基于MDT的地震滑坡道路可通行性空间分布快速评估方法研究

如何预测地震滑坡道路可通行性空间分布是快速评估中的一个难题,为解决这一问题,本文提出一种基于MDT的地震滑坡道路可通行性空间分布快速评估方法。该方法包括3个步骤:①定制路段单元,将路段作为地震滑坡道路可通行性评估的基本单元,利用GIS将评估区内的道路划分成路段,制作每条路段两侧180m的缓冲区;②对路段进行地震滑坡属性赋值,统计各缓冲区范围内不同地震滑坡敏感性水平的像元数量,将统计结果作为对应路段的地震滑坡属性;③路段可通行性空间分布推断,利用MDT模型计算道路可通行性,最后进行地震滑坡道路可通行性空间分布制图。利用该方法对我国2008年汶川M_S8.0、2014年鲁甸M_S6.5和2012年彝良M_S5.6、M_S5.7地震灾区进行研究。其中,汶川地震灾区用来进行地震滑坡道路可通行性空间分布快速评估方法的建立和方法有效性的评价,鲁甸地震灾区和彝良地震灾区则用来对所建立方法在相似区域可移植性的评价。通过计算P值来检验模型的统计学显著性,并通过计算kappa值来评价模型反演结果与实际情况的一致性。结果表明模型计算出的道路可通行性是判断地震灾区道路是否因地震滑坡中断的良好指标;在允许一定误差的情况下,利用MDT模型进行地震滑坡道路可通行性空间分布快速评估的方法可以移植到其他相似区域。     

利用2014年云南地震验证与研究四川地震预警快速震级估算模型

地震预警系统是防震减灾中最为行之有效的方法之一,其中对地震震级的估算是非常重要且困难的。目前,比较成熟的地震震级估计方法包括：基于最大卓越周期（τ_p^max）、特征周期（τ_c）和最大位移幅值（P_d）方法。利用2014年云南地3次大地震主震及余震P波初期部分的信息,验证与研究了四川地区地震预警快速震级估算模型在云南地区的适用性,结果表明3种参数模型均能在短时间内有效地进行震级估算。对于本研究数据库而言,P_d-4 s时窗模型最优,3个参数模型求得的估计震级在大震下均没有出现明显的震级饱和现象。但由于目前难以在短时窗下得到准确的震源/中距,因此推荐在云南地区地震预警系统中使用τ_p^max和τ_c模型来估计震级。     

2010年青海玉树MS7.1地震序列ETAS模型参数及其变化特征研究

采用时间序列的“传染型余震序列”（ETAS）模型和最大似然法对2010年4月14日青海玉树M_S7.1地震序列的参数进行了估计。为考察其结果的稳定性,设定不同的截止震级和不同的拟合截止时间分别进行序列参数估算。选用截止震级M_C=M_L1.5对玉树M_S7.1地震序列整体的参数进行拟合,计算获得α=0.948 2,p=1.059 6和b=0.817 3。与中国大陆M>7.0地震序列的平均序列参数相比较表明,玉树M_S7.1地震序列表现为触发次级余震的能力较弱,序列衰减速率较快。研究表明,截止震级M_C对α、k和p值有一定的影响。随截止震级逐渐增大,参数k值逐渐减小,α值总体上有增加的趋势,而对p值的影响较小。在地震序列的早期阶段,ETAS模型参数中的p值和α值在震后14天内随时间的变化幅度较为明显,其后各参数变化相对平稳。     

基于综合刚度原理的陡坡基桩有限杆单元分析

为分析陡坡段桥梁基桩内力及变形特性,提出一种有限杆单元计算方法。首先,基于综合刚度原理并引入双参数地基模型,建立适用于陡坡基桩内力计算分析的修正有限杆单元刚度方程;在此基础上结合杆系结构受力特征,提出考虑P-Δ效应的杆单元修正矩阵;最后,根据有限杆单元计算原理,基于MATLAB平台编制计算程序,并对某工程实例进行分析。计算结果显示:改进有限杆单元方法可行且具有较高的精度;陡坡段桥梁基桩P-Δ效应显著,应在设计计算中予以考虑;在浅层地基土抗力计算中双参数地基模型较m法精度更高。     

