Obtaining Accurate Isotopic Compositions with the Double Spike Technique: Practical Considerations

Ever‐increasing precision in isotope ratio measurements requires a concomitant small bias within and between laboratories. The double spike technique is the most suitable method to obtain reliable isotope composition data that are accurately corrected for instrumental mass fractionation. Compared with other methods, such as sample‐calibrator bracketing (SCB), only the double spike technique can correct for all sources of fractionation after equilibration of the sample with the double spike, such as that incurred during chemical separation and measurement. In addition, it is not dependent on a priori assumptions of perfect matrix matching of samples to reference materials or quantitative recovery of the sample through the chemical separation procedure to yield accurate results. In this review article, we present a detailed discussion of the merits of the double spike technique, how to design and calibrate a suitable double spike and analytical strategies. Our objective is to offer a step‐by‐step introduction to the use of the double spike technique in order to lower potential barriers that researchers new to the subject might face, such that double spiking will replace SCB as the measurement method of choice.     

Strategy for consistent model parameter calibration for soft soils using multi-objective optimisation

Constitutive models for soft soils require a large number of parameters to model the complex material response. One set of parameters should capture the soil response for various laboratory & in situ stress paths. This requires a new method to calibrate a consistent set of model parameters using test data from different load paths of classic geomechanical tests. The feasibility of the proposed method is demonstrated with the recently developed CREEP-SCLAY1S model. After a sensitivity analysis that highlights the model parameters for optimisation, an optimisation process for two different configurations is designed. The latter is successfully verified against artificially generated laboratory data.     

On the seismic behaviour of monolithic lunar arches subjected to moonquakes

With a new era emerging in the field of lunar exploration and habitation, there is a need for research on structural forms made of local soil material (regolith), which will be able to endure the extreme conditions in harsh environments (e.g., extreme temperature fluctuations, solar and cosmic radiation, meteor showers, strong ground motions, etc.). The present work focuses on understanding the dynamic and seismic behaviour of certain structural typologies of monolithic arches by means of finite element analysis (FEA). These typologies were extensively investigated previously, using static analyses accounting for the reduced gravitational field on the moon, and proved to be of the optimum shape against certain loading scenarios. Specifically, these optimal monolithic arch forms (named enhanced varying-thickness arches – EVTAs) examined herewith, are described by varying-thickness geometry, properly enhanced at certain weak points for increasing their structural stability. Aiming at a fair comparison, the seismic behaviour of EVTAs is contrasted to that of their corresponding monolithic constant-thickness (CTAs) counterparts (having the same amount of structural material). After defining an appropriate damage state, the authors conduct preliminary pushover analyses to determine the structural capacity of the arches against lateral loading. Subsequently, the modal analysis of the EVTAs shows that the second/vertical mode exhibits a natural period almost equal to that of their first/translational mode and substantially longer than the corresponding second/vertical mode of their CTA counterparts, indicating a potential vulnerability along the vertical excitation. Furthermore, taking into account that shallow moonquakes are comparable to intraplate earthquakes in terms of hazard potential, the authors produce sets of stochastic seismic excitations used as time histories for seismic analyses. The probability of exceedance of the defined damage state as a function of the peak ground acceleration (PGA) is presented through indicative fragility curves, where the structural superiority of EVTAs against their CTA counterparts is demonstrated.     

模拟退火算法用于产生城市土地空间布局方案

本文发展了一种模拟退火算法辅助产生城市土地空间布局方案。首先建立了城市土地空间配置问题的数学模型 ,目标函数是最小化土地开发费用、最大化不同功能地块间的空间协调性。由于问题存在的诸多空间约束条件使得可行的土地利用布局方案的搜寻过程变得十分缓慢 ,采用了将一些空间约束条件结合到目标函数中的方法 ,如结合了距离约束、方向约束、相同土地利用单元的邻近约束和地块形状的紧凑约束等约束条件。应用于湖南省长沙市暮云工业区用地规划的结果表明 ,模拟退火算法是一种辅助城市土地利用规划的有用的、有潜力的优化方法     

Designing efficient and balanced police patrol districts on an urban street network

In police planning, a territory is often divided into several patrol districts with balanced workloads, in order to repress crime and provide better police service. Conventionally, in this districting problem, there is insufficient consideration of the impacts of street networks. In this study, we propose a street-network police districting problem (SNPDP) that explicitly uses streets as basic underlying units. This model defines the workload as a combination of different attributes and seeks an efficient and balanced design of districts. We also develop an efficient heuristic to generate high-quality districting plans in an acceptable time. The capability of the algorithm is demonstrated in comparison to an exact linear programming solver on simulated datasets. The SNPDP model is successfully implemented and tested in a case study in London, and the generated police districts have different characteristics that are consistent with the crime risk and land use distribution. Besides, we demonstrate that SNPDP is superior to an aggregation grid-based model regarding the solution quality. This model has the potential to generate street-based districts with balanced workloads for other districting problems, such as school districting and health care districting.     

