平台地形对地震地面运动特征周期值的影响

本文人工合成了36条代表不同频谱特性的地震动,构造了简单的平台地形,并利用人工合成地震动作为平台地形计算输入地震波,获得了地表观测点的时程和反应谱.在此基础上,分析了具有不同高度、侧向坡降和介质阻尼等的平台地形对地震动特征周期值的影响.研究的结果表明:当平台高度与入射地震波优势波长相比较小时,平台的高度、侧向坡降、阻尼比等对地震动特征周期值的影响不大,此时单个平台地形地表地震动的特征周期主要依赖于入射地震动的特征周期,而且一般比入射地震动的特征周期略有增大;当平台高度与入射地震波优势波长相比较大时,平台高度对地表地震动特征周期影响较大.     

Estimation of river flow by artificial neural networks and identification of input vectors susceptible to producing unreliable flow estimates

Reliable river flow estimates are crucial for appropriate water resources planning and management. River flow forecasting can be conducted by conceptual or physical models, or data-driven black box models. Development of physically-based models requires an understanding of all the physical processes which impact a natural process and the interactions among them. Since identification of the relationships among these physical processes is very difficult, data-driven approaches have recently been utilized in hydrological modeling. Artificial neural networks are one of the widely used data-driven approaches for modeling hydrological processes. In this study, estimation of future monthly river flows for Guvenc River, Ankara is conducted using various artificial neural network models. Success of artificial neural network models relies on the availability of adequate data sets. A direct mapping from inputs to outputs without consideration of the complex relationships among the dependent and independent variables of the hydrological process is identified. In this study, past precipitation, river flow data, and the associated month are used to predict future river flows for Guvenc River. Impacts of various input patterns, number of training cycles, and initial values assigned to the weights of the connections are investigated. One of the major weaknesses of artificial neural networks is that they may fail to generate good estimates for extreme events, i.e. events that do not occur at all or often enough in the training data set. It is very important to be able to identify such unlikely events. A fuzzy c-means algorithm is used in this study to cluster the training and validation input vectors into regular and extreme events so that the user will have an idea about the risk of the artificial neural network model to generate unreliable results.     

河南省数字地震台网自动量算的震级与人工量算的震级的偏差分析

对2007年12月-2008年9月河南省数字地震台网资料自动量算的震级与人工量算的震级进行了比较和分析。结果表明：由MSDP软件自动量算的震级与人工量算的震级偏差较小,且自动量算的震级可以满足地震定位精度的要求,在地震速报时可采用自动量算的震级。     

Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul,Turkey

Tropospheric (ground‐level) ozone has adverse effects on human health and environment. In this study, next day's maximum 1‐h average ozone concentrations in Istanbul were predicted using multi‐layer perceptron (MLP) type artificial neural networks (ANNs). Nine meteorological parameters and nine air pollutant concentrations were utilized as inputs. The total 578 datasets were divided into three groups: training, cross‐validation, and testing. When all the 18 inputs were used, the best performance was obtained with a network containing one hidden layer with 24 neurons. The transfer function was hyperbolic tangent. The correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and index of agreement or Willmott's Index (d₂) for the testing data were 0.90, 8.78 µg/m³, 11.15 µg/m³, and 0.95, respectively. Sensitivity analysis has indicated that the persistence information (current day's maximum and average ozone concentrations), NO concentration, average temperature, PM₁₀, maximum temperature, sunshine time, wind direction, and solar radiation were the most important input parameters. The values of R, MAE, RMSE, and d₂ did not change considerably for the MLP model using only these nine inputs. The performances of the MLP models were compared with those of regression models (i.e., multiple linear regression and multiple non‐linear regression). It has been found that there was no significant difference between the ANN and regression modeling techniques for the forecasting of ozone concentrations in Istanbul.     