基于经验参考场地的广义线性反演方法

为了克服广义线性反演方法（GIT）中理想的参考场地难以找到的局限性,本文将四分之一波长法计算的平均场地放大系数和谱衰减法计算的高频衰减参数作为GIT方法的经验参考场地（ERS）。以2008年5月至8月间28个强震动台站记录的95个汶川8.0级地震余震的615组强震动记录为例,通过与其他方法的比较,验证了ERS-GIT的合理性,并讨论了各类方法之间差异的原因。结果显示,ERS-GIT方法、传统GIT方法和非参数化GIT方法计算得到的平均应力降依次为1.15 MPa,0.78 MPa和0.52 MPa;ERS-GIT方法和传统GIT方法得到的品质因子分别为Q（f）=75.02f^1.27和Q（f）=65.56f^1.22。以场地的卓越频率为分界,ERS-GIT方法得到的局部场地响应在长周期和高频部分分别与H/V谱比法和传统GIT方法更为接近。     

新疆及邻区体波震级初步分析研究

依据《地震震级的规定（GB 17740—2017）》,分析了2009—2017年新疆地震台网所记录的新疆及邻区476次中深源地震事件,测定了13601个m_b（短周期体波震级）和12035个m_B（BB）（宽频带体波震级）的数据样本,回归分析m_b和m_B（BB）得到回归方程及量规函数,结果显示m_b和m_B（BB）相关系数为0.966,表明两者显著相关。因此,建议对于中强型中深源地震可以直接从原始速度型宽频带数字地震记录上测定长周期体波震级m_B（BB）,提高地震速报测定的速度和精度。通过震级偏差统计和台站场地响应计算,分析新疆地震台网中的XKR、HTA、ATS和KSZ等地震台站震级偏差较大的原因为砂岩、灰岩、砂土层等类型的台基放大了场地响应,说明台基类型对体波震级偏差的影响较大。与NEIC测定的体波震级对比时,发现新疆地震台网测定体波震级平均偏大0.42级,且偏差随着震源深度的增加有增大的趋势。     

甘肃及邻区基于地震预警参数τc和Pd的震级快速计算模型研究

地震预警作为目前能够有效减轻地震灾害的手段之一,可以在破坏性地震动来临之前提供几秒至几十秒的预警时间,提醒预警区域采取紧急处置措施以减轻人员伤亡和财产损失。地震预警是一个复杂的系统,其中利用地震参数快速、准确地进行实时震级估算是其有效发挥减灾作用的关键。文章基于2012—2020年发生在甘肃省及邻区的190个地震共821条记录(3.1≤M≤6.6),计算分析在P波1~10 s时间窗长度内,两种地震预警参数(卓越周期τ_c和位移幅值P_d)分别在垂直向、水平向和三分向均值测项三种不同条件下与峰值参数PGV、PGD的相关性,拟合相应的预警震级快速计算模型,并将估算震级M_E与实际震级M_C进行比较。结果表明：时间窗长度的改变对结果有一定的影响,而测项改变造成的差别较小。此外,在拟合度、预测残差均值和标准差,以及残差分布等预测效果方面,采用参数P_d的模型性能优于参数τ_c。考虑到参数和震级的实时计算以及预警信息发布的时效性要求,推荐在该地区应用垂直向记录P波到时3 s后的参数P_d来进行地震预警震级实时快速计算。     

基于自相似河网结构的河网消退系数Cs计算方法研究

新安江模型河网汇流参数Cs对洪峰模拟影响较大,目前Cs的确定需依赖于大量的历史数据,因此Cs的确定成为无资料地区和资料匮乏区水文模型应用中亟需解决的棘手问题.本文基于参数的物理意义,通过自相似河网结构的假定,构建Cs与河网形态、流域下垫面特征的相关联系,提出基于河链蓄量方程的Cs估算方法,对半干旱、半湿润和湿润地区等不同水文气象分区的11个流域的Cs值进行推算并代入新安江模型中进行模拟,经比较发现,11个流域子流域Cs计算均值与新安江模型率定结果相近,说明该Cs计算方法是合理的.选取陈河、屯溪两个典型流域研究单元流域属性对Cs的影响,由结果可以看出Cs与流域面积、河链数、河宽呈正相关,与单元流域距离出口的远近呈负相关,这表明流域分块后各单元流域Cs值不一致,而新安江模型中采用相同Cs值对不同单元进行调节必然会造成汇流计算的误差.为进一步提高该方法在无资料地区的应用效果,将新安江模型汇流模块修改为每个单元使用对应的Cs计算值进行滞后演算,以陈河和屯溪流域为例采用新安江模型Cs率定值、Cs计算均值以及修改后新安江模型3种不同方案进行模拟比较,从模拟结果可以得出,修改后的模型具有明显优势,将模型参数与下垫面条件建立了联系,模型物理机制提高且参数的独立性增强,对于新安江模型在无资料地区的应用具有重要的指导意义.     

