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.     

Prediction of relative crest settlement of concrete-faced rockfill dams analyzed using an artificial neural network model

A neural network model has been developed for the prediction of relative crest settlement (RCS) of concrete-faced rockfill dams (CFRDs) using 30 databases of field data from seven countries (of which 21 were used for training and 9 for testing). The settlement values predicted using the optimum artificial neural network (ANN) model are in good agreement with these field data. A database prepared from reported crest settlement values of CFRDs after construction was used to train the ANN model to predict the RCS. It is demonstrated here that the model is capable of predicting accurately the relative crest settlement of CFRDs and is potentially applicable for general usage with knowledge of the three basic properties of a dam (void ratio, e; height, H; and vertical deformation modulus, E_V).

The performance of the new ANN model is compared with that of conventional methods based on the Clements theory and also with that of a proposed equation derived from the field data. The comparison indicates that the ANN model has strong potential and offers better performance than conventional methods when used as a quick interpolation and extrapolation tool. The conventional calculation model was proposed based on the fixed connection weights and bias factors of the optimum ANN structure. This method can support the dam engineer in predicting the relative crest settlement of a CFRD after impounding.     

Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes

Increasing demand on infrastructures increases attention to shallow soft ground tunneling methods in urbanized areas. Especially in metro tunnel excavations, due to their large diameters, it is important to control the surface settlements observed before and after excavation, which may cause damage to surface structures. In order to solve this problem, earth pressure balance machines (EPBM) and slurry balance machines have been widely used throughout the world. There are numerous empirical, analytical, and numerical analysis methods that can be used to predict surface settlements. But substantially fewer approaches have been developed for artificial neural network-based prediction methods especially in EPBM tunneling. In this study, 18 different parameters have been collected by municipal authorities from field studies pertaining to EPBM operation factors, tunnel geometric properties, and ground properties. The data source has a preprocess phase for the selection of the most effective parameters for surface settlement prediction. This paper focuses on surface settlement prediction using three different methods: artificial neural network (ANN), support vector machines (SVM), and Gaussian processes (GP). The success of the study has decreased the error rate to 13, 12.8, and 9, respectively, which is relatively better than contemporary research.     

Application of an Expert System for the Assessment of Blast Vibration

The purpose of this article is to evaluate and predict the blast induced ground vibration using different conventional vibration predictors and artificial neural network (ANN) at a surface coal mine of India. Ground Vibration is a seismic wave that spread out from the blast hole when detonated in a confined manner. 128 blast vibrations were recorded and monitored in and around the surface coal mine at different strategic and vulnerable locations. Among these, 103 blast vibrations data sets were used for the training of the ANN network as well as to determine site constants of various conventional vibration predictors, whereas rest 25 blast vibration data sets were used for the validation and comparison by ANN and empirical formulas. Two types of ANN model based on two parameters (maximum charge per delay and distance between blast face to monitoring point) and multiple parameters (burden, spacing, charge length, maximum charge per delay and distance between blast face to monitoring point) were used in the present study to predict the peak particle velocity. Finally, it is found that the ANN model based on multiple input parameters have better prediction capability over two input parameters ANN model and conventional vibration predictors.     

基于因子分析的BP神经网络在岩体变形模量预测中的应用

岩体变形模量是研究岩体变形特性的重要参数,它对工程岩体稳定性评价与优化设计具有重要意义。本文提出了基于因子分析的BP神经网络预测岩体变形模量的方法。以西藏某水电站为例,在现场调查、室内外试验的基础上,建立了48组包括密度、吸水率、纵波波速、单轴抗压强度、岩块变形模量以及泊松比等因素的数据库,采用因子分析法对6个影响因素进行分析,可得3个公共因子,该3个公共因子作为神经网络的输入参数,采用BP神经网络进行预测。结果表明:利用因子分析法可降维输入数据,消除BP神经网络中由于输入数据太多而影响数据处理速度的缺陷; 把因子分析法和BP神经网络结合进行岩体变形模量的预测,可使预测精度提高; 该研究思路不仅对岩体变形参数的预测是一个有益的尝试,而且对类似岩土工程问题的预测也有借鉴意义。     

建筑物对浅埋隧道开挖地表沉降的影响

采用解析法研究穿越地表建筑物浅埋隧道开挖引起的地表沉降。由无建筑物时岩土体开挖引起的地表沉降公式及半无限平面在均布荷载下的相对沉陷,推导出了穿越地表建筑物浅埋隧道施工引起的地表沉降公式,并通过实例验证了此方法的可行性。采用上述方法研究了地表建筑物的重量及其与浅埋隧道位置关系对地表沉降的影响,研究结果表明：浅埋隧道开挖引起的地表沉降随建筑物重量的增大而增大;建筑物中心到隧道轴线的水平距离是对地表沉降的一个重要影响因素,超过一定范围时建筑物的存在对地表沉降的影响可以忽略不计。研究结果可为类似隧道工程提供一定参考。     

