Neural network modelling of planform geometry of headland-bay beaches

The shoreline of beaches in the lee of coastal salients or man-made structures, usually known as headland-bay beaches, has a distinctive curvature; wave fronts curve as a result of wave diffraction at the headland and in turn cause the shoreline to bend. The ensuing curved planform is of great interest both as a peculiar landform and in the context of engineering projects in which it is necessary to predict how a coastal structure will affect the sandy shoreline in its lee. A number of empirical models have been put forward, each based on a specific equation. A novel approach, based on the application of artificial neural networks, is presented in this work. Unlike the conventional method, no particular equation of the planform is embedded in the model. Instead, it is the model itself that learns about the problem from a series of examples of headland-bay beaches (the training set) and thereafter applies this self-acquired knowledge to other cases (the test set) for validation. Twenty-three headland-bay beaches from around the world were selected, of which sixteen and seven make up the training and test sets, respectively. As there is no well-developed theory for deciding upon the most convenient neural network architecture to deal with a particular data set, an experimental study was conducted in which ten different architectures with one and two hidden neuron layers and five training algorithms – 50 different options combining network architecture and training algorithm – were compared. Each of these options was implemented, trained and tested in order to find the best-performing approach for modelling the planform of headland-bay beaches. Finally, the selected neural network model was compared with a state-of-the-art planform model and was shown to outperform it.     

Wavelet regression model as an alternative to neural networks for monthly streamflow forecasting

The accuracy of the wavelet regression (WR) model in monthly streamflow forecasting is investigated in the study. The WR model is improved combining the two methods—the discrete wavelet transform (DWT) model and the linear regression (LR) model—for 1‐month‐ahead streamflow forecasting. In the first part of the study, the results of the WR model are compared with those of the single LR model. Monthly flow data from two stations, Gerdelli Station on Canakdere River and Isakoy Station on Goksudere River, in Eastern Black Sea region of Turkey are used in the study. The comparison results reveal that the WR model could increase the forecast accuracy of the LR model. In the second part of the study, the accuracy of the WR model is compared with those of the artificial neural networks (ANN) and auto‐regressive (AR) models. On the basis of the results, the WR is found to be better than the ANN and AR models in monthly streamflow forecasting. Copyright © 2009 John Wiley & Sons, Ltd.     

路堑边坡开挖对邻近既有隧道影响研究

设计中的承德迎宾大道路堑边坡邻近既有京承铁路21号隧道,边坡开挖后会对既有隧道产生不利影响。根据现场试验取得的围岩及隧道衬砌的力学参数,对边坡开挖进行了静力学和动力学数值分析,研究了路堑边坡可开挖的最大范围及爆破振动对隧道的影响。结果表明：当路堑二级边坡坡脚距离隧道中心线距离大于等于30 m时,边坡开挖后隧道衬砌内力虽有增加,但仍可保证结构安全。进行边坡爆破开挖时,如考虑3倍的安全系数,当京承铁路21号隧道衬砌振速小于1.5 cm/s时,隧道衬砌内产生的动拉应力小于衬砌极限抗拉强度,可按此控制指标进行现场监测。     

公路软基沉降预测的支持向量机模型

提出了基于支持向量机（SVM）模型对公路软基沉降进行预测的一种新方法,工程实例预测结果表明,在同样的训练均方误差下,SVM模型预测能力要优于BP神经网络模型,同时该模型能够综合利用分级加载过程中的沉降观测数据作为训练样本集,比仅依靠预压期内部分实测沉降数据的双曲线法更能反映地基土的变形趋势。因此,将建立的SVM模型应用于公路软基沉降预测能够更准确地反映实际沉降过程     

分形理论在空间网络分布特征研究中的应用

探讨了以图论为基础的空间网络的测度方法,以及以分形理论为基础的表达空间网络分布特征的几种分维数,并阐述了各种分维数的地理意义,最后对于分维数的测算进行了评述.     

沙漠区调水工程施工道路的设计与施工

石羊河下游地区近年来由于河水注入量的持续减少,使得依赖河水补给的地下水相应减少,造成区域性地下水位的下降,从而出现了一系列的生态环境问题。石羊河流域下游的民勤绿洲面临严重的生态危机,如何抢救和保护民勤绿洲,引起了社会各界的广泛关注。为了恢复和重建受损的生态系统,实施了下游调水工程——甘肃省景泰川电力提灌二期向民勤调水工程,该工程是国内第一个长距离穿越沙漠的跨流域调水工程,线路经过腾格里沙漠流动沙丘区,无水无路,施工条件非常艰苦;经过试验研究,提出了以当地材料为主的沙漠区施工道路的设计与施工方案。工程施工和运行实践表明,该方案施工简便,费省效宏。     

甘肃道路冰锥病害及防治技术

甘肃省大部分地区位于寒冷地区,各种道路冻害十分严重.通过调研,分析了甘肃省道路冰锥分布、发生机理及危害,针对冰锥类型提出了相应的防治措施.甘肃省道路冰锥主要分布在甘南高原、祁连山区及东段的乌鞘岭地区.冰锥类型多样,威胁道路安全的冰锥类型主要为工程活动诱发冰锥,其次是天然形成的山坡冰锥.在213国道合作-郎木寺改建工程中利用了提高路基、排水沟与涵洞结合、渗水盲沟及改变水流方向等方法防治道路冰锥,效果理想.     

高陡山区开采自然坡失稳分析的神经网络方法

针对山区地下开采自然边坡稳定性分析问题,根据大量工程实测资料,建立了人工神经网络预测模型。通过对人工神经网络算法的改进,选取适当的动量项系数及变步长方法,对已有的实测资料进行了训练和测试,并对丁家河磷矿自然边坡稳定性进行了具体的预测分析,理论计算结果与工程实际情况一致。分析结果表明,所建立的理论模型可用于山区磷矿开采自然边坡稳定性预测分析。     

模糊综合评判方法在四川某公路深挖路堑边坡稳定性分析中的应用

应用模糊综合评判方法分析研究四川某公路深挖路堑边坡的稳定性 ,研究表明边坡整体及部分块体在边坡开挖后均处于基本稳定状态 ,为边坡的治理提供了依据。