芦山地震和九寨沟地震台站实测震级差异性分析

本文选取2013年芦山地震和2017年九寨沟地震波形,重新量取垂直向振幅,计算宽频带面波震级M_S（BB）,分析各台站实测震级出现方向性差异的原因。其中,通过572个宽频带台站实测芦山地震震级M_S（BB）7.1,通过603个宽频带台站实测九寨沟地震震级M_S（BB）6.9。芦山地震实测震级大于M_S（BB）7.3的台站呈现WN-ES向分布,与断层倾向一致;实测震级小于M_S（BB）7.0的台站呈现NE-WS向分布,与其所在断层走向一致。九寨沟地震实测震级大于M_S（BB）7.0的台站分布呈现NE向分布,与断层倾向一致;实测震级小于M_S（BB）6.8的台站总体分布较为离散,大体呈现NW-SE向分布,与树正断裂走向一致。实测震级偏大的台站方向性分布与多普勒效应和P波辐射花样联系不明显。对比分析芦山地震和九寨沟地震,去除场地响应和仪器自身影响,台站实测震级差异性仍然存在,因此,台站实测震级差异性是由于受到了多普勒效应、辐射花样、仪器和场地响应之外的因素影响。综合考虑地震震级涉及的影响因素,芦山地震和九寨沟地震的台站实测震级差异性可能与地震波的传播路径有关。     

2004年12月26日印度尼西亚MW9.0大震前后震源机制一致性变化特征研究

本文选取2004年12月26日发生在印度尼西亚西北近海、震中位于巽他海沟的东侧的M_W9.0地震的余震分布空间范围为研究区域,分析了该区域震源机制,并利用震源机制和构造应力场的一致性参数a进行了地震检验。研究结果表明,M_W≥7.5地震之前,都有参数a降低的现象,M_W9.0地震前,a值都有动荡起伏的情况。该研究区长达数千千米,而连续发生的M_W≥5.0地震的震源机制与构造应力场一致,应当不是随机现象,似可视为具某种前兆意义的现象。     

Evaporation modelling by heuristic regression approaches using only temperature data

Accurate estimation of pan evaporation (E_pan) is very important in water resources management, irrigation scheduling and water budget of lakes. This study investigates the accuracy of two heuristic regression approaches, multivariate adaptive regression splines (MARS) and M5 model tree (M5Tree) in estimating pan evaporation using only temperature data as input. Monthly minimum temperature, maximum temperature and E_pan data from three Turkish stations were used, with month number (periodicity information) added as input to see its effect on estimation accuracy. The models were compared with the calibrated Hargreaves-Samani (CHS), Stephens-Stewart (SS) and multiple linear regression methods. Three different train-test splitting strategies (50%–50%, 60%–40% and 75%–25%) were employed for better evaluation of the applied methods. The results show that the MARS method generally estimated monthly E_pan with higher accuracy compared to the M5Tree, CHS and SS methods. When extraterrestrial radiation, calculated from Julian date and latitude information, was used as input to the SS instead of solar radiation, satisfactory estimates were obtained. A positive effect on model accuracy was observed when involving periodicity information in inputs and increasing training data length.     

Pile group response to liquefaction-induced lateral spreading: E-Defense large shake table test

This paper presents the results of a large-scale shake table test at E-Defense facility on a pile group located adjacent to a gravity-type quay wall and were subjected to liquefaction-induced large ground displacements. Extensive liquefaction-induced large ground lateral spreading displaced the quay wall about 2.2 m and damaged the pile foundation. The pile foundation consisted of a six-pile group which supported a footing and a superstructure model. Large lateral soil displacements were measured by several sensors such as inclinometers and the results favorably agreed with the directly observed deformations. Soil lateral displacement decreased as the distance from the quay wall increased landward. The piles were densely instrumented and the measured bending strain records were able to explain the damage to the piles. Lateral pressures of the liquefied soil exerted on the piles were measured using earth pressure (EP) sensors. The application of two design guidelines (JRA [1] and JSWA [2]) for estimation of liquefaction-induced lateral pressure on piles is discussed and their advantages and shortcomings are addressed. Furthermore, two simplified methods (Shamoto et al. [3] and Valsamis et al. [4]) are employed to predict the extent of liquefaction-induced large ground displacements and they are compared to the measured deformations. Finally, their accuracy for predicting the liquefaction-induced lateral displacements is evaluated and practical recommendations are made.     

