城市功能区划的数量化方法研究及其应用--以深圳市南山区物流中心选址为例

文章总结了以往国内学者对城市功能区域的数量化方法研究，认为在层次分析法等方法中对指标权重的确定存在主观因素过多等不足，严重影响了研究的科学性和准确性，文章为了克服上述不足，将多属性效用理论的代理价值法与博弈论的方法应用于城市功能区的布局与研究，并以深圳市南山区物流中心的选址为例，阐述了代理价值法与博弈论在城市功能区域中的具体实践操作。     

自适应顺序采样Kriging方法及其在增量动力分析中的应用

复杂结构的增量动力分析（IDA）对于结构的抗震设计和分析有着重要意义,但需对结构进行大量的非线性时程分析,计算量成本高。本文结合Kriging元模型和自适应顺序采样并用于结构增量动力分析以提高其计算效率和精度,其中:Kriging元模型用于预测结构的地震响应,顺序采样根据候选点的熵值补充非线性时程分析逐步增加Kriging模型的预测精度。借助本文方法,IDA曲线可通过少量的时程分析实现较高的精度。为了校验本文方法的可行性与有效性,对二层和九层钢框架结构模型应用直接IDA、hunt&fill方法和本文方法分析并比较了上述三种方法的计算误差、计算效率和IDA曲线差别。在此基础上本文将自适应顺序采样Kriging方法用于考虑结构不确定性参数的IDA分析,并和传统的蒙特卡洛方法进行比较。结果表明:该方法具有较高的计算效率,可以保证IDA曲线的精度。     

Remote sensing of ecosystem services: An opportunity for spatially explicit assessment

Ecosystem service is an emerging concept that grows to be a hot research area in ecology. Spatially explicit ecosystem service values are important for ecosystem service management. However, it is difficult to quantify ecosystem services. Remote sensing provides images covering Earth surface, which by nature are spatially explicit. Thus, remote sensing can be useful for quantitative assessment of ecosystem services. This paper reviews spatially explicit ecosystem service studies conducted in ecology and remote sensing in order to find out how remote sensing can be used for ecosystem service assessment. Several important areas considered include land cover, biodiversity, and carbon, water and soil related ecosystem services. We found that remote sensing can be used for ecosystem service assessment in three different ways: direct monitoring, indirect monitoring, and combined use with ecosystem models. Some plant and water related ecosystem services can be directly monitored by remote sensing. Most commonly, remote sensing can provide surrogate information on plant and soil characteristics in an ecosystem. For ecosystem process related ecosystem services, remote sensing can help measure spatially explicit parameters. We conclude that acquiring good in-situ measurements and selecting appropriate remote sensor data in terms of resolution are critical for accurate assessment of ecosystem services.     

Seismic fragility of reinforced concrete girder bridges using Bayesian belief network

Infrastructure owners and operators, or governmental agencies, need rapid screening tools to prioritize detailed risk assessment and retrofit resources allocation. This paper provides one such tool, for use by highway administrations, based on Bayesian belief network (BBN) and aimed at replacing so‐called generic or typological seismic fragility functions for reinforced concrete girder bridges. Resources for detailed assessments should be allocated to bridges with highest consequence of damage, for which site hazard, bridge fragility, and traffic data are needed. The proposed BBN based model is used to quantify seismic fragility of bridges based on data that can be obtained by visual inspection and engineering drawings. Results show that the predicted fragilities are of sufficient accuracy for establishing relative ranking and prioritizing. While the actual data and seismic hazard employed to train the network (establishing conditional probability tables) refer to the Italian bridge stock, the network structure and engineering judgment can easily be adopted for bridges in different geographical locations. Copyright © 2015 John Wiley & Sons, Ltd.     

Modification of stochastic ground motion models for matching target intensity measures

Stochastic ground motion models produce synthetic time‐histories by modulating a white noise sequence through functions that address spectral and temporal properties of the excitation. The resultant ground motions can be then used in simulation‐based seismic risk assessment applications. This is established by relating the parameters of the aforementioned functions to earthquake and site characteristics through predictive relationships. An important concern related to the use of these models is the fact that through current approaches in selecting these predictive relationships, compatibility to the seismic hazard is not guaranteed. This work offers a computationally efficient framework for the modification of stochastic ground motion models to match target intensity measures (IMs) for a specific site and structure of interest. This is set as an optimization problem with a dual objective. The first objective minimizes the discrepancy between the target IMs and the predictions established through the stochastic ground motion model for a chosen earthquake scenario. The second objective constraints the deviation from the model characteristics suggested by existing predictive relationships, guaranteeing that the resultant ground motions not only match the target IMs but are also compatible with regional trends. A framework leveraging kriging surrogate modeling is formulated for performing the resultant multi‐objective optimization, and different computational aspects related to this optimization are discussed in detail. The illustrative implementation shows that the proposed framework can provide ground motions with high compatibility to target IMs with small only deviation from existing predictive relationships and discusses approaches for selecting a final compromise between these two competing objectives.     