双线盾构施工时邻近地下管线安全性判别

研究双线盾构隧道施工时邻近地下管线的安全性判别方法。基于Winker弹性地基梁模型,考虑管土效应,建立连续管线应变与地表沉降关系式;假设管线位移与土体位移相同,建立非连续管线接头转角与地表沉降关系式;同时考虑管线老化,定义与时间相关的折减系数,建立一种通过测量地表沉降值即可判断管线安全性的方法。当管线应变或接头转角为安全允许值时,对应的地表沉降即为控制值。施工时,若地表沉降超过该值,则表明管线存在危险。该方法将不易监测的管线状态转化为可见的地表沉降。研究结果表明：预测值与实测值的对比说明了所提方法具有可靠性;双线隧道水平间距L值对地表沉降控制值的影响非常大。当L较小时,最大值出现在两隧道中轴线处;当L较大时,最大值出现在隧道轴线上方附近处;随着L的增大,最大控制值逐渐减小。     

基于PSO-BP神经网络的基坑周边地面沉降预测方法研究

基坑工程施工过程中的周边地面沉降直接关系到周围建筑物的安全,本文根据上海前滩地区某基坑工程的历史监测数据、施工工况和周边地层参数等多源数据对基坑周边地面沉降进行监测和预测。以PSO-BP神经网络为基础,通过将基于时序和基于沉降影响因素的网络模型对比发现：二者预测结果误差较小且基于时序的神经网络预测精度更高,说明利用PSO-BP神经网络能够很好地对基坑周边地面沉降进行分析与预测。为了综合考虑时间效应和空间效应的影响,在基于沉降影响因素的预测模型的基础上加入历史监测数据作为模型输入层进行优化,结果表明：优化后的PSO-BP神经网络模型具有更小的相对误差范围和更高的预测精度,在基坑周边地面沉降预测中有很好的应用前景。     

Updating the models and uncertainty of mechanical parameters for rock tunnels using Bayesian inference

Rock mechanical parameters and their uncertainties are critical to rock stability analysis, engineering design, and safe construction in rock mechanics and engineering. The back analysis is widely adopted in rock engineering to determine the mechanical parameters of the surrounding rock mass, but this does not consider the uncertainty. This problem is addressed here by the proposed approach by developing a system of Bayesian inferences for updating mechanical parameters and their statistical properties using monitored field data, then integrating the monitored data, prior knowledge of geotechnical parameters,and a mechanical model of a rock tunnel using Markov chain Monte Carlo(MCMC) simulation. The proposed approach is illustrated by a circular tunnel with an analytical solution, which was then applied to an experimental tunnel in Goupitan Hydropower Station, China. The mechanical properties and strength parameters of the surrounding rock mass were modeled as random variables. The displacement was predicted with the aid of the parameters updated by Bayesian inferences and agreed closely with monitored displacements. It indicates that Bayesian inferences combined the monitored data into the tunnel model to update its parameters dynamically. Further study indicated that the performance of Bayesian inferences is improved greatly by regularly supplementing field monitoring data. Bayesian inference is a significant and new approach for determining the mechanical parameters of the surrounding rock mass in a tunnel model and contributes to safe construction in rock engineering.     

Prediction of thermal conductivity of rocks by soft computing

The transfer of energy between two adjacent parts of rock mainly depends on its thermal conductivity. Knowledge of the thermal conductivity of rocks is necessary for the calculation of heat flow or for the longtime modeling of geothermal resources. In recent years, considerable effort has been made to develop artificial intelligence techniques to determine these properties. Present study supports the application of artificial neural network (ANN) in the study of thermal conductivity along with other intrinsic properties of rock due to its increasing importance in many areas of rock engineering, agronomy, and geoenvironmental engineering field. In this paper, an attempt has been made to predict the thermal conductivity (TC) of rocks by incorporating uniaxial compressive strength, density, porosity, and P-wave velocity using artificial neural network (ANN) technique. A three-layer feed forward back propagation neural network with 4-7-1 architecture was trained and tested using 107 experimental data sets of various rocks. Twenty new data sets were used for the validation and comparison of the TC by ANN. Multivariate regression analysis (MVRA) has also been done with same data sets of ANN. ANN and MVRA results were compared based on coefficient of determination (CoD) and mean absolute error (MAE) between experimental and predicted values of TC. It was found that CoD between measured and predicted values of TC by ANN and MVRA were 0.984 and 0.914, respectively, whereas MAE was 0.0894 and 0.2085 for ANN and MVRA, respectively.     

