Response-based method for determining the extreme behaviour of floating offshore platforms

The accurate prediction of extreme excursion and mooring force of floating offshore structures due to multi-variete environmental conditions which requires the joint probability analysis of environmental conditions for the worst case situation is still impractical as the processing of large amount of met-ocean data is required. On the other hand, the simplified multiple design criteria (e.g. the N-year wave with associated winds and currents) recommended by API known as traditional method does lead neither to the N-year platform response nor to the N-year mooring force. Therefore, in order to reduce the level of conservatism as well as uncertainties involved in the traditional method the response-based method can be used as a reliable alternative approach. In this paper this method is described. In order to perform the calculations faster using large databases of sea states, Artificial Neural Networks (ANN) is designed and employed. In the paper the response-based method is applied to a 200,000 tdw FPSO and the results are discussed.     

假晴东方鲀人工育苗研究

假晴东方鲀人工育苗获得４１．８ｍｍ种苗３５１０００尾，每立方水体出 苗８６０尾。越冬成活率９１．６％，增重率６１．８％。利用室内水泥池及养虾土池 养成商品鱼，分别获得１９６９ ｇ／ｍ３和 ２０８ ｋｇ／亩的收成。     

黑鲷三倍体的人工诱导研究

分别于1989年3—6月、1990年3—5月在天津和青岛两地采用冷、热休克法对黑鲷进行三倍体的人工诱导研究。结果表明,冷休克法诱导三倍体的效果好于热休克法,其最适诱导条件为卵子在受精后5min,温度为3—4℃的海水中处理10—20min,三倍体诱导率最高可达50.35％;正交试验证明,三倍体率受冷、热休克法的三因素影响程度的顺序由大到小依次为,处理时间、处理时刻、处理温度;黑鲷染色体数目:2n=48,3n=72,它们的染色体组型也同时获得。     

含矿预测的人工神经网络方法

运用神经网络模型的一典型模型——“反向传播”模型的改进形式,处理矿产资源统计预测问题,得出与数量化理论Ⅱ处理极为相似的结果.     

The almost constant-speed two-body problem with resistance

The almost constant-speed motion of a mass acted upon by a Newtonian attraction and a resisting force is treated. The equation of orbit is derived for a specific type of resistance which covers the familiar case of Danby's drag(=r ^–2) whilst the vector invariants are obtained by direct operation on the vector form of the equation of motion.     

Aftershocks series monitoring of the September 18, 2004 M = 4.6 earthquake at the western Pyrenees: A case of reservoir-triggered seismicity?

On September 18, 2004, a 4.6 mbLg earthquake was widely felt in the region around Pamplona, at the western Pyrenees. Preliminary locations reported an epicenter less than 20 km ESE of Pamplona and close to the Itoiz reservoir, which started impounding in January 2004. The area apparently lacks of significant seismic activity in recent times. After the main shock, which was preceded by series of foreshocks reaching magnitudes of 3.3 mbLg, a dense temporal network of 13 seismic stations was deployed there to monitor the aftershocks series and to constrain the hypocentral pattern. Aftershock determinations obtained with a double-difference algorithm define a narrow epicentral zone of less than 10 km², ESE–WNW oriented. The events are mainly concentrated between 3 and 9 km depth. Focal solutions were computed for the main event and 12 aftershocks including the highest secondary one of 3.8 mbLg. They show mainly normal faulting with some strike-slip component and one of the nodal planes oriented NW–SE and dipping to the NE. Cross-correlation techniques applied to detect and associate events with similar waveforms, provided up to 33 families relating the 67% of the 326 relocated aftershocks. Families show event clusters grouped by periods and migrating from NW to SE. Interestingly, the narrow epicentral zone inferred here is located less than 4 km away from the 111-m high Itoiz dam. These hypocentral results, and the correlation observed between fluctuations of the reservoir water level and the seismic activity, favour the explanation of this foreshock–aftershock series as a rapid response case of reservoir-triggered seismicity, burst by the first impoundment of the Itoiz reservoir. The region is folded and affected by shallow dipping thrusts, and the Itoiz reservoir is located on the hangingwall of a low angle southward verging thrust, which might be a case sensible to water level fluctuations. However, continued seismic monitoring in the coming years is mandatory in this area to infer more reliable seismotectonic and hazard assessments.     

Application of back-propagation networks in debris flow prediction

Debris flows have caused serious loss of human lives and a lot of damage to properties in Taiwan over the past decades. Moreover, debris flows have brought massive mud causing water pollution in reservoirs and resulted in water shortage for daily life locally and affected agricultural irrigation and industrial usages seriously. A number of methods for prediction of debris flows have been studied. However, the successful prediction ratio of debris flows cannot always maintain a stable and reliable level. The objective of this study is to present a stable and reliable analytical model for occurrence predictions of debris flows. This study proposes an Artificial Neural Networks (ANN) model that was constructed by seven significant factors using back-propagation (BP) algorithm. These seven factors include (1) length of creek, (2) average slope, (3) effective watershed area, (4) shape coefficient, (5) median size of soil grain, (6) effective cumulative rainfall, and (7) effective rainfall intensity. A total of 178 potential cases of debris flows collected in eastern Taiwan were fed into the ANN model for training and testing. The average ratio of successful prediction reaching 93.82% demonstrates that the presented ANN model with seven significant factors can provide a stable and reliable result for the prediction of debris flows in hazard mitigation and guarding systems.     

Modeling the cyclic swelling pressure of mudrock using artificial neural networks

The stochastic nature of the cyclic swelling behavior of mudrock and its dependence on a large number of interdependent parameters was modeled using Time Delay Neural Networks (TDNNs). This method has facilitated predicting cyclic swelling pressure with an acceptable level of accuracy where developing a general mathematical model is almost impossible. A number of total pressure cells between shotcrete and concrete walls of the powerhouse cavern at Masjed–Soleiman Hydroelectric Powerhouse Project, South of Iran, where mudrock outcrops, confirmed a cyclic swelling pressure on the lining since 1999. In several locations, small cracks are generated which has raised doubts about long term stability of the powerhouse structure. This necessitated a study for predicting future swelling pressure. Considering the complexity of the interdependent parameters in this problem, TDNNs proved to be a powerful tool. The results of this modeling are presented in this paper.     

基于GIS与ANN耦合技术的地裂缝灾情非线性模拟预测系统——以山西榆次地裂缝灾害为例

现代地裂缝在世界许多国家普遍存在 ,已成为当今世界范围内的主要地质灾害之一。本文在详尽分析了山西榆次地裂缝的各个致灾因子的基础上 ,利用GIS技术建立了地质学意义上的专题层 ;然后采用人工神经网络技术构建出了地裂缝灾害活动性的评价模型 ,并建立了地裂缝活动性的评价系统 ,对榆次地裂缝进行了灾害活动性评价 ,为榆次市城建和国土规划等部门的正确决策提供了重要的科学依据     

喀什"奇异震群"与构造地震的对比研究

20 0 1年 11月 14、 15日新疆喀什台记录到 86次振动事件 (“奇异震群”事件 )。为识别其性质 ,应用波谱分析、偏振分析等方法 ,对该事件和同范围构造地震的地震波特征进行了对比研究 ,发现 :(1)喀什“震群”的全波列衰减比构造地震的全波列衰减快很多 ;(2 )与构造地震的速度谱相比 ,“震群”的频谱相对较为“集中”,而构造地震的则较为“发散”;(3)“震群”位移谱的拐角频率和卓越频率均较构造地震的相应量高出一倍多 ,且离散较小。通过对喀什“奇异震群”这些特征进行分析研究 ,认为这一事件极有可能是人工事件