs. In GPR scanning, a 40-MHz antenna is used with specifications of 5 dielectric constant, 600 scans/nS, and 40 m depth. The anomalies acquired on GPR scans at various depths are confirmed with low-resistivity ranges of 27–50?Ω m at 23 and 27 m depths obtained from the MERI. Further, drilling with a down-the-hole hammer was carried out at two recommended sites down to 50–70 m depth, which were complimentary of VES results. The integrated geophysical anomalies have good agreement with the drilling lithologs validating the MERI and GPR data. The yields of these bore wells varied from 83 to 130 l/min. This approach is possible and can be replicated by water resource authorities in thrust areas of dry land environs of hard rock terrain around the world.  相似文献   

20.
In this paper, an M–EEMD–ELM model (modified ensemble empirical mode decomposition (EEMD)-based extreme learning machine (ELM) ensemble learning paradigm) is proposed for landslide displacement prediction. The nonlinear original surface displacement deformation monitoring time series of landslide is first decomposed into a limited number of intrinsic mode functions (IMFs) and one residual series using EEMD technique for a deep insight into the data structure. Then, these sub-series except the high frequency are forecasted, respectively, by establishing appropriate ELM models. At last, the prediction results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original landslide displacement series. A case study of Baishuihe landslide in the Three Gorges reservoir area of China is presented to illustrate the capability and merit of our model. Empirical results reveal that the prediction using M–EEMD–ELM model is consistently better than basic artificial neural networks (ANNs) and unmodified EEMD–ELM in terms of the same measurements.  相似文献   

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