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.     

可持续发展评估指标、方法及应用研究

可持续发展评估指标及方法研究是可持续发展定量评估研究的基础,是实施可持续发展管理的依据,是生态经济学与可持续发展研究的前沿领域和热点问题之一. 在概括介绍可持续发展评估的工具--指标/指数的概念和功能、国际上代表性可持续发展研究机构指标选取的原则的基础上,全面分析归纳了可持续发展指标(体系)的类型及其框架模式. 详细介绍了联合国可持续发展委员会(UNCSD)、经济合作与发展组织(OECD)、世界保护同盟(IUCN)、世界银行等国际上代表性机构的可持续发展系统性指标体系的最新研究进展,并分析了这些系统性指标体系的优缺点. 深入分析了国际上典型的社会发展类、经济发展类、生态环境类可持续发展指标(指数)的研究、开发与实际评估应用的情况. 最后,总结了当前可持续发展指标(体系)研究的特点与趋势.     

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。     

Drought in Bulgaria and atmospheric synoptic conditions over Europe

Drought in Bulgaria is analyzed from the multiple viewpoints of statistical occurrence, spatial patterns, and synoptic conditions. A new index of drought, the SD (spatial-dryness) index, characterizes drought by both intensity and spatial extent. The occurrence of the SD index is analyzed using global gridded data sets. Examination of transitional probabilities of multiple months and years with drought occurrence suggests persistence is sufficiently frequent to be important for climate-related environmental planning. Finally, it is shown that specific seasonal synoptic patterns are associated with wet and dry conditions in Bulgaria. This revised version was published online in July 2006 with corrections to the Cover Date.     

Hydration and refractive indices of Holocene tephra glass in Hokkaido, Northern Japan

Refractive indices of incompletely hydrated tephra glasses vary widely. Thus, glass refractive index is not a practical indicator for identifying Holocene tephras. Hydration near the surface of tephra glass shards can be removed by either hydrofluoric acid treatment or annealing for 12 h at 400°C. The annealing procedure is a particularly reliable and simple way to dehydrate tephra glasses. Standard deviations (s) of refractive indices for glasses after 12 h annealing are small (s=0.0014–0.0018), in contrast with untreated glasses (s=0.0034–0.0405). The refractive indices of dehydrated tephra glasses are 0.006–0.014 lower than those of the untreated (hydrated) glasses. Using the 400°C 12-h annealing procedure, values for the refractive indices of eight Holocene tephras in Hokkaido were obtained. These refractive indices are useful indicators for identification and correlation of Holocene tephras.     

城市震害高危害小区的研究和GIS的实现技术

论述了在城市地震灾害中如何考虑诸方面因素来评价城市的灾害轻重分布情况。中建立坑危害小区分析模型，设想将城市划分等面积网格，给每个单元网格赋予灾害指数，以确定城市中受灾较为严重的区域，为求灾指挥提供决策信息。同时，将高危害小区的模型研究结果转化为计算机技术可以实现的GIS模型。该研究结果已应用于泰安市防震减灾示范研究项目中。     

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.     

灰色系统理论预测大气氮氧化物污染的应用

根据中国南方某省所辖城市近年来N0x污染指数的测量数据的统计资料，建立了GM(1，1）灰色预测模型，通过预测探讨了大气中氮氧化物指数的变化趋势。所建模型残差仅1．17％，关联系数为0．9067，精度较高，具有较好的可行性和适应性，可以为管理规则提供决策依据。