巷道围岩动态工程分类技术研究

基于围岩质量计算、柱状图数字化和重组、结构面诊断、围岩结构装配、采动应力作用后围岩结构演化等研究成果,采用工程要求、基本结构、演化结构为一级指标,提出了围岩的动态工程分类技术、分类结果的表式表达技术,并指出了分类结果的应用方法。     

地方创业与集群转型：一个理论整合的视角

在辨析地方创业概念和内涵的基础上,分析地方创业在集群发展不同阶段的作用和方式,并借鉴演化经济地理中最新的复杂理论,重点辨析在集群非线性发展过程中地方创业主体与集群结构的反馈过程,其中通过整合经济地理学中制度和关系转向的研究进展,提出必须特别关注在集群转型阶段地方创业主体的能动性,把地方创业过程看作创业家与集群内其他主体之间关系网络的制度-权力实践过程,这个实践过程的目的是为了使得新产业的技术范式和组织范式获得社会合理性,从而提高集群弹性,并促进集群转型和更新。     

Modelling long-term groundwater fluctuations by extreme learning machine using hydro-climatic data

The ability of the extreme learning machine (ELM) is investigated in modelling groundwater level (GWL) fluctuations using hydro-climatic data obtained for Hormozgan Province, southern Iran. Monthly precipitation, evaporation and previous GWL data were used as model inputs. Developed ELM models were compared with the artificial neural networks (ANN) and radial basis function (RBF) models. The models were also compared with the autoregressive moving average (ARMA), and evaluated using mean square errors, mean absolute error, Nash-Sutcliffe efficiency and determination coefficient statistics. All the data-driven models had better accuracy than the ARMA, and the ELM model’s performance was superior to that of the ANN and RBF models in modelling 1-, 2- and 3-month-ahead GWL. The RMSE accuracy of the ANN model was increased by 37, 34 and 52% using ELM for the 1-, 2- and 3-month-ahead forecasts, respectively. The accuracy of the ELM models was found to be less sensitive to increasing lead time.     

Modeling of the river ecological status with macrophytes using artificial neural networks

Biomonitoring methods based on macrophytes have been used mandatorily in the assessment of freshwaters since the implementation of the Water Framework Directive (WFD). The Macrophyte Index for Rivers (MIR) was developed in Poland for the monitoring of running waters under the WFD requirements. This index shows the degree of river degradation under the influence of water pollutants, especially nutrients. The aim of the present study was to determine the relationship between the MIR and various hydrochemical parameters using artificial neural networks (ANNs). Physico-chemical parameters of water (monthly results for the whole year), which were derived from 147 lowland river survey sites, all located in Poland, were applied to model the MIR values. Water quality variables were determined over three timeframes: the annual average; the average for the vegetation period; and the average for the summer period. Quality of the networks was assessed using coefficient of determination (R²), Nash-Sutcliffe efficiency (NSE) and root mean square error (RMSE). The best modeling quality was obtained for yearly average values of water quality parameters. The quality statistics were: R² = 0.722, NSE = 0.721 and RMSE = 0.056 (training dataset); R² = 0.555, NSE = 0.533 and RMSE = 0.101 (validation dataset); R² = 0.650. NSE = 0.600 and RMSE = 0.089 (testing dataset). This indicates that macrophytes reflect the whole year impact of pollution, whereas summer.     

Methodology for the development of bridge‐specific fragility curves

A new methodology for the development of bridge‐specific fragility curves is proposed with a view to improving the reliability of loss assessment in road networks and prioritising retrofit of the bridge stock. The key features of the proposed methodology are the explicit definition of critical limit state thresholds for individual bridge components, with consideration of the effect of varying geometry, material properties, reinforcement and loading patterns on the component capacity; the methodology also includes the quantification of uncertainty in capacity, demand and damage state definition. Advanced analysis methods and tools (nonlinear static analysis and incremental dynamic response history analysis) are used for bridge component capacity and demand estimation, while reduced sampling techniques are used for uncertainty treatment. Whereas uncertainty in both capacity and demand is estimated from nonlinear analysis of detailed inelastic models, in practical application to bridge stocks, the demand is estimated through a standard response spectrum analysis of a simplified elastic model of the bridge. The simplified methodology can be efficiently applied to a large number of bridges (with different characteristics) within a road network, by means of an ad hoc developed software involving the use of a generic (elastic) bridge model, which derives bridge‐specific fragility curves. Copyright © 2016 John Wiley & Sons, Ltd.     

