Tracing sediment loss from eroding farm tracks using a geochemical fingerprinting procedure combining local and genetic algorithm optimisation

Eroding farm tracks represent important spatially distributed features in many agricultural landscapes and there is concern over their role in catchment sediment problems. It is, however, important to place eroding farm tracks in the context of catchment sediment sources more generally, especially since the former afford potential for targeted sediment mitigation. A sediment source tracing procedure was therefore used to assess the importance of eroding farm track surfaces as a contemporary primary suspended sediment source relative to inputs from pasture or cultivated topsoils and channel banks/subsurface sources, in the upper River Piddle catchment (~ 100 km²), in southern England. The study provided a timely opportunity to assess the performance of both local and global (genetic algorithm; GA) optimisation techniques in the sediment geochemistry mass balance modelling used to apportion sources. Over the duration of the study, average median source contributions for individual time-integrated suspended sediment samples collected from three sub-catchments ranged between 1 ± 1 and 19 ± 3% for farm track surfaces, 31 ± 3 and 55 ± 2% for pasture topsoils, 1 ± 1 and 19 ± 1% for cultivated topsoils and 23 ± 2 and 49 ± 1% for channel banks/subsurface sources. Comparison of the local and GA optimisation techniques demonstrated that GA with random initial values improved the minimisation of the objective functions compared to local searching by 0.01-0.04% of 5000 repeat Monte Carlo iterations. GA informed by the outputs of the local optimisation as initial values improved corresponding performance by 0.05-0.20%. These findings increased confidence in the outputs from the local optimisation mass balance modelling, but fingerprint property datasets should be treated on an individual basis. Future sediment source tracing studies should always endeavour to combine local and global search tools to avoid the risk of using localised solutions for source apportionment estimates.     

Topology optimization method of lattice structures based on a genetic algorithm

A two-stage topology optimization method of lattice structures based on a genetic algorithm is proposed. The first stage is the form-finding analysis of lattice structures, and the optimal initial shape was achieved with the numerical inverse hanging method. The second stage is the topology optimization of single-layer lattice structures, which can be realized by changing the mesh size and the tube configurations to minimize the total weight of steel tubes subject to the design requirements. The mesh configuration optimization is realized through the adjustment of the nodal horizontal co-ordinates and the removal of tubes with lower stress. The maximum displacement of the structure, the maximum stress of the circular steel tubes, and the nonlinear buckling load are the state variables, and a genetic algorithm (GA) is the optimization algorithm. Different stress-limiting values used to delete the tubes were discussed. The numerical examples show that the two-stage topology optimization method for lattice structures proposed in this paper is correct and efficient. Furthermore, the forms of the optimized structure are rich, and the structure is lightweight and efficient.     

Thermodynamic analysis and optimisation of an irreversible radiative-type heat engine by using non-dominated sorting genetic algorithm

Finite-time thermodynamics with an ecological principle is used for a heat engine with an irreversible radiative along with losses owing to internal irreversibilities and the heat transfer through finite-temperature differences. In this study, the ecological function is optimised regarding the cycle temperature ratio and the effects of the extreme temperature ratio and the internal irreversibilities are investigated on the optimum cycle performance. Paper presented here used a non-dominated sorting genetic algorithm called NSGA-II to optimise the thermal efficiency, the dimensionless ecological function and the dimensionless power simultaneously. Rather than a sole ultimate optimum outcome resulting in conventional single-objective optimisation, a set of optimum solutions were obtained called the Pareto optimal frontier. Hence, in order to select a final optimal answer, a progression of decision making was utilised. Two decision-making procedures were employed in the objectives’ space to obtain the optimum answers from the Pareto optimum outcomes.     

论专业更名

根据历届新生入学的专业教育教学研究实践,以详实的专业报考志愿数据,阐述了原供热、通风与空气调节工程,城市燃气工程,供热空调与燃气工程3个专业重组更名为"建筑环境与设备工程"专业后被社会了解、认知情况以及所产生的弊端。通过分析,提出将"建筑环境与设备工程"更名为"建筑环境与能源应用"专业,该更名不仅具有必要性、贴切性、前瞻性、高屋建瓴的大器性,而且还能从生源抓起应对能源和环境的挑战,能更好地培养注册设备工程师。     

Multi-parameter building thermal analysis using the lattice method for global optimisation

The energy performance in buildings is a complex function of the building form and structure, heating system, occupancy pattern, operating schedules, and external climatic conditions. Computer simulations can help understand the dynamic interactions of these parameters. However, to carry out a multi-parameter analysis for the optimisation of the building energy performance, it is necessary to reduce the large number of tests resulting from all possible parameter combinations. In this paper, the lattice method for global optimisation (LMGO) for reducing the number of tests was used. A multi-parameter study was performed to investigate the heating energy use in office buildings using the thermal simulation code APACHE (IES-FACET). From the results of the sensitivity analysis it was possible to estimate the relative importance of various energy saving features.     

