自组织学习联想神经树在多金属成矿预测中的应用

本文运用自组织学习联想神经树，对鄂东南地区４４个铁帽进行了计算机识别，识别成功率达１００％，结果表明，该方法性能良好，可望成为多金属成矿预测的一种有效的辅助手段。     

南宁机务段洗修库拆除爆破

文章介绍了南宁机务段洗修库采用一次性控制定向倒塌的拆除爆破设计及施工情况，着重论述了爆破方案的选择以及爆破高度、孔网参数、单孔装药量、炮眼布置、装药堵塞、起爆网路与起爆顺序、爆破安全距离等的设计，进而对此次爆破效果和体会进行总结和评价。     

后注浆刚性桩复合地基的工程实践

某高层建筑物原设计为人工挖孔扩底桩基础,施工过程中由于土层软弱以及地下水影响无法扩底成桩。本文介绍了对上述工程实施的大直径刚性桩复合地基设计方案,该方案利用后注浆技术提高了人工挖孔桩单桩承载力。基桩检测和建筑物沉降观测结果表明该工程地基处理方案是成功的。     

A 24‐h Forecast of Oxidant Concentration in Tokyo Using Neural Network and Fuzzy Learning Approach

In this study, several types of adaptive network‐based fuzzy inference system (ANFIS) with different membership functions (MFs) and artificial neural network (ANN) were employed to predict hourly photochemical oxidants that were oxidizing substances such as ozone and peroxiacetyl nitrate produced by photochemical reactions. The results indicated that ANFIS statistically outperforms ANN in terms of hourly oxidant prediction. The minimum mean absolute percentage errors (MAPEs) of 4.99% could be achieved using ANFIS with bell shaped MFs. The maximum correlation coefficient, the minimum mean square errors, and the minimum root mean square errors were 0.99, 0.15, and 0.39, respectively. ANFIS's architecture consists of both ANN and fuzzy logic including linguistic expression of MFs and if‐then rules, so it can overcome the limitations of traditional neural network and increase the prediction performance.     

Artificial neural networks and geostatistical models for housing valuations in urban residential areas

Property valuation studies often use classical statistics techniques. Among these techniques, the Artificial Neural Networks are the most applied, overcoming the inflexibility and the linearity of the hedonic models. Other researchers have used Geostatistics techniques, specifically the Kriging Method, for interpreting spatial-temporal variability and to predict housing unit prices. The innovation of this study is to highlight how the Kriging Method can help to better understand the urban environment, improving the results obtained by classical statistics. This study presents two different methods that share the general objective of extracting information regarding a city’s housing from datasets. The procedures applied are Ordinary Kriging (Geostatistics) and Multi-Layer Perceptron algorithm (Artificial Neural Networks). These methods were used to predict housing unit prices in the municipality of Pozuelo de Alarcon (Madrid). The implementation of both methods provides us with the urban characteristics of the study area and the most significant variables related to price. The main conclusion is that the Ordinary Kriging models and the Neural Networks models, applied to predicting housing unit prices are necessary methodologies to improve the information obtained in classical statistical techniques.

Abbreviations: ANN: Artificial Neural Networks; OK: ordinary Kriging; MLP: multi-layer perceptron     

A bottom‐up approach to discover transition rules of cellular automata using ant intelligence

This paper presents a new method to discover transition rules of geographical cellular automata (CA) based on a bottom‐up approach, ant colony optimization (ACO). CA are capable of simulating the evolution of complex geographical phenomena. The core of a CA model is how to define transition rules so that realistic patterns can be simulated using empirical data. Transition rules are often defined by using mathematical equations, which do not provide easily understandable explicit forms. Furthermore, it is very difficult, if not impossible, to specify equation‐based transition rules for reflecting complex geographical processes. This paper presents a method of using ant intelligence to discover explicit transition rules of urban CA to overcome these limitations. This ‘bottom‐up’ ACO approach for achieving complex task through cooperation and interaction of ants is effective for capturing complex relationships between spatial variables and urban dynamics. A discretization technique is proposed to deal with continuous spatial variables for discovering transition rules hidden in large datasets. The ACO–CA model has been used to simulate rural–urban land conversions in Guangzhou, Guangdong, China. Preliminary results suggest that this ACO–CA method can have a better performance than the decision‐tree CA method.     