Long-range forecasting of suspended sediment

This study examines the employment of two methods, multiple linear regression (MLR) and an artificial neural network (ANN), for multistep ahead forecasting of suspended sediment. The autoregressive integrated moving average (ARIMA) model is considered for one-step ahead forecasting of sediment series in order to provide a comparison with the MLR and ANN methods. For one- and two-step ahead forecasting, the ANN model performance is superior to that of the MLR model. For longer ranges, MLR models provide better accuracy, but there is an important assumption violation. The Durbin-Watson statistics of the MLR models show a noticeable decrease from 1.3 to 0.5, indicating that the residuals are not dependent over time. The scatterplots of the three methods (MLR, ARIMA and ANN) for one-step ahead forecasting for the validation period illustrate close fits with the regression line, with the ANN configuration having a slightly higher R² value.     

交通系统地震服务性能分析

本文论述了交通系统的震害,建立了城市交通系统地震救灾服务功能模型及评估方法以及救灾功能的可靠性分析方法,提出了基于可靠度的系统地震服务功能的分析方法和最佳路径优化方法,并以可靠度和道路长度作为协调参数,运用优化方法解决了交通系统安全性和经济性之间的矛盾,提出了交通系统加固优化的实用的分析方法。最后以某城市交通网络系统为例进行了分析研究,为城市交通系统防震减灾研究提供理论基础。     

人工智能技术在数值天气预报中的应用

当前,人工智能迎来第3次发展浪潮并在多个领域大数据分析中取得巨大成功,这为人工智能技术与数值天气预报结合提供了契机.已有大量研究尝试将人工智能技术用于数值天气预报的初值生成、预报和产品应用过程中,涉及观测资料预处理、资料同化、模式积分、后处理以及高性能计算,通过误差估计、参数估计和局部代理等手段使预报结果,得到改进且计...     

Virtual geotechnical laboratory experiments using a simulator

The details of a test simulator that provides a realistic environment for performing virtual laboratory experimentals in soil mechanics is presented. A computer program ‘Geo‐Sim’ that can be used to perform virtual experiments, and allow for real‐time observations of material response is presented. The results of experiments, for a given set of input parameters, are obtained with the test simulator using well‐trained artificial neural‐network‐based soil models for different soil types and stress paths. Multimedia capabilities are integrated in Geo‐Sim, using software that links and controls a laser disc player with a real‐time parallel processing ability. During the simulation of a virtual experiment, relevant portions of the video image of a previously recorded test on an actual soil specimen are dispalyed along with the graphical presentation of response from the feedforward ANN model predictions. The pilot simulator developed to date includes all aspects related to performing a triaxial test on cohesionless soil under undrained and drained conditions. The benefits of the test simulator are also presented. Copyright © 2000 John Wiley & Sons, Ltd.     

基于超前钻探测试的隧道地层智能识别方法

可靠地识别掌子面前方地层是保证隧道工程稳定与安全的重要因素之一。传统的超前地质预报方法不能同时保证有高识别精度、低实施成本和占用少的施工时间,对于不同地质情况的地层识别通用性不强。在传统超前钻孔的同时获取掌子面前方围岩钻探测试数据,实时获取不同深度岩层情况,将大大提高超前预报效率,方便快捷,不影响施工,但目前缺乏客观、准确的地层识别方法。提出了一种基于神经网络的钻探测试数据智能分析和地层识别方法,对楚大高速公路九顶山隧道超前钻探测试数据进行了深入分析,通过隧道开挖后所揭示地层对分析方法进行了验证。结果表明：单一钻进参数用于地层识别的错误率在35%左右,打击能和打击数、送水压力和送水流量的参数组合不能显著提升地层识别准确率;钻进速度、扭矩、回转数、推进力的参数组合可降低地层识别错误率至22%。在神经网络模型中引入钻进参数的标准差,可大幅降低错误率,可使地层划分错误率下降9%～12%;多参数组合下的神经网络钻探测试神经网络模型对随机抽样的地层识别错误率小于10%,对单个钻孔的地层识别错误率小于14%。