换个角度看价格战

最近一段时间，价格战重新成为工程机械行业的热门话题，与以往一样，价格战仍然是大多数人的攻击对象，扮演着负面角色。很重要的一个原因是，价格战导致行业利润不断萎缩，其压力已经开始让很多企业感到有点儿喘不过气来。但是，呼吁的结果同样也没有逃脱过去的命运，至少到目前为止还没有看到价格战停止的迹象。     

Multi-objective optimisation of retaining walls using hybrid adaptive gravitational search algorithm

This paper presents an effective hybrid evolutionary approach for multi-objective optimisation of reinforced concrete (RC) retaining walls. The proposed algorithm combines an adaptive gravitational search algorithm (AGSA) with pattern search (PS) called AGSA–PS. In the resulting hybrid approach, the PS algorithm is employed as a local search algorithm around the global solution found by AGSA. The proposed algorithm was tested on a set of five well-known benchmark functions and simulation results demonstrate the superiority of the new method compared with the standard algorithm. Thereafter, the proposed AGSA–PS is applied for multi-objective optimisation of RC retaining walls. Two objective functions include total cost and embedded CO₂ emissions of retaining wall are considered. The reliability and efficiency of the AGSA–PS for multi-objective optimisation of retaining structures are investigated by considering two design examples of retaining walls. Experimental results demonstrate that the resulting algorithm has high viability, accuracy and significantly outperforms the original algorithm and some other methods in the literature.     

Project scheduling with limited resources using a genetic algorithm

This paper presents a genetic algorithm for the Resource-Constrained Project Scheduling Problem (RCPSP). In comparison with previous genetic algorithms proposed in literature for this problem, this paper proposes an alternative representation of the chromosomes using a multi-array object-oriented model in order to take advantage of programming features in most common languages for the design of decision support systems. The approach was tested on sets of standard problems taken from the literature and freely available on the Internet (PSPLIB). Computational results validate the effectiveness of the proposed algorithm and show that our procedure equals most of previous results with less computational time.     

A genetic algorithm applied to the validation of building thermal models

This paper presents the coupling of a building thermal simulation code with genetic algorithms (GAs). GAs are randomized search algorithms that are based on the mechanisms of natural selection and genetics. We show that this coupling allows the location of defective sub-models of a building thermal model, i.e. parts of model that are responsible for the disagreements between measurements and model predictions. The method first of all is checked and validated on the basis of a numerical model of a building taken as reference. It is then applied to a real building case. The results show that the method could constitute an efficient tool when checking the model validity.     

Optimum operational conditions of a rotary regenerator using genetic algorithm

The optimum operational conditions of an air-to-air rotary regenerator (also called air preheater or heat wheel) for air conditioning applications which was designed and manufactured in Energy Systems Improvement Laboratory (ESIL) has been investigated in this paper. The performance of such a rotary regenerator was modeled and the numerical values of modeling output were verified with the experimental data obtained from the equipment testing. In the next step, the optimum operational conditions of the rotary regenerator were obtained using genetic algorithm optimization technique subject to a list of constraints. The objective function in the optimization technique was the thermal effectiveness, while the design parameters (decision variables) were volumetric flow rates of cold and hot air streams, matrix rotational speed, and the exchanger frontal area (heat transfer surface area). The apparatus was tested under the optimized operating conditions and the results were compared with the results obtained numerically applying genetic algorithm optimization. The experimental value for the effectiveness showed an acceptable closeness (2.07%) with the corresponding value obtained from the system modeling and optimization. The economic analysis of energy savings by the designed and manufactured regenerator showed a pay back period about 3 years.     