爆破荷载作用下岩体损伤破坏的分形研究

通过对现有损伤参量的定义方法的分析,尝试采用分形维数作为表征岩石损伤的参量,建立分形维数与损伤程度的相对关系。根据盒维数算法思想及数字图像存储原理,编写了基于Matlab的数字图像的简易盒维数算法。结合实际工程爆破施工,采用数字式全景钻孔摄像系统进行了现场测试。通过对实测的爆前、爆后岩体的影像资料进行裂纹提取及图形处理,得到了爆破前后岩体损伤裂纹对应的分形维数,确定了爆破作用下所产生的损伤增量。结果表明,用分形维数来描述岩石的损伤,克服了其他定义方法涉及过多岩石特性参数的不便,其结果为研究岩石的损伤程度及演化过程,探索了一条新的途径。     

深部煤层卸压爆破防治冲击地压效果的数值模拟研究

卸压爆破技术可降低煤岩体中应力集中程度,有效避免冲击地压灾害的发生。以跃进煤矿23 130工作面卸压爆破工程为例,运用FLAC3D软件进行数值模拟,分析了不同条件下卸压爆破前后围岩应力分布和转移规律,并结合电磁辐射监测技术,对采掘现场卸压效果进行了检验。结果表明,装药量越大,应力峰值降低和转移越明显;煤层厚度越大,应力峰值降低越明显,但推移距离基本不变;采深越大,应力峰值降低越大,但峰值区推移距离减小;爆破区域的电磁辐射检测值降低明显,表明卸压爆破效果理想。综合利用数值模拟方法和电磁辐射监测技术,可为采掘区域合理确定爆破参数和有效降低冲击地压危险提供依据。     

空孔形状对岩石定向断裂爆破影响规律的研究

利用新型数字激光动态焦散线试验系统,采用有机玻璃板试件进行模型试验,研究了含预制不同形状空孔对岩石定向断裂控制爆破的影响规律。试验结果表明：在定向断裂控制爆破中,设置菱形空孔更有利于实现精细化定向断裂控制爆破,可有效保证巷道周边眼爆破的成型效果;对比含3种不同形状空孔试件爆生主裂纹扩展速度可知,含菱形空孔的扩展速度最大,含圆形空孔的次之,含带刻槽圆形空孔的最低;含圆形空孔试件爆生主裂纹端部动态应力强度因子总体比含菱形和切槽圆形空孔试件爆生主裂纹端部动态应力强度因子大;主裂纹扩展中后期阶段,含菱形空孔试件爆生主裂纹端部动态应力强度因子相对较小。     

深部岩体地应力瞬态卸载诱发振动效应的影响因素

针对深埋隧洞爆破开挖振动控制,以瀑布沟水电站2#引水隧洞爆破开挖为工程背景,讨论了地应力瞬态卸荷诱发振动特征及影响因素,并结合实测数据采用数值模拟的方法进行了验证分析。研究发现：中、高地应力条件下开挖面初始应力的瞬间释放诱发的振动是爆破开挖诱发振动的重要组成部分;当波阻抗一定时,开挖面初始应力动态卸荷诱发振动由地应力水平、开挖面初始应力、开挖面面积和振动衰减指数共同决定,动态卸荷诱发振动最强段发生在开挖面的初始应力和开挖面面积综合效应最大的位置;深埋隧洞爆破开挖振动的强度是由爆炸荷载和开挖面初始应力动态卸荷效应共同决定的,减小炮孔排距和进尺、采用较小洞径和分部开挖能有效降低地应力瞬态卸荷效应强度,控制深埋隧洞的爆破振动强度。