Empirical models for estimating liquefaction-induced lateral spread displacement

Existing empirical models for estimating liquefaction-induced lateral spread displacement (D_LL) have been derived from a dataset poorly distributed with respect to earthquake magnitude and source distance, and also produced from different tectonic source types and faulting mechanisms. Both the poor distribution and mixed tectonic source types and faulting mechanisms of the data have an adverse impact on the reliability of the empirical models. To overcome these problems in the development of empirical models, we replace the direct use of magnitude and source distance with pseudo-displacement derived from spectral acceleration attenuation models that are well supported by earthquake data, and use a modification factor to account for effects of the non-linear soil response. Attenuation models derived from very large and reasonably well-balanced datasets have been selected, one being a Japanese attenuation model and other being a combination of the Sadigh et al. model and the Youngs et al. model. These models are capable of accounting for the effects of earthquake tectonic source type and faulting mechanism. We determined the model coefficients by selecting the pseudo-displacements calculated for a number of spectral periods to achieve an unbiased distribution of residuals with respect to earthquake magnitude and source distance. Sensitivity analyses have been carried out based on the new and existing models, and show that the new model is more robust than the existing models. Comparison with a limited number of data from the 1997 Kocaeli, Turkey earthquake suggests our model provides comparable liquefaction-induced lateral displacement D_LL estimates.     

Suspended load estimation using L1-fuzzy regression,L2-fuzzy regression and MARS-fuzzy regression models

ABSTRACT

The problem of estimation of suspended load carried by a river is an important topic for many water resources projects. Conventional estimation methods are based on the assumption of exact observations. In practice, however, a major source of natural uncertainty is due to imprecise measurements and/or imprecise relationships between variables. In this paper, using the Multivariate Adaptive Regression Splines (MARS) technique, a novel fuzzy regression model for imprecise response and crisp explanatory variables is presented. The investigated fuzzy regression model is applied to forecast suspended load by discharge based on two real-world datasets. The accuracy of the proposed method is compared with two well-known parametric fuzzy regression models, namely, the fuzzy least-absolutes model and the fuzzy least-squares model. The comparison results reveal that the MARS-fuzzy regression model performs better than the other models in suspended load estimation for the particular datasets. This comparison is done based on four goodness-of-fit criteria: the criterion based on similarity measure, the criterion based on absolute errors and the two objective functions of the fuzzy least-absolutes model and the fuzzy least-squares model. The proposed model is general and can be used for modelling natural phenomena whose available observations are reported as imprecise rather than crisp.

Editor D. Koutsoyiannis; Associate editor H. Aksoy     

Statistical models for shear strength of RC beam‐column joints using machine‐learning techniques

This paper proposes a new set of probabilistic joint shear strength models using the conventional multiple linear regression method, and advanced machine‐learning methods of multivariate adaptive regression splines (MARS) and symbolic regression (SR). In order to achieve high‐fidelity regression models with reduced model errors and bias, this study constructs extensive experimental databases for reinforced and unreinforced concrete joints by collecting existing beam‐column joint subassemblage tests from multiple sources. Various influential parameters that affect joint shear strength such as material properties, design parameters, and joint configuration are investigated through tests of statistical significance. After performing a set of regression analyses, the comparison of simulation results indicates that MARS approach is the best estimation method. Moreover, the accuracy of analytical predictions of the derived MARS model is compared with that of existing joint shear strength relationships. The comparison results show that the proposed model is more accurate compared to existing relationships. This joint shear strength prediction model can be readily implemented into joint response models for evaluation of earthquake performance and inelastic responses of building frames. Copyright © 2014 John Wiley & Sons, Ltd.     

Earthquake magnitude estimation using the τ c and P d method for earthquake early warning systems

Earthquake early warning (EEW) systems are one of the most effective ways to reduce earthquake disaster. Earthquake magnitude estimation is one of the most important and also the most difficult parts of the entire EEW system. In this paper, based on 142 earthquake events and 253 seismic records that were recorded by the KiK-net in Japan, and aftershocks of the large Wenchuan earthquake in Sichuan, we obtained earthquake magnitude estimation relationships using the τ _c and P _d methods. The standard variances of magnitude calculation of these two formulas are ±0.65 and ±0.56, respectively. The P _d value can also be used to estimate the peak ground motion of velocity, then warning information can be released to the public rapidly, according to the estimation results. In order to insure the stability and reliability of magnitude estimation results, we propose a compatibility test according to the natures of these two parameters. The reliability of the early warning information is significantly improved though this test.     