Precedence relationship options for the resource levelling problem using a genetic algorithm

The resource levelling problem (RLP) arises in project scheduling where the intention is to reduce the fluctuation of the resource demand. The solutions of the RLP are achieved through the rearrangement of the project activities under a constraint of preselected precedence relationships. To relieve the constraint, a new concept of the RLP with relationship options is initiated. An activity of the project network can have one or more alternative types of relationship with other activities as appropriate. An alternative relationship provides more float time and allows new possibilities for the arrangement of efficient patterns of the project resource demand. The scheduling problem model was formulated by using mathematical equations on spreadsheet software and solved by using the genetic algorithm based optimization. The prototype was tested in two different project instances. The test results demonstrated that this new model could calculate and arrange the project schedules for all selected alternative types of relationships. The model with relationship options provided consistent results for efficient resource utilization schedules, which were better than the one without relationship options. These schedules could decrease the resource demand fluctuation and the maximum resource demand level. This new model of the RLP with relationship options not only provides the additional flexibility to level the resource demands but also determines suitable types of relationships for the project activities.     

基于遗传算法的RC结构优化设计

结合我国现行的结构设计规范,对承受多工况荷载下的钢筋混凝土框架结构离散变量的优化设计问题,提出了基于遗传算法的优化算法。通过建立配筋模板数据库,将梁柱的计算配筋面积转换成最优的且满足规范和施工要求的实际配筋形式。在遗传算法中提出了自适应设计域技术,调整了遗传算法的搜索方向,提高了算法的效率。将上述优化算法在大型有限元软件ANSYS和建筑结构设计专用软件SATWE软件上进行了程序实现。对一个30层的结构进行了优化设计,结果表明,本文提出的方法是有效的和可操作的。     

Optimization of building thermal design and control by multi-criterion genetic algorithm

The design of buildings is a multi-criterion optimization problem, there always being a trade-off to be made between capital expenditure, operating cost, and occupant thermal comfort. This paper investigates the application of a multi-objective genetic algorithm (MOGA) search method in the identification of the optimum pay-off characteristic between the energy cost of a building and the the occupant thermal discomfort. Results are presented for the pay-off characteristics between energy cost and zone thermal comfort, for three design days and three building weights. Inspection of the solutions indicates that the MOGA is able to find the optimum pay-off characteristic between the daily energy cost and zone thermal comfort. It can be concluded that multi-criterion genetic algorithm search methods offer great potential for the identification of the pay-off between the elements of building thermal design, and as such can help inform the building design process.     

Optimum design of steel truss arch bridges using a hybrid genetic algorithm

The design of steel truss arch bridges is formulated as an optimization problem. The objective function considered is the weight of the steel truss arch bridge. The objective function is minimized subjected to the design constraints of strength (stress) and serviceability (deflection). An efficient, accurate, and robust algorithm is proposed for optimal design of steel truss arch bridges. The proposed algorithm integrates the concepts of the genetic algorithm (GA) and the finite element method. A real-coded/integer-coded method is used to realistically represent the values of the design variables. Three GA operators consisting of constraint aggregate selection procedure, arithmetic crossover, and non-uniform mutation are proposed. Finite element method is used to compute values of implicit objective functions. A numerical example involving a detailed computational model of a long span steel truss arch bridge with a main span of 552 m is presented to demonstrate the applicability and merits of the proposed method.     

Reconsideration of the settlement behavior of peat from view point of hydraulic conductivity

This paper deals with the settlement of peat from the view point of hydraulic conductivity (k). The validity of using the oedometer tests, including the calculation method, for measuring k is carefully examined by numerical analysis as well as a test combined with an oedometer and a hydraulic conductivity test. It is found from these studies that the conventional oedometer test (JIS A 1217, 2009) can be evaluated to measure k for peat with the same accuracy as that for usual clays, provided that the incremental load ratio is unity. The significant difference in the characteristics of k for peat and usual clayey soils is their relation between the compression index (C_c) and the hydraulic conductivity change index (C_k). As a result, rather than remaining constant during consolidation, the coefficient of consolidation (c_v) of peat decreases considerably with increasing consolidation pressure (p), while the c_v coefficient for usual clayey soil is almost constant at the normally consolidated stage. The influence of c_v dependent on p is studied by a numerical analysis for the one dimensional consolidation problem as well as for the ground improved by vertical drain. It is found that if the incremental consolidation pressure (Δp/p) is large, careful judgment is required when adopting conventional consolidation analyses, especially in case of the vertical drain.     

从生态的角度谈传统民居的建造

凝结了千百年劳动人民智慧的民居不仅有着良好的居住布局、居住环境、独特的建筑风貌,更有其生态智慧,