Neural network-based methodology for inter-arrival times of earthquakes

In this paper, an artificial neural network (ANN)?Cbased methodology is proposed to determine the probability of inter-arrival time (IAT) of main shock of six broad seismic regions of India. Initially, classical methodology using exponential distribution is applied to IAT of earthquake events computed from earthquake catalog data. From the goodness-of-fit test results, it has been found that exponential distribution is not adequate. In this paper, a more efficient ANN-based methodology is proposed, and two ANN models are developed to determine the probability of IAT of earthquake events for a specified region, specified magnitude range or magnitude greater than the specified value. The performance of ANN models developed is validated with number of examples and found to predict the probability with minimal error compared to exponential distribution model. The methodology developed can be applied to any other region with the database of the respective regions.     

Creating an advanced backpropagation neural network toolbox within GIS software

An artificial neural network (ANN) toolbox is created within GIS software for spatial interpolation, which will help GIS users to train and test ANNs, perform spatial analysis, and display results as a single process. The performance is compared to that of the open source Fast Artificial Neural Network library and conventional interpolation methods by creating digital elevation models (DEMs) given that nearly exact solutions exist. Simulation results show that the advanced backpropagations such as iRprop speed up the learning, while they can get stuck in a local minimum depending on initial weight sets. Besides, the division of input–output examples into training and test data affects the accuracy, particularly when the distribution of the examples is skewed and peaked, and the number of data is small. ANNs, however, show the similar performance to inversed distance weighted or kriging and outperform polynomial interpolations as a global interpolation method in high-dimensional data. In addition, the neural network residual kriging (NNRK) model, which combines the ANN toolbox and kriging within GIS software, is performed. The NNRK outperforms conventional methods and well captures global trends and local variations. A key outcome of this work is that the ANN toolbox created within the de facto standard GIS software is applicable to various spatial analysis including hazard risk assessment over a large area, in particular when there are multiple potential causes, the relationship between risk factors and hazard events is not clear, and the number of available data is small given its performance for DEM generation.     

人工神经网络方法在径流预报中的应用

采用ＢＰ神经网络模型,以西北内陆河黑河流莺峡年平均出山地表流量为研究对象,对人工神经网络研究方法在干旱区环流径流预报中的应用进行了初步尝试,结果表明该预报成功率较高,证实了人工神经网络方法应用于流量预报领域的可行性,并分析了该方法在预报过程中的优缺性。     

邻近建筑物盾构施工地面沉降数值分析

在建筑物密集的城区,盾构施工使周围一定范围内的既有建筑物受到影响。在考虑建筑物基础形式的不同的情况下,采用二维有限元方法对邻近不同位置建筑物工况下的盾构隧道施工进行了模拟和分析。研究表明,建筑物轴线到隧道轴线的水平距离和建筑物基础形式是影响邻近建筑物工况下隧道开挖引起地面沉降的重要因素,建筑物的存在会增大隧道开挖引起的地面沉降,建筑物对隧道开挖引起的地面沉降的影响存在一个影响范围,超过该范围时建筑物的影响可忽略不计     

高速公路连拱隧道开挖地表沉降研究

对隧道开挖引起的地表沉降模型进行总结,详细地调查和分析了金丽温高速公路红枫连拱隧道工程区的地质特征,系统研究了在偏压条件下连拱隧道开挖引起的地表沉降规律,对红枫隧道开挖引起的地表沉降进行研究,建立了偏压条件下连拱隧道分步开挖引起的地表沉降预测模型,对不同开挖工况下的差异沉降进行分析,证明该预测模型比较真实的反映了在偏压及浅埋条件下连拱隧道开挖引起的地表沉降规律,为浅埋连拱隧道开挖过程中防止地表过度变形提供理论基础。     

软弱黄土隧道变形规律现场测试与分析

为分析软弱黄土隧道的变形规律,以西宁过境高速大有山黄土隧道为依托,采用精密水准仪和收敛计对隧道地表下沉、拱顶下沉和水平收敛进行了系统现场测试。结果表明：软弱黄土隧道拱顶下沉远大于水平收敛,变形时间长,变形量大,累计拱顶下沉值最大为950.6 mm。在临界埋深范围,围岩变形比深埋、浅埋时都大,且变形量离散性高;围岩变形速率在二衬施作时较大,软弱黄土隧道中作为围岩-支护系统稳定性判据的变形速率宜适当提高;围岩变形随时间变化符合指数函数规律,可利用指数函数预测围岩的最终变形;软弱黄土隧道变形分为急剧变形、持续增长和缓慢增长3个阶段,最终趋于稳定。隧道断面的初次开挖对地表变形影响显著,隧道轴线沉降最大,并沿横向逐渐减小。软弱黄土隧道预留变形量在不同位置处不宜统一设置,西宁地区软弱黄土Ⅴ级围岩建议拱顶预留700～800 mm,边墙预留300～350 mm,拱顶与边墙之间以曲线过渡。     

