中国长江主要灾害与整治

由于江水和人类活动，引发长江崩塌、淤寒、洪涝和污染等灾害。研其原因主要为水动力改变；自然环境变化；人为破坏。整方案应采取治沙，营造“森林水库”；治水，按水动力学运动规律，用蓄、疏堵等手段综合治理；治污，要彻底根治污源；养殖，大力发展养殖业，促使生态平衡。     

水压与钻孔联合爆破拆除大型地面煤仓

分析了水压与钻孔联合爆破的特点，给出了设计方法及参数，实践表明，在坚固筒体内构筑物拆除中应用该技术可取得安全，快速解体破碎和定向倾倒的良好效果。     

人为地质灾害和地质环境

本文论述了人为地质灾害的概念, 典型事例以及人为地质灾害与地质环境的相互关系。指出, 人为地质灾害是由于人类的工程经济活动而引起的地质灾害。人为地质灾害可导致地质环境恶化, 使环境质量下降；而地质环境恶化又可导致地质灾害的加重, 使灾害频度增大, 强度增高。     

Analysis of parameters of ground vibration produced from bench blasting at a limestone quarry

The aim of this study is to predict peak particle velocity level at a limestone quarry located in Istanbul, Turkey. The ground vibration components were measured for 73 blast events during the bench blast optimization studies during a long period. In blasting operations; ANFO (blasting agent), gelatine dynamite (priming) and NONEL detonators (firing) were used as explosives at this site. Parameters of scaled distance (charge quantity per delay and the distance between the source and the station) were recorded carefully and the ground vibration components were measured by means of vibration monitors for every event. Then, the data pairs of scaled distance and particle velocity were analyzed. The equation of scaled distance extensively used in the literature was taken into consideration for the prediction of peak particle velocity. At the end of statistical evaluations, an empirical relationship with good correlation was established between peak particle velocity and scale distance for this site. The established relationship and the results of the study are presented.     

人工神经网络方法预报长江上游流域面雨量的探讨

利用1998～2000年3～11月长江上游六大流域逐日降水实况和MAPS、T106(T213)、欧洲中心等数值产品资料，采用人工神经网络方法，建立了六大流域强降水面雨量等级预报模型。并于2002年6～9月进行了业务试验。     

董家山隧道小净距段爆破控制的数值模拟

结合都-汶高速公路董家山隧道小净距段的实际情况,应用数值模拟方法,对小净距段在爆破荷载作用下的相互影响问题进行了研究.结果表明在隧道爆破施工中应对迎爆侧进行重点监控,采用合理的开挖和加固方式将有效降低爆破施工对先建隧道的不利影响.研究结论可为类似小净距段隧道的爆破设计、施工及现场监控量测提供参考.     

Determination and application of the weights for landslide susceptibility mapping using an artificial neural network

The purpose of this study is the development, application, and assessment of probability and artificial neural network methods for assessing landslide susceptibility in a chosen study area. As the basic analysis tool, a Geographic Information System (GIS) was used for spatial data management and manipulation. Landslide locations and landslide-related factors such as slope, curvature, soil texture, soil drainage, effective thickness, wood type, and wood diameter were used for analyzing landslide susceptibility. A probability method was used for calculating the rating of the relative importance of each factor class to landslide occurrence. For calculating the weight of the relative importance of each factor to landslide occurrence, an artificial neural network method was developed. Using these methods, the landslide susceptibility index (LSI) was calculated using the rating and weight, and a landslide susceptibility map was produced using the index. The results of the landslide susceptibility analysis, with and without weights, were confirmed from comparison with the landslide location data. The comparison result with weighting was better than the results without weighting. The calculated weight and rating can be used to landslide susceptibility mapping.     

探索地理信息智能化处理技术

GIS自产生至今30多年来，世界各国都竞相开展GIS技术研究和应用开发，使其得到了迅速的发展，已形成较为完整的理论体系和大批实用系统，并已在社会各专业领域得到广泛应用并产生了巨大效益，目前，开始朝着产业化、集成化、智能化和社会化的方向发展。随着3S技术的集成应用，人们对GIS的运行效率及其深入的应用提出了更高的要求，地理信息智能化处理已成为地图学和GIS建设必须解决的问题，也是GIS发展的必然方向。在地理信息处理中采用人工智能技术，发展智能GIS或者专家GIS，是解决复杂地学问题的重要途径，也是目前GIS最吸引人的一个应用方向。目前在地理信息处理的许多领域都在使用人工智能技术，比如地图模式识别、地理数据的自动分类、地学专家系统、智能GIS、空间数据挖掘等。本文通过对当前地理信息智能化处理技术发展现状的阐述，旨在探索人工智能在地理信息处理中的应用前景。     

洛阳热电厂汽机主厂房2、3号机房爆破拆除技术

绍了采用控制爆破拆除大跨度工业厂房的施工方法，主要论述了在要求振动较小情况下，怎样控制爆破参数。     

Prediction of creep characteristic of rock under varying environment

The strain developed due to creep is mainly proportional to the logarithm of the time under load, and is mostly proportional to the stress and temperature. At higher temperature the creep rate falls slowly with respect to time, and the creep strain is proportional to a fractional power of time, with the exponent increasing as the temperature increases and reaching a value approximately one-third at temperatures of about 0.5°C. At these temperatures, the creep increases with stress according to a power greater than unity and possibly exponentially. It increases with temperature as (−U/kT), where U is an activation energy and k is Boltzman’s constant. There are different methods to determine the creep strain and the energy of Jog (B) including experimental methods, multivariate regression analysis, and by numerical simulation. These methods are less cumbersome and time consuming. In the present investigation, artificial neural network technique has been used for prediction of the creep strain and energy of Jog (B). Two different networks have been tested and validated. Both the networks have four input neurons and one hidden layer with five neurons, and one output neuron. The data for different rocks at temperatures up to 750°C under conditions of compressive or tortional stress are taken from the literatures. The training and testing data sets used were 163 and 14, respectively. To deal with the problem of overfitting of data, Bayesian regulation has been used and network is trained with suitable training epochs. The coefficients of correlation among the predicted and observed values are found high and they improve the confidence of the users. The mean absolute percentage error obtained are also very low.