A new method for indentifying the model error of adjustment system

Some theory problems affecting parameter estimation are discussed in this paper. Influence and transformation between errors of stochastic and functional models is pointed out as well. For choosing the best adjustment model, a formula, which is different from the literatures existing methods, for estimating and identifying the model error, is proposed. On the basis of the proposed formula, an effective approach of selecting the best model of adjustment system is given. Project supported by the Open Research Fund Program of the Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University (No. 905276031-04-10).     

Structural damage detection using the optimal weights of the approximating artificial neural networks

This work presents a novel neural network‐based approach to detect structural damage. The proposed approach comprises two steps. The first step, system identification, involves using neural system identification networks (NSINs) to identify the undamaged and damaged states of a structural system. The partial derivatives of the outputs with respect to the inputs of the NSIN, which identifies the system in a certain undamaged or damaged state, have a negligible variation with different system errors. This loosely defined unique property enables these partial derivatives to quantitatively indicate system damage from the model parameters. The second step, structural damage detection, involves using the neural damage detection network (NDDN) to detect the location and extent of the structural damage. The input to the NDDN is taken as the aforementioned partial derivatives of NSIN, and the output of the NDDN identifies the damage level for each member in the structure. Moreover, SDOF and MDOF examples are presented to demonstrate the feasibility of using the proposed method for damage detection of linear structures. Copyright © 2001 John Wiley & Sons, Ltd.     

Parameter identification of inelastic structures under dynamic loads

The generalized model of differential hysteresis contains 13 control parameters with which it can curve‐fit practically any hysteretic trace. Three identification algorithms are developed to estimate the control parameters of hysteresis for different classes of inelastic structures. These algorithms are based upon the simplex, extended Kalman filter, and generalized reduced gradient methods. Novel techniques such as global search and internal constraints are incorporated to facilitate convergence and stability. Effectiveness of the devised algorithms is demonstrated through simulations of two inelastic systems with both pinching and degradation characteristics in their hysteretic traces. Owing to very modest computing requirements, these identification algorithms may become acceptable as a design tool for mapping the hysteretic traces of inelastic structures. Copyright © 2002 John Wiley & Sons, Ltd.     

应用CP网络进行岩性识别

为通过测井解决岩性识别问题，引入了具有分类准确、算法简练等优点的CP（Counter-Propagation)网络。在详细介绍CP网络的网络模型和算法的基础上，结合某油田的实际测井资料，进行了CP网络识别研究。应用结果表明：CP网络训练周期短、识别准确率高、不存在收敛问题。通过试验研究得出结论：CP网络完全可以用于解决岩性识别等问题，具有广阔的应用前景。     

沉积物物源定量识别的非线性规划模型——以东海陆架北部表层沉积物物源识别为例

依据“质量守衡”原理和沉积物物源难以穷尽的认识 ,提出了沉积物物源定量识别的非线性规划数学模型。利用该模型实现了东海陆架北部表层沉积物物源的定量计算 ,阐明了长江、黄河入海物质的大致分布特征。长江沉积物主要分布于 32°N以南、台湾暖流以西的海域 ,黄河沉积物主要分布于 32°N以北、台湾暖流以东的海域。长江、黄河两类沉积物的这种分布格局与本海域的流系分布相吻合。     

桩土接触面单元参数分析

桩的抗拔特性与接触面的特性密切相关，正确选定接触面的力学参数对于分析桩的抗拔机理有重要意义。根据桩土接触面点面接触模型和库仑摩擦变形原理，在10组土－结构（钢、混凝土）剪切摩擦试验分析的基础上，对桩土接触面摩擦参数的取值问题进行了研究，得到了一些结论，对于桩土接触面力学参数正确取值有一定的参考价值。     

有限弹性变形地壳中的地震波

地壳由半无限大的基岩上一层厚度为H^-的表土层组成，入射地震波为垂直的SH波，产生水平地面运动。当浅源大地震发生时，在极震区以外行波传播产生地面运动将使地壳介质有非线性的有限弹性变形。用小参数摄动法使非线性控制方程为线性化的小参数各阶控制方程，得出头两阶线性控制方程的解析解。     

海洋平台结构振动的AMD主动控制参数优化分析

本文针对海洋平台结构的冰激振动和地震反应控制问题，提出了采用AMD主动控制的控制策略，结合JZ20－2MUQ平台结构进行了AMD控制系统的硬参数和软参数的优化分析，并就相应于最优参数下的AMD控制海洋平台结构冰激振动和地震反应的几种代表性工况进行了时程分析，得到了一些定性和定量的结论，为实际工程的控制设计提供了基础。本文提出的AMD主动控制方法对类似的海洋平台结构的控制问题也有参考价值。     

2001年11月14日新疆-青海交界8.1级地震

简要介绍了2001年11月14日新疆－青海交界8．1级地震的预测情况，给出了中、美、日等对此次地震的震源参数测定和震源机制，以及余震的分布和加卸载响应比随时间的变化。进而对震区近期地震趋势进行预测。     

Parameter identification for lined tunnels in a viscoplastic medium

This paper is dedicated to the identification of constitutive parameters of elasto‐viscoplastic constitutive law from measurements performed on deep underground cavities (typically tunnels). This inverse problem is solved by the minimization of a cost functional of least‐squares type. The exact gradient is computed by the direct differentiation method and the descent is done using the Levenberg–Marquardt algorithm. The method is presented for lined or unlined structures and is applied for an elastoviscoplastic constitutive law of the Perzyna class. Several identification problems are presented in one and two dimensions for different tunnel geometries. The used measurements have been obtained by a preliminary numerical simulation and perturbed with a white noise. The identified responses match the measurements. We also discuss the usage of the sensitivity analysis of the system, provided by the direct differentiation method, for the optimization of in situ monitoring. The sensitivity distribution in space and time assess the location of the measurements points as well as the time of observation needed for reliable identification. Copyright © 2002 John Wiley & Sons, Ltd.