黄土地层地铁暗挖隧道地表纵向沉降规律及其预测分析方法

地铁隧道施工过程中,地表纵向沉降槽最大倾斜率及其出现的时机对地面和地下建筑物的安全评价非常重要。针对目前采用累积概率曲线描述纵向沉降曲线的不足,依据黄土地区地铁隧道暗挖施工引起地表纵向沉降槽特征的研究,寻求一种能反映地表纵向沉降规律的函数,引入掌子面地表位移释放率和地表纵向沉降最大斜率2个特征值,提出基于特征值的地表纵向沉降预测方法。研究结果表明,黄土地区暗挖法地铁隧道施工过程,老黄土-古土壤地层掌子面地表位移释放率和地表纵向沉降最大斜率均较大,饱和软黄土地层的较小。经过数学严密推导,提出一种考虑掌子面地表位移释放率和地表纵向沉降最大斜率的地表纵向沉降预测分析方法,分析了其随着特征值的敏感性,验证了预测方法的可靠性和合理性。研究成果对于分析和预测地铁隧道施工引起地表不均匀沉降对地面与地下建筑的影响具有重要的意义。     

甬江沉管隧道长期沉降监测数据及有限元分析

甬江沉管隧道位于甬江下游河湾处的软土地基上,地基承载力较低,使隧道发生了较大的沉降。此外,甬江严重的淤积及每天2.67 m的潮差对隧道的沉降产生了显著的影响。依据甬江沉管隧道运营期间16 a的沉降监测数据,结合地层条件、潮汐和清淤资料,对该条沉管隧道的长期沉降进行了分析,并提出了基于流-固耦合理论的有限元方法计算沉管隧道的长期沉降,计算结果与监测结果具有较好的一致性。此外,采用上述计算方法分析了影响沉管隧道沉降的3个主要因素（即地层条件、基槽淤积和回淤与清淤）对隧道运营期沉降的影响。分析表明,地层条件是影响沉管隧道沉降的主要因素,软土地基隧道沉降远大于其他地基。潮汐作用会使隧道沉降发生周期性变化,该变化约占隧道运营期沉降的4%～10%。淤积对隧道长期沉降影响显著,但定期清淤只能短时间减小隧道的沉降,使隧道沉降产生周期性变化。上述结论均可为相关工程提供参考。     

Application of an Expert System to Predict Maximum Explosive Charge Used Per Delay in Surface Mining

The present paper mainly deals with the prediction of maximum explosive charge used per delay (Q _MAX) using an artificial neural network (ANN) incorporating peak particle velocity (PPV) and distance between blast face to monitoring point (D). One hundred and fifty blast vibration data sets were monitored at different vulnerable and strategic locations in and around major coal producing opencast coal mines in India. One hundred and twenty-four blast vibrations records were used for the training of the ANN model vis-à-vis to determine site constants of various conventional vibration predictors. The other 26 new randomly selected data sets were used to test, evaluate and compare the ANN prediction results with widely used conventional predictors. Results were compared based on coefficient of correlation (R), mean absolute error and mean squared between measured and predicted values of Q _MAX. It was found that coefficient of correlation between measured and predicted Q _MAX by ANN was 0.985, whereas it ranged from 0.316 to 0.762 by different conventional predictor equations. Mean absolute error and mean squared error was also very small by ANN, whereas it was very high for different conventional predictor equations.     

广州地铁超长水平冻结多参量监测分析

广州地铁3号线天河客运站折返线工程是目前国内最长、开挖断面最大的水平冻结隧道工程。文中根据不同施工阶段中对盐水温度、土层温度、地表变形、冻土压力、隧道衬砌变形等多个参量的现场监测数据,从时间和空间上分析了冻结帷幕演化过程、冻结帷幕发展速度等;探讨了土层温度变化规律以及冻土压力与土体温度间的相互关系,得出了在积极冻结期,沿测温孔深度方向土体温度的变化梯度随冻结时间增加不断减小,土体温度变化速率随时间增加而降低的特征;对比研究了冻结阶段、隧道开挖阶段和融沉阶段地表变形特征,并提出了缩短积极冻结期的建议和方法。