桩基支护方式选择的新方法

针对深基坑施工中桩基支护方式的选择,以机器学习的新方法--支持向量机为工具,建立了桩基支护方式选择的支持向量机模型,并将该模型应用于具体的工程进行验证中。研究结果表明,该种方法是适应性较好的,具有很好的应用前景。     

泥石流堆积物粒度分布特征影响因素分析及分形维数预测

泥石流堆积物作为泥石流发育最终的产物,含有大量与泥石流发生过程和发育特征相关的信息,能够反映泥石流灾害程度和活动强度。研究表明,泥石流堆积物颗粒具有明显的自相似性和无标度区间,运用分形理论,计算泥石流堆积物颗粒分布的分维数。分析分维数与主沟长度、泥砂补给段长度比、主沟平均比降、流域最大相对高差和松散物源量的关系,结果表明分维数与各因素之间存在较强的非线性响应关系。以乌东德库区泥石流实测数据为例,以上述的5个因素作为输入单元,建立了泥石流堆积物分维数支持向量机预测模型,并对分维数进行了预测,其预测结果的最大误差为1.25%,说明预测值与实测值吻合度较高。综合表明支持向量机预测模型能够较好地模拟和泛化数据,是一种行之有效的泥石流堆积物分形维数预测方法,可用于不具备筛析条件的泥石流堆积物粒度分布特征的预测与研究,进而可为研究泥石流的形成机理、类型、危险度和堆积物的形成演化特征及物理力学性质提供一个新思路。     

杭州地铁一号线闸弄口站监测分析

结合工程实例介绍监测技术在杭州地铁一号线闸弄口站中的应用,重点阐述该监测技术的设计、实施及效果。本工程主要实施了水平位移、支撑轴力、地下水位监测和周围地表及建筑物的沉降监测等,并对监测点的布置、监测频率及警戒值的设置都作了科学合理的规定,达到指导施工和优化设计的目的。     

泥石流危险性评价的支持向量机模型相关问题研究

泥石流是一种危害性极大的山区自然灾害,其危险性评价的意义在防灾减灾预案中尤为重要。论文结合近十年来支持向量机方法在泥石流危险性评价中的应用情况,重点探讨泥石流不同评价指标的选取、影响支持向量机性能的参数确定、泥石流数据的不均衡性等对评价结果的影响以及泥石流评价模型的可推广性问题。首先,引入粗糙集理论,对选定的泥石流评价指标进行属性约简,筛选出影响泥石流评价结果的核心指标;其次,比较使用较广泛的网格搜索算法、遗传算法和粒子群优化算法3种方法确定的支持向量机的惩罚指标和核函数参数对评价效果的影响;最后,通过对泥石流单沟的不同评价指标和危险等级的实测数据进行训练和测试,建立泥石流危险性评价的改进支持向量机模型,研究泥石流数据的不均衡性对危险性评价结果的影响,并将建立的模型应用于不同区域的泥石流危险性评价中进行推广性验证。研究结果表明支持向量机模型能够应用于泥石流危险度评价中,但其评价精度的高低、泛化能力的强弱与评价指标的选择、支持向量机性能参数的确定、泥石流数据的均衡性紧密相关,在实际应用中应该加强与支持向量机模型相关问题的研究,才能建立具有较好适用性的泥石流危险性评价模型。     

Predicted ultimate capacity of laterally loaded piles in clay using support vector machine

The determination of ultimate capacity of laterally loaded pile in clay is a key parameter for designing the laterally loaded pile. The available methods for determination of ultimate resistance of pile in clay are not reliable. This study investigates the potential of a support vector machine (SVM)-based approach to predict the ultimate capacity of laterally loaded pile in clay. The SVM, which is firmly based on statistical learning theory, uses a regression technique by introducing an ?-insensitive loss function. A sensitivity analysis has been carried out to determine the relative importance of the factors affecting ultimate capacity. The results show that SVM has the potential to be a practical tool for prediction of the ultimate capacity of pile in clay.     

