A Practical Indirect Back Analysis Approach for Geomechanical Parameters Identification

Back analysis is a powerful tool to determine geomechanical parameters. In this article, a practical approach that employs the response surface method and MS Excel solver for back analysis is presented. The least square support vector machine (LSSVM)-based response surface is utilized to represent the nonlinear relationship between the geomechanical parameters and the monitored information and Excel solver is used to search the geomechanical parameters based on the monitoring information. We analyze the robustness and search ability of the proposed approach. On the other hand, two numerical cases on a circular tunnel and a slope are used to verify the proposed approach. Results clearly indicate that LSSVM-based response surface is effectively able to be used to identify the geomechanical parameters from the monitoring displacements.     

Intelligent fuzzy weighted input estimation method for the multi-story buildings with unknown ground motion acceleration

This paper presents a novel intelligent fuzzy weighted input estimation method which effiviently and robustly estimates the unknown ground motion accelerations. The new input estimation method includes the Kalman Filter (KF) and the recursive least square estimator (RLSE), which is weighted by the fuzzy weighting factor proposed based on the fuzzy logic inference system. By directly synthesizing the Kalman filter with the estimator, this work presents an efficient robust forgetting zone, which is capable of providing a reasonable compromise between the tracking capability and the flexibility against noises. The excellent performace of this inverse method is demonstrated by solving the earthquake-excitation estimation problem, and the proposed algorithm is compared by alternating between the constant and adaptive weighting factors. The results reveal that this method has the properties of better target tracking capability and more effective noise reduction.     

汶川地震地表破裂周围建筑物重建的避让距离

2008年5月12日汶川M_s8.0级地震地表破裂迹线周围建筑物倒塌率非常之高,导致重大的人员伤亡,举世震惊.为了避免历史悲剧重演,沿地表破裂迹线周围建筑物重建时,必须确定一个合适的避让距离.本文依据现场科考地表破裂带宽度、建筑物完全倒塌宽度、倒塌率与断层距之间的关系,联合未来一段时间范围内断层地震危险性分析、地表破裂迹线永久位移分布模式,建立完全倒塌与断层距之间的关系.提出了几种情况下的汶川M_s8.0级地震地表迹线处建筑物重建应考虑的避让距离.     

进化-最小二乘支持向量机的边坡稳定性估计

针对最小二乘支持向量机的参数选择问题,用遗传算法来搜索最小二乘支持向量机的相关参数,避免了人工搜索参数的盲目性,提高了模型的推广性能。根据大量的实际边坡工程数据,建立了基于进化-最小二乘支持向量机的边坡稳定性模型,并将其应用于估计丁家河磷矿自然边坡稳定状况。计算结果与工程实际情况一致,表明了该方法的有效性和合理性。     

基于B样条函数的散乱数据曲面拟合和数据压缩

针对大规模散乱数据,本文提出一种基于B样条函数的曲面拟合和数据压缩方法.在此方法中,构建最小平面矩形区域覆盖所有的散乱数据点,根据二维情况下的B样条函数的线性插值公式,利用最小二乘法拟合规则网格控制点的数值,存储有限规则网格控制点的数值,并通过插值可以得到任意点的数值,从而达到曲面拟合和数据压缩的目的.通过算例验证了此方法的可行性以及使用三次B样条函数曲面拟合的优越性.由于此方法具有局部误差小、计算高效的特征,可以适用于数据梯度较小的二维系统数据的曲面拟合和数据压缩,如地形高程数据和射线追踪走时表.     

