地震动峰值速度对地下隧洞内力的影响研究

本文利用混合模拟方法,合成了加速度反应谱及峰值位移相同,而峰值速度不同的两组人工地震动时程.同时选择典型隧洞,进行了弹塑性地震反应分析,研究了地震波水平输入时,地震动峰值速度对隧洞内力的影响.在软土场地中,当加速度反应谱及峰值位移相同时,峰值速度较大的地震动时程将引起隧洞较大的弯矩和剪力.在硬土场地中,峰值速度对隧洞弯...     

Biofiltration of Hexane Vapor: Experimental and Neural Model Analysis

Biofiltration is a commonly practiced biological technique to remove volatile compounds from waste gas streams. From an industrial view‐point, biofilter (BF) operation should be flexible to handle temperatures and inlet load (IL) variations. A compost BF was operated at different temperatures (30–45°C) and at various inlet loading rates (ILR; 8–598 g m^?3 h^?1) under intermittent loading conditions. Complete removal of n‐hexane was observed at 30 and 35°C at ILRs up to 330 g m^?3 h^?1. Besides, 20–75% of the pollutant was removed at 40°C, corresponding to the different ILs applied to the BF. Increasing the temperature to 45°C decreased the removal efficiency (RE) significantly. A feed forward neural network was used to predict the RE of BF with temperature and ILR as the input variables. The experimental data was divided into training (2/3) and test datasets (1/3). The best structure of neural network was obtained by trial and error on the basis of the least differences between predicted and experimental values, as ascertained from their coefficient of regression (R²) values. The modeling results showed that a multilayer network with the topology 2?10?1 was able to predict BF performance effectively with R²‐value of 0.995 for the test data. The results from this study showed the predicting capability of ANNs which can be considered as an alternative for conventional knowledge‐based models.     

质点振动速度监测在爆破掘进工程中的应用

工程爆破施工过程中如何控制其对周围建筑物、正在施工项目和处于养护龄期内的混凝土结构的影响一直是爆破施工中的实际问题,直接关系爆破施工的单响药量和施工进度。隧道爆破掘进施工中为了追求施工进度,往往在爆破方案中使用较大的单响装药量,从而忽略了大药量爆破产生的冲击波效应对隧道岩壁、已有结构的破坏,本文简单介绍了施工前或施工中,进行质点振动速度监测的实际应用。     

Prediction of maximum wave-induced liquefaction in porous seabed using multi-artificial neural network model

In the last few decades, considerable efforts have been devoted to the phenomenon of wave-induced liquefactions, because it is one of the most important factors for analysing the seabed and designing marine structures. Although numerous studies of wave-induced liquefaction have been carried out, comparatively little is known about the impact of liquefaction on marine structures. Furthermore, most previous researches have focused on complicated mathematical theories and some laboratory work. In the present study, a data dependent approach for the prediction of the wave-induced liquefaction depth in a porous seabed is proposed, based on a multi-artificial neural network (MANN) method. Numerical results indicate that the MANN model can provide an accurate prediction of the wave-induced maximum liquefaction depth with 10% of the original database. This study demonstrates the capacity of the proposed MANN model and provides coastal engineers with another effective tool to analyse the stability of the marine sediment.     

Prediction of geometrical properties of perfect breaking waves on composite breakwaters

Breaking wave loads on coastal structures depend primarily on the type of wave breaking at the instant of impact. When a wave breaks on a vertical wall with an almost vertical front face called the “perfect breaking”, the greatest impact forces are produced. The correct prediction of impact forces from perfect breaking of waves on seawalls and breakwaters is closely dependent on the accurate determination of their configurations at breaking. The present study is concerned with the determination of the geometrical properties of perfect breaking waves on composite-type breakwaters by employing artificial neural networks. Using a set of laboratory data, the breaker crest height, h_b, breaker height, H_b, and water depth in front of the wall, d_w, from perfect breaking of waves on composite breakwaters are predicted using the artificial neural network technique and the results are compared with those obtained from linear and multi-linear regression models. The comparisons of the predicted results from the present models with measured data show that the h_b, H_b and d_w values, which represent the geometry of waves breaking directly on composite breakwaters, can be predicted more accurately by artificial neural networks compared to linear and multi-linear regressions.     

Evidences of increasing primary production in the ocean by Stommel's perpetual salt fountain

The American physical oceanographer Henry Stommel and co-workers proposed “the perpetual salt fountain” and suggested the possibility of upwelling deep seawater without an energy source. In the open ocean, deep seawater containing rich nutrients becomes a source of primary production. Previously, we have tested Stommel's hypothesis by numerical simulations and in ocean experiments, and confirmed the upwelling of a perpetual salt fountain. In the present study, we conducted an open-ocean experiment in the Philippines Sea, and succeeded to demonstrate an increase in chlorophyll concentration. The chlorophyll concentration at the pipe outlet was much greater than that in the surrounding seawater. Satellite ocean-color image around the pipe was analyzed, and the signal of artificial upwelling is investigated. Composite analysis of satellite chlorophyll image indicates an increased surface chlorophyll distribution in the vicinity of pipe position, in which the increasing signal is much larger than the expected production based on nutrient supply. Although the problem must be further discussed, this increased signal is shown to be statistically significant. This mechanism may contribute to effective utilization of fishery resources in subtropical oligotrophic region.     

Using Artificial Intelligence techniques to classify IRAS LRS spectra of evolved stars

The IRAS Low Resolution Spectrometer (LRS) covered the spectral region from 7µm to 23µm, and an Atlas was produced containing 5425 spectra. Most of the spectra were associated with evolved stars, including over 3000 spectra from the dust shells around O-rich stars. When Artificial Intelligence techniques were applied to the dataset, a new classification was derived. A scheme with 77 classes, grouped into 9 metaclasses, resulted, and for those types of spectra which were well represented in the initial dataset (i.e. the evolved stars) a very subtle classification was derived, often using line shapes, relative line strengths, or the presence of additional weak features.     

不同爆破法在钦陆一级公路路基施工的应用

广西地矿建设发展中心在承担钦州至灵山陆屋镇一级公路施工中，根据不同性质的地段，采用不同的爆破方法，即节约了成本，又圆满完成路基施工任务，获得良好的社会效益和经济效益。     

ARTIFICIAL INTELLIGENCE AND ITS APPLICABILITY TO GEOGRAPHICAL PROBLEM SOLVING

Artificial Intelligence (AI) may be regarded as an attempt to understand the processes of perception and reasoning that underlie successful problem-solving and to incorporate the results of this research in effective computer programs. At present, AI is largely a collection of sophisticated programming techniques. Many of these techniques are based on the premise that the manner in which knowledge is acquired, organized, accessed and modified in both humans and machines provides the basis of “intelligent” decision-making. The techniques of AI are applicable to a wide variety of geographical problems, including the modeling of individual and aggregate decision-making, and the construction of expert systems and “intelligent” geographical information systems.     

人工冻结梁构件中不同加筋作用的对比试验研究

考虑土层冻结构件中冻结管的加筋作用,对于增加冻结构件强度,减小变形量,提高人工冻结支护工程经济效益有重要作用。为研究冻结管对人工冻结构件的加筋作用,通过淤泥质粘土人工冻结梁构件三点弯试验,获得了各级载荷下构件的位移值。根据对冻土梁加筋与否,构件特定力学响应的比较,完成了冻结管加筋作用的量化分析。