支持向量机在水淹层测井识别中的应用

支持向量机(SVM)算法是特别适合于用有限已知样本训练建模,进而预报未知样本属性的模式识别新算法.笔者尝试将Vapnik提出的支持向量机算法用于水淹层测井识别.总结了P油田水淹层的声波时差、自然电位、深感应电阻率、中感应电阻率及密度测井曲线与水淹程度的对应关系,建立了基于支持向量分类机的识别模型,并将上述参数作为训练样本的输入,油气特征作为训练样本的输出,对支持向量机进行训练.对于P油田水淹层的实际预测结果表明:支持向量机可以成为一种用于水淹层识别的有效工具.     

深厚软土CFG桩复合地基沉降计算方法对比分析

论文结合京沪高速铁路李窑试验段,对比分析了深厚软土CFG桩复合地基加固区、下卧层附加应力和沉降的不同计算方法,并对现场案例的加固区和下卧层在不同计算方法的沉降计算结果进行了分析研究。结果表明：（1）Boussinesq解能够准确计算复合地基加固区附加应力,Mindlin-Geddes解能够准确计算复合地基下卧层附加应力;（2）面积加权法能够有效计算复合地基加固区沉降;（3）通过线性叠加,计算复合地基下卧层附加应力,能够有效考虑群桩下的复合地基下卧层沉降;（4）由面积加权法和弹性力学法计算得出加固区的沉降量接近,而远大于桩土模量比法计算结果;（5）复合地基置换率相同的情况下,随着桩数的增加下卧层的沉降随之增加。     

上海某教学楼地基沉降特点及原因分析

本文介绍了上海徐汇区某6层教学楼因地基不均匀沉降而导致倾斜和损坏的工程实例。文章对地基沉降特点和原因作了分析。在地基条件较差的地区采用天然地基建造荷重较大的多层建筑物时,对勘察和设计方面应注意的问题提出了一些看法。     

Prediction of pile settlement using artificial neural networks based on standard penetration test data

In recent years artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, accurate prediction of pile settlement is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile settlement based on standard penetration test (SPT) data. Approximately 1000 data sets, obtained from the published literature, are used to develop the ANN model. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum model. Finally, the paper compares the predictions obtained by the ANN with those given by a number of traditional methods. It is demonstrated that the ANN model outperforms the traditional methods and provides accurate pile settlement predictions.     

路堤荷载下碎石桩处理软基沉降计算

以沉降控制为目的的碎石桩复合地基,其沉降计算在设计、施工中具有重要地位。本文基于典型的桩土单元体模型,建立了复合地基体积压缩系数与复合地基弹性模量的关系,考虑施工中的时间因素及相应的固结度对沉降的影响,提出相应的沉降计算公式,并结合四川省遂-资高速公路软基变形监测的数据进一步验证其可行性。研究表明本算法计算值比实测沉降约大10% ～20%。填筑过程中,计算沉降历时曲线与实测曲线拟合较好,更接近实测值;在此基础上,初步探讨了影响路堤荷载下碎石桩处理软基沉降变化的因素,得出桩径、桩间距对其影响较为明显。同时结合本例,建议布桩时桩间距与桩径比值最好控制在2～4之间。     

格栅结构复合地基的应用研究

格栅结构复合地基是一种有侧限结构复合地基。通过载荷试验和相关测试研究表明:格栅结构的侧向变形受到相邻桩的限制,侧向变形减少;格栅内的地基土没有侧向位移;地基土内有应力集中和应力分担比增加的规律。     

An Intelligent Method for Geochemical Discrimination of Tectonic Settings of Basalt Based on Olivine Composition: GWO-SVM Method and its VerificationEI北大核心CSCD