表面活性剂强化含水层修复过程的全局灵敏度

针对非水相流体污染含水层的表面活性剂强化修复过程,在多相流数值模拟模型以及径向基函数人工神经网络替代模型的基础上,应用Sobol法对影响修复效果的变量进行全局灵敏度分析。当替代模型的训练集包含12组和22组数据时,替代模型与模拟模型拟合的确定性系数分别为0.977 8和0.981 6,表明随着训练集数据的增多,替代模型与模拟模型的近似精度逐渐增加。灵敏度分析结果表明：对修复效果贡献最大的决策变量为总抽水量(总灵敏度为0.491 2),其次为修复时间(总灵敏度为0.468 5),表面活性剂浓度对修复效果的贡献最小(总灵敏度为0.124 2);各个变量之间存在着相互作用,但相互作用对输出响应的影响不大。     

Evaluating conservation effectiveness of nature reserves established for surrogate species: Case of a giant panda nature reserve in Qinling Mountains, China

Many nature reserves are established to protect the habitat needs of particular endangered species of interest but their effec-tiveness for protecting other species is questionable.In this study,this effectiveness was evaluated in a nature reserve network located in the Qinling Mountains,Shaanxi Province,China.The network of reserves was established mainly for the conservation of the giant panda,a species considered as a surrogate for the conservation of many other endangered species in the region.The habitat suitability of nine protected species,including the giant panda,was modeled by using Maximum Entropy（MAXENT） and their spatial congruence was analyzed.Habitat suitability of these species was also overlapped with nature reserve boundaries and their management zones（i.e.,core,buffer and experimental zones）.Results show that in general the habitat of the giant panda constitutes a reasonable surrogate of the habitat of other protected species,and giant panda reserves protect a relatively high proportion of the habitat of other protected species.Therefore,giant panda habitat conservation also allows the conservation of the habitat of other protected species in the region.However,a large area of suitable habitat was excluded from the nature reserve network.In addition,four species exhibited a low proportion of highly suitable habitat inside the core zones of nature reserves.It suggests that a high proportion of suitable habitat of protected species not targeted for conservation is located in the experimental and buffer zones,thus,is being affected by human activities.To increase their conservation effectiveness,nature reserves and their management zones need to be re-examined in order to include suitable habitat of more endangered species.The procedures described in this study can be easily implemented for the conservation of many endangered species not only in China but in many other parts of the world.     

Multi‐objective risk‐informed design of floor isolation systems

The design of floor isolation systems (FISs) for the protection of acceleration sensitive contents is examined considering multiple objectives, all quantified in terms of the probabilistic system performance. The competing objectives considered correspond to (i) maximization of the level of protection offered to the sensitive content (acceleration reduction) and (ii) minimization of the demand for the isolator displacement capacity and, more importantly, for the appropriate clearance to avoid collisions with surrounding objects (floor displacement reduction). Both of these objectives are probabilistically characterized utilizing a versatile, simulation‐based framework for quantifying seismic risk, addressing all important uncertainties related to the seismic hazard and the structural model. FIS performance is assessed through time‐history analysis, allowing for all important sources of nonlinearity to be directly addressed in the design framework. The seismic hazard is described through a stochastic ground motion model. For efficiently performing the multi‐objective optimization, an augmented surrogate modeling methodology is established, considering development of a single metamodel with respect to both the uncertain model parameters and the design variables for the FIS system. This surrogate model is then utilized to simultaneously support the probabilistic risk assessment and the design optimization to provide the Pareto front of dominant designs. Each of these designs establishes a different compromise between the considered risk‐related objectives offering a variety of potential options to the designer. Within the illustrative example, the efficiency of the established framework is exploited to compare three different FIS implementations, whereas the impact of structural uncertainties on the optimal design is also evaluated. Copyright © 2016 John Wiley & Sons, Ltd.     

Uncertainty analysis of flood inundation modelling using GLUE with surrogate models in stochastic sampling

A generalized likelihood uncertainty estimation (GLUE) method incorporating moving least squares (MLS) with entropy for stochastic sampling (denoted as GLUE‐MLS‐E) was proposed for uncertainty analysis of flood inundation modelling. The MLS with entropy (MLS‐E) was established according to the pairs of parameters/likelihoods generated from a limited number of direct model executions. It was then applied to approximate the model evaluation to facilitate the target sample acceptance of GLUE during the Monte‐Carlo‐based stochastic simulation process. The results from a case study showed that the proposed GLUE‐MLS‐E method had a comparable performance as GLUE in terms of posterior parameter estimation and predicted confidence intervals; however, it could significantly reduce the computational cost. A comparison to other surrogate models, including MLS, quadratic response surface and artificial neural networks (ANN), revealed that the MLS‐E outperformed others in light of both the predicted confidence interval and the most likely value of water depths. ANN was shown to be a viable alternative, which performed slightly poorer than MLS‐E. The proposed surrogate method in stochastic sampling is of practical significance in computationally expensive problems like flood risk analysis, real‐time forecasting, and simulation‐based engineering design, and has a general applicability in many other numerical simulation fields that requires extensive efforts in uncertainty assessment. Copyright © 2014 John Wiley & Sons, Ltd.     

基于主动学习径向基函数的边坡系统可靠度分析

相较于极限平衡法,强度折减法在计算边坡稳定性系数上有许多优势,但更大的计算量在一定程度上限制了其在边坡可靠度分析中的应用。为了有效地减少可靠度分析中数值模型的计算次数,以减轻使用强度折减法所带来的计算压力,引入了基于主动学习径向基函数（ARBF）代理模型的高效分析方法：利用主动学习函数在极限状态面附近搜索训练样本更新代理模型,加快模型训练的收敛速度;采用线性核径向基插值函数简化模型参数优化过程,建立简洁、稳定的代理模型。此外,为了充分发挥主动学习代理模型的优势,提出针对土质边坡特性的初始采样策略。当得到稳定的代理模型后,结合蒙特卡罗模拟计算边坡的系统失稳概率。作为对比,基于两个典型边坡算例,测试了两种经典的可靠度方法：主动学习克里金模型（AK）和二次响应面法（QRSM）,论证了引入的主动学习径向基函数代理模型在计算效率上的高效性和计算模型上的稳定性。