建立基于土地利用的交通发生量模型的研究

针对出行生成预测中变量众多以及多重相关性问题,以常州调查数据为样本,分别采用逐步回归分析方法和偏最小二乘回归（PLSR）方法,建立基于土地利用形态的交通发生量模型。并对两者建模方法进行分析比较．结果表明PLSR能有效地解决变量多重相关性问题,模型意义清晰,效果显著。     

PSO-LSSVM模型在位移反分析中的应用

提出了一种基于均匀设计原理、最小二乘支持向量机(LSSVM)和粒子群优化算法(PSO)的快速位移反分析方法。该方法利用均匀设计和有限差分法获得学习样本,再用粒子群算法搜索最优的最小二乘支持向量机模型参数。并用最小二乘支持向量机回归模型建立反演参数与监测点位移值之间的非线性映射关系,最后用粒子群算法从全局空间上搜索与实测位移最吻合的反演参数。该反演模型利用了粒子群算法高效简单、均匀设计构造高质量小样本以及最小二乘支持向量机的小样本、泛化性能好的特点。将该模型应用于龙滩水电站左岸地下厂房区岩体地应力场的反演分析中,计算结果与实测的位移值和地应力值均吻合较好,说明了该模型在岩土工程快速反演分析中具有良好的应用价值。     

杂交边界点方法求解动态断裂力学问题

结合杂交边界点法和双互易法则,推导出求解动力问题的纯边界类型无网格方法--双互易杂交边界点方法,并将该方法用于求解含中心裂纹的方板受瞬态载荷作用的问题。该方法将问题的解分为通解和特解两部分,通解使用杂交边界点法求解,特解则利用局部径向基函数近似,域内布点仅仅为了径向基插值,因此仍然是一种纯边界类型的无网格方法。同时,将移动最小二乘近似中的基函数扩充,使该方法能更好地模拟裂纹尖端应力场的奇异性,具有后处理简单、精度高的优点。数值算例表明了该方法的稳定性和有效性。     

基于大气污染控制的中国煤炭消费总量控制方案初步研究

控制煤炭消费是应对大气污染问题的关键措施,但中国煤炭消费总量控制仍未分解与落实到地方层面。研究基于大气污染的约束分析,结合情景分析与数据回归,通过建立大气污染物与煤炭消费之间的量化关系,分析了2020、2030年全国及各省/市煤炭消费总量控制目标,初步设计了中国煤炭消费总量控制方案,提出2020和2030年全国煤炭消费量控制目标分别为40亿t和45亿t,各省则均按预测结果调整执行,汇总值分别为44亿t和50亿t,鉴于两者之间的差距在于国家统计口径考虑了重复因素的剔除,故采纳基于国家统计数据的分析结果。建议构建绿色经济发展用煤指标,适当提高电力耗煤的比例,为促进中国中长期煤炭资源的合理消费提供科技支撑。     

Multivariate approach for chlorophyll-a and suspended matter retrievals in Case II type waters using hyperspectral data

Abstract

Remote sensing has become promising in providing temporal and spatial information on biogeodynamics in large and open freshwater bodies. In optically complex environments, such as in the Western Basin of Lake Erie (WBLE), the water contains multiple biogeochemical constituents or colour producing agents (CPAs), such as phytoplankton, suspended matter and dissolved organic carbon; identifying and analysing such in-water constituents is crucial for understanding and assessing many biogeochemical processes. For example, concentrations of chlorophyll-a and total suspended matter can be used as proxies to assess phytoplankton dynamics and particulate loading. However, quantitative estimation of their concentrations from satellite observations is complicated when working with mixed spectral signatures. Hyperspectral remote sensing is fast emerging as a key technology for advanced and improved understanding of optically complex waters. This study estimates concentrations of chlorophyll-a and total suspended matter (TSM) in the WBLE by applying the partial least squares (PLS) method to a full range (400–900 nm) of continuous narrow spectral bands. The PLS method models the covariance between hyperspectral bands and CPAs, and identifies the optimal bands that characterize most of the variance in the CPAs. This method avoids the curse of dimensionality and the effects of multi-collinearity, a challenge that is associated with new-generation hyperspectral satellite sensors. Validation parameters for the PLS-based models produced R² of 0.84 for chlorophyll-a (RMSE = 1.18 μg/L), and R² of 0.90 for TSM (RMSE = 1.26 mg/L), illustrating the potential of the PLS method for isolating and extracting absorption features characterizing the various CPAs in optically complex Case II type waters.

Editor Z.W. Kundzewicz Associate editor Not assigned