Geochemical discrimination of tectonic settings of basalts has been an important research direction of geochemistry for decades. Olivine is one of the earliest crystallized minerals of basaltic magma, which records a lot of hidden information of the formation and evolution of the magma. Therefore, basic elements in olivine are used to discriminate three tectonic settings, including the mid-ocean ridge basalt (MORB), ocean island basalt (OIB) and island arc basalt (IAB). However, it is still difficult to accurately discriminate the tectonic settings by using these diagrams. The machine learning algorithm is introduced to solve the aforementioned problem. The classification performance of the machine learning discrimination method largely depends on the rationality of parameter determination. To this end, the paper proposes a coupling intelligent method for geochemical discrimination of tectonic settings using olivine composition of the basalts based on the grey wolf optimizer (GWO)-optimized support vector machine (SVM), or GWO-SVM for short. GWO is used to seek the optimal parameter combination of SVM to form the optimal mapping relationship between basic elements in olivine and basalt tectonic settings, so as to realize the accurate discrimination of MORB, OIB and IAB. In addition, according to the published geochemical data of basalt samples, the discrimination performance of GWO-SVM is evaluated by means of the simulation experiment, hold-out validation and k-fold cross-validation. The evaluation results are represented by the confusion matrix and its derived evaluation indicators. The results show that GWO-SVM can discriminate the tectonic settings of the basalts based on olivine compositions with overall classification accuracy of up to 85%. Thus, in comparison with the traditional discrimination diagram method, the machine learning discrimination method based on multi-algorithm fusion can significantly improve the discrimination accuracy of basalt tectonic settings. © 2020, Science Press. All right reserved.     

How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?

Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions.     

碎石桩复合地基现场试验及数值模拟分析

依托某储油罐的碎石桩复合地基处理实例,进行了复合地基现场变形、荷载传递及固结速率的长期监测。参照工程实际建立了碎石桩复合地基三维有限元水土耦合分析模型,讨论了碎石桩桩长以及复合地基置换率对碎石桩复合地基工程性状的影响。监测结果和有限元数值分析结果表明：碎石桩复合地基对于沉降和水平位移的控制效果较好,保证了施工过程中地基的稳定性;随着上部荷载的增加,桩土应力比逐渐增大。碎石桩给地基提供了良好的排水通道,有效加快了地基的固结速率;碎石桩复合地基沉降随着桩长和置换率的增加而减小,但达到一定程度时,置换率和桩长对沉降的减少效果有限;复合地基桩土应力比随着桩长的增加而增大,随着置换率的增大而减小;置换率和桩长的增加都能加快碎石桩复合地基的固结速率,但是置换率对固结速率的影响更大一些。     

基于正交试验的复合地基沉降影响因素分析

结合苏北滨海平原连云港地区海相沉积的软土粘粒含量大、海侵海退、多夹层等特点及某高速公路沉降观测资料,考虑复合地基段沉降效果良好、固结显著, 对水泥搅拌桩处理海相软土地基实测累计沉降的影响因素进行单因素和多因素分析。在正交设计理论的基础上, 提出似正交设计方法并重点对软土厚度、预压方式、填土高度、桩长和桩间距进行多因素分析, 计算证明了预压方式和填土高度对水泥土桩复合地基加固软土地基的实测累计沉降的显著影响, 建议在该工程软土厚度范围变化不大的地段, 应优先调整预压方式并控制填土高度。     

PSO-LIBSVM在钾盐矿层识别中的应用研究

钾盐的紧缺严重制约了中国农业的发展,加大钾盐的勘探开发力度有助于提高我国钾盐的自给自足能力。四川盆地钾盐资源丰富,是我国目前重要的钾盐勘探开发研究区域之一。杂卤石作为四川盆地最重要的固态钾盐矿物,常夹杂在硬石膏、岩盐和白云岩等岩层中。针对常规测井解释方法难以精确识别杂卤石的问题,因此,提出一种新的基于粒子群算法(PSO)优化的支持向量机(SVM)杂卤石识别方法开展四川盆地杂卤石的分类识别研究。以PSO和SVM理论为基础,结合测井解释方法,选择对杂卤石测井响应灵敏的有效数据作为输入样本,随机产生训练集和测试集,并采用PSO优选出径向基核函数参数,建立杂卤石分类预测模型。与录井结果对比,基于PSO的SVM模型识别准确率达到了97.5758%,在识别精度和速度上明显优于交叉验证方法优化的SVM模型。结果表明,该模型在四川盆地钾盐勘探中具有广阔的应用前景。