新型大孔径静态破碎技术的试验研究

在传统静态爆破的基础上,提出了一种新型大孔径静态爆破技术,使用新型扩孔钻头结合机械堵孔器解决了大孔径静态爆破的冲孔问题。由于孔径的增大,使得破碎剂膨胀压力增大,膨胀剂反应速度加快,加大了堵孔难度。设计的新型扩孔钻头和新型堵孔器进行了孤岩块的大孔径静态爆破试验,试验结果证实了该新型大孔径静态爆破技术的可行性,达到了提高爆破能力和缩短破坏时间的预期目的。为今后的敏感地区岩石爆破工程开挖提供了新途径。     

Prediction of berm geometry using a set of laboratory tests combined with teaching–learning-based optimization and artificial bee colony algorithms

Understanding sediment movement in coastal areas is crucial in planning the stability of coastal structures, the recovery of coastal areas, and the formation of new coast. Accretion or erosion profiles form as a result of sediment movement. The characteristics of these profiles depend on the bed slope, wave conditions, and sediment properties. Here, experimental studies were performed in a wave flume with regular waves, considering different values for the wave height (H₀), wave period (T), bed slope (m), and mean sediment diameter (d₅₀). Accretion profiles developed in these experiments, and the geometric parameters of the resulting berms were determined. Teaching–learning-based optimization (TLBO) and artificial bee colony (ABC) algorithms were applied to regression functions of the data from the physical model. Dimensional and dimensionless equations were found for each parameter. These equations were compared to data from the physical model, to determine the best equation for each parameter and to evaluate the performances of the TLBO and ABC algorithms in the estimation of the berm parameters. Compared to the ABC algorithm, the TLBO algorithm provided better accuracy in estimating the berm parameters. Overall, the equations successfully determined the berm parameters.     

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.     

Fragility functions of blockwork wharves using artificial neural networks

The use of artificial neural networks in the general framework of a performance-based seismic vulnerability evaluation for earth retaining structures is presented. A blockwork wharf-foundation-backfill complex is modeled with advanced nonlinear 2D finite difference software, wherein liquefaction occurrence is explicitly accounted for. A simulation algorithm is adopted to sample geotechnical input parameters according to their statistical distribution, and extensive time histories analyses are then performed for several earthquake intensity levels. In the process, the seismic input is also considered as a random variable. A large dataset of virtual realizations of the behavior of different configurations under recorded ground motions is thus obtained, and an artificial neural network is implemented in order to find the unknown nonlinear relationships between seismic and geotechnical input data versus the expected performance of the facility. After this process, fragility curves are systematically derived by applying Monte Carlo simulation on the obtained correlations. The novel fragility functions herein proposed for blockwork wharves take into account different geometries, liquefaction occurrence and type of failure mechanism. Results confirm that the detrimental effects of liquefaction increase the probability of failure at all damage states. Moreover, it is also demonstrated that increasing the base width/height ratio results in higher failure probabilities for the horizontal sliding than for the tilting towards the sea.     

试谈岩溶石漠化地区植树造林构想——爆破造隙蓄水凿岩挖坑培土植树造林法

依据岩溶石山区环境地质和水文地质等特征,结合贵州凯里在白云岩极浅覆盖区挖坑植树造林的成功事例,笔者首次提出了爆破造隙蓄水凿岩挖坑培土植树造林的构想,即先钻孔打眼装药爆破,将岩体适当震松,然后凿岩挖坑培土植树,利用爆破漏斗的囊状裂隙空间构建局部地下水仓,并依靠裂隙水毛细管作用向上供水和植物根系向下生长吸取地下水及水中的有效元素养分等,使种植的树木长期得到滋养而易于成活成林成材。该法简单易行,在岩溶石山区以及其它地层岩石区广泛使用,可望达到较快恢复生态环境的预期效果。      

爆破地震效应对坡体稳定性的影响

天然边坡在开挖过程中,由于爆破动力作用破坏了原岩的完整性,造成边坡岩体发生局部或整体层裂而形成边坡岩体松动带,使岩体的自稳能力降低(边坡抗滑力减小),甚至可能导致路堑滑坡或坍塌.而目前在边坡设计中没有考虑由于开挖爆破地震效应对边坡稳定性的影响,从而造成个别边坡在开挖过程中或施工完成后产生滑动.通过现场爆破地震效应试验,根据应力波在岩体中的传播规律和运动规律,得出岩体内质点振动加速度和不同部位岩体平均加速度对边坡稳定性的影响效果,给出了水平和垂直方向振动加速度作用下边坡安全系数的计算公式,并通过了实例验证,从而为边坡稳定性设计提供了有力依据.     

煤矿巷道底板冲击矿压发生的原因及控制研究

巷道底板水平应力是导致底板冲击矿压发生的主要因素,根据巷道底板冲击矿压的特点,建立了底板冲击矿压发生条件与影响因素的力学模型,初步确定了底板冲击矿压危险性系数的表达式。当底板岩层泊松比一定时,底板冲击矿压危险性系数与巷道埋深、巷道宽度的平方、水平构造应力、巨厚坚硬老顶影响系数成正比,与弹性模量、巷道底板软弱层厚度的平方成反比。通过数值模拟,发现巷道开挖后底板煤层的水平应力升高和垂直应力降低的规律,底板应力极易达到煤层破坏极限,在支护不当和外界扰动下容易发生底板冲击矿压。最后确定了底板强度弱化减冲原理,在跃进煤矿25110工作面下巷采取底板爆破卸压措施后取得了良好效果。研究成果为解决类似条件底板冲击矿压防治问题提供了理论依据和借鉴。     

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。