Prediction of Density Flow Plunging Depth in Dam Reservoirs: An Artificial Neural Network Approach

Experimental findings and observations indicate that plunging flow is related to the formation of bed load deposition in dam reservoirs. The sediment delta begins to form in the plunging region where the inflow river water meets the ambient reservoir water. Correct estimation of dam reservoir flow, plunging point, and plunging depth is crucial for dam reservoir sedimentation and water quality issues. In this study, artificial neural network (ANN), multi‐linear regression (MLR), and two‐dimensional hydrodynamic model approaches are used for modeling the plunging point and depth. A multi layer perceptron (MLP) is used as the ANN structure. A two‐dimensional model is adapted to simulate density plunging flow through a reservoir with a sloping bottom. In the model, nonlinear and unsteady continuity, momentum, energy, and k–ε turbulence equations are formulated in the Cartesian coordinates. Density flow parameters such as velocity, plunging points, and plunging depths are determined from the simulation and model results, and these are compared with previous experimental and model works. The results show that the ANN model forecasts are much closer to the experimental data than the MLR and mathematical model forecasts.     

基于人工鱼群算法和Pearson相关系数的裂缝属性识别

将人工鱼群算法和Pearson相关系数结合,引入到进行裂缝属性识别的横波分裂方法中。结果表明,本文使用的方法能快速准确地识别裂缝属性,相比于模型空间扫描、粒子群算法及遗传算法等识别方法,收敛速度增快了约3倍,稳定性上也有所提高。     

Evaluation of the applicability of pier local scour formulae using laboratory and field data

The reliable estimation of the local scour depth at a bridge pier is essential for proper design and maintenance of bridge piers. Most local scour formulae have been developed based on the results of laboratory experiments. The formulae based on laboratory data do not often produce reasonable predictions for field piers because laboratory investigations are apt to oversimplify or ignore many of the complexities of the flow fields around the bridge piers. Validation of the formulae is necessary in order to ascertain which of the formulae are able to provide reasonable estimates of the local scour depth. In this study, six commonly cited formulae based on laboratory data or field data were selected for validation using 180 laboratory data sets gathered from the literature and 446 field data sets collected from four countries. The six formulae validated in this paper are the Colorado State University (CSU), Neill, Froehlich, Breuser, Laursen, and simplified Chinese formulae. Comparisons between the predicted and measured depths were performed using scour from the laboratory and field data. An artificial neural network technique was also applied in order to compare the tendencies between the field and laboratory data sets.     

Experimental prediction of the noncontact jet Trencher’s excavation depth in clay

Trenching depth calculation of traveling jet is a very important field in both pipeline burying and dredging engineering. The trenching ability of a noncontact control flow jet trencher was predicted by experimental method according to the geometry similarity. In the test, the jet pressure, the shear strength of clay, and the translational speed of nozzle were the same with the design parameters, while the nozzle was a reduced scale model. The trenching depth was found about seven to nine times of the nozzle’s diameter. The width of the trench was about four times of the nozzle’s diameter. Based on the test results the noncontact jet trencher was produced and succeeded in practical usage.     

Artificial neural networks and cluster analysis in landslide susceptibility zonation

A landslide susceptibility analysis is performed by means of Artificial Neural Network (ANN) and Cluster Analysis (CA). This kind of analysis is aimed at using ANNs to model the complex non linear relationships between mass movements and conditioning factors for susceptibility zonation, in order to identify unstable areas. The proposed method adopts CA to improve the selection of training, validation, and test records from data, managed within a Geographic Information System (GIS). In particular, we introduce a domain-specific distance measure in cluster formation. Clustering is used in data pre-processing to select non landslide records and is performed on the whole dataset, excluding the test set landslides. Susceptibility analysis is carried out by means of ANNs on the so-generated data and compared with the common strategy to select random non-landslide samples from pixels without landslides. The proposed method has been applied in the Brembilla Municipality, a landslide-prone area in the Southern Alps, Italy. The results show significant differences between the two sampling methods: the classification of the test set, previously separated and excluded from the training data, is always better when the non-landslide patterns are obtained using the proposed cluster sampling. The case study validates that, by means of a domain-specific distance measure in cluster formation, it is possible to introduce expert knowledge into the black-box modelling method, implemented by ANNs, to improve the predictive capability and the robustness of the models obtained.     

圆周运动天体轨道半径的相对论效应

从理论上探讨圆周运动天体轨道半径的相对论效应,并应用于行星际飞船与人造地球卫星的情形。结果表明,在这两种情形中,均存在着可观测的效应。