Parametric analysis of single pile response in laterally spreading ground

Extensive damage to pile-supported structures has been witnessed in several recent earthquakes (Chi-Chi, 1999; Kobe, 1995, etc.), as a result of liquefaction-induced lateral spreading of slightly sloping ground or free-face topographic irregularities. This paper presents a parametric analysis of the basic pile and soil parameters, as well as the pile-soil interaction mechanisms affecting the response of single piles subjected to such lateral spreading, based on numerical simulation with the nonlinear P-y method. In parallel, a set of design charts and analytical relations is established, for approximate computation of maximum pile deflections and bending moments, using a “theory guided” multi-variable statistical analysis of the numerical predictions. Three different combinations (design cases) of pile head constraints and soil conditions were considered, which are commonly encountered in practice. The overall accuracy of the proposed analytical relations is evaluated against experimental results from seven centrifuge and five large shaking table experiments.     

Performance of Multivariate Adaptive Regression Splines (MARS) in predicting runoff in mid-Himalayan micro-watersheds with limited data / Performances de régressions par splines multiples et adaptives (MARS) pour la prévision d'écoulement au sein de micro-bassins versants Himalayens d'altitudes intermédiaires avec peu de données

Abstract

Steep topography and land-use transformations in Himalayan watersheds have a major impact on hydrological characteristics and flow regimes, and greatly affect the perenniality and sustainability of water resources in the region. To identify the appropriate conservation measures in a watershed properly, and, in particular, to augment flow during lean periods, accurate estimation of streamflow is essential. Due to the complexity of rainfall—runoff relationships in hilly watersheds and non-availability of reliable data, process-based models have limited applicability. In this study, data-driven models, based upon the Multiple Adaptive Regression Splines (MARS) technique, were employed to predict streamflow (surface runoff, baseflow and total runoff) in three mid-Himalayan micro-watersheds. In addition, the effect of length of historical records on the performance of MARS models was critically evaluated. Though acceptable MARS models could be developed with a 2-year data set, their performance improved considerably with a 3-year data set. Various indicators of model performance, such as correlation coefficient, average deviation, average absolute deviation and modelling efficiency, showed significant improvement for simulation of surface runoff, baseflow and total flow. To further analyse the versatility and general applicability of the MARS approach, 2-year data sets were used to develop the model and test it on a third-year data set to assess its performance. The models simulated the surface runoff, baseflow and total flow reasonably well and can be reliably applied in ungauged small watersheds under identical agro-climatic settings.     

Empirical models for predicting lateral spreading considering the effect of regional seismicity

A revised empirical model has been developed for predicting liquefaction-induced lateral spreading displacement (LD) as a function of both response spectral acceleration derived from strong-motion atte...     

基于GIS的白龙江流域舟曲—武都段的滑坡危险性评价

本研究以围绕着白龙江流域的甘肃省南部的宕昌县、舟曲县和武都区部分地区为研究区,根据全国滑坡编目中得到的272个历史滑坡数据以及选取的高程、坡度、坡向、平面曲率、剖面曲率、归一化植被指数（NDVI）、降雨、岩性、距道路距离和距河流距离10种影响因子,利用三种具有代表性的定量方法：信息量模型、以及基于频率比模型的逻辑回归模型和人工神经网络模型对研究区内滑坡灾害危险性进行评价。三种评价结果均显示研究区内滑坡灾害的极高和高危险区主要沿白龙江河谷地区呈带状分布。从危险性分区图可看出,人工神经网络模型得到的分区图较为合理,既表现出沿河谷地区集中分布的趋势,也呈现出对滑坡历史数据较为独立的特征,这一研究结果与前人研究结果一致。根据受试者工作特征曲线（ROC曲线）对三种模型的精度进行检验,检验得到的AUC值分别为0.818、0.829和0.837,说明三种评价结果均具有较高的可靠性,基于频率比模型的人工神经网络模型相比其他两个模型具有更好的评价精度,能更好地进行滑坡危险性的预测和评价,其中高程、降雨、岩性以及距道路距离对评价结果影响更大,这四种影响因子重要性值占比为52.1%。为该地区的城市扩建与灾害预防预测提供了参考。