On the interest of using field primary production data to calibrate phytoplankton rate processes in ecosystem models

In many ecosystem models based on empirical formulations, parameters generally are calibrated in order to achieve the best fit between measured and simulated chlorophyll a standing stocks. An accurate calibration of rate processes as primary production rarely is taken into account. In this paper, we test the usefulness of calibration of phytoplankton photosynthetic processes in an ecosystem model using field primary production data. We used 18 months of photosynthetic process data from the Baie des Veys ecosystem (Normandy, France). Five empirical formulations of photosynthesis–irradiance curve models amongst the most widely used were tested. In each formulation, the variability of photosynthetic parameters (i.e. the light-saturated rate of photosynthesis (P_max^B) and the initial slope of the photosynthesis–light curve (α^B)) was considered depending on environmental factors (temperature and nutrient availability). The fit of the five equations as well as the calibration of parameters on field measurements (i.e. the light-saturated rate of photosynthesis (P_ref^B), the initial slope of the photosynthesis–light curve (α_ref^B), the half-saturation constant for nitrogen (K_N) and silicates uptake (K_Si), and the coefficient in the exponential thermal effect (K_T)) was performed using the whole available data set of P vs. E curves (n = 143, P vs. E curves). Then, the Smith formulation allowing the best simulation of the Baie des Veys primary production and corresponding parameters were introduced in an ecosystem box model. This formulation led directly to a satisfactory representation of the Baie des Veys phytoplankton dynamics without additional calibration. Results obtained were compared with a more classical approach in which ecosystem models were calibrated using published values of parameters. This comparison showed that for the two years studied, annual primary production estimated through the ecosystem model was 13% and 26% higher with our approach than with the more classical approach. This work emphasizes the importance of accurately representing rate processes in ecosystem models in order to adequately simulate production as well as standing stocks.     

Bearing capacity of foundations on slopes

By applying the lower bound finite element limit analysis in conjunction with non-linear optimisation, the bearing capacity factors, N_c, N_q and N_γ, due to the components of cohesion, surcharge and unit weight, respectively, have been estimated for a horizontal strip footing placed along a sloping ground surface. The variation of N_c, N_q and N_γ with changes in slope angle (β) for different soil friction angle (φ) have been computed for smooth as well as rough strip footings. The analysis reveals that along a sloping ground surface, in addition to N_γ, the factors N_c and N_q also vary considerably with changes in footing roughness. Compared to the smooth footing, the extent of the plastic zone around the footing becomes greater for the rough footing. The results obtained from the analysis are found to compare well with those previously reported in literature.     

An intelligent method to discover transition rules for cellular automata using bee colony optimisation

This paper presents a new, intelligent approach to discover transition rules for geographical cellular automata (CA) based on bee colony optimisation (BCO–CA) that can perform complex tasks through the cooperation and interaction of bees. The artificial bee colony miner algorithm is used to discover transition rules. In BCO–CA, a food source position is defined by its upper and lower thresholds for each attribute, and each bee searches the best upper and lower thresholds in each attribute as a zone. A transition rule is organised when the zone in each attribute is connected to another node by the operator ‘And’ and is linked to a cell status value. The transition rules are expressed by the logical structure statement ‘IF-Then’, which is explicit and easy to understand. Bee colony optimisation could better avoid the tendency to be vulnerable to local optimisation through local and global searching in the iterative process, and it does not require the discretisation of attribute values. Finally, The BCO–CA model is employed to simulate urban development in the Xi’an-Xian Yang urban area in China. Preliminary results suggest that this BCO approach is effective in capturing complex relationships between spatial variables and urban dynamics. Experimental results indicate that the BCO–CA model achieves a higher accuracy than the NULL and ACO–CA models, which demonstrates the feasibility and availability of the model in the simulation of complex urban dynamic change.     

Analysis of a large inter-basin water transfer system in India / Analyse d’un grand système de transfert d’eau inter-bassins en Inde

Abstract

Abstract Inter-basin transfer of water in India is a long-term option to correct the spatial and temporal mismatch of water availability and demand, largely owing to the monsoon climate. This paper is concerned with analysis and preliminary design of a large inter-basin water transfer system in peninsular India. The system covers four major basins and involves operation of 13 major structures. The study was carried out in three stages. First, the surface water deficit in each basin was estimated. Then the net deficit was worked out by considering the availability of groundwater. Finally, the link systems were planned to transfer the amount of water needed to meet the demands with desired reliability.     

Optimisation of deep mixing technique by artificial neural network based on laboratory and field experiments

ABSTRACT

Ground improvement techniques are inevitable for weak soils that cannot endure the design load imposed by superstructures. Deep mixing technique (DMT) as one of these methods is promising and effective when a deep soil layer with low bearing capacity is encountered. Such deposits are quite common in the South-west of Iran where the studied site is located. In order to validate the influence of DMT on the enhancement of strength, both in-situ and laboratory tests were conducted. Afterwards, a parametric study was carried out to investigate the influence of key factors including cement content, water–cement ratio, curing time and plasticity index (PI) on the performance of DMT. In summary, a total of 192 different conditions were examined in this study by using two methods of 3D plotting and artificial neural networks (ANNs) as the optimisation tool. Results proved the importance of water–cement ratio as a key parameter in DMT. Based on the trained networks, ANN was revealed to give satisfactory predictions on the strength of an improved soil with different admixture conditions. More important, the optimisation made by ANN could determine the specific values for selected key admixture factors to reach a desired strength level with the coefficient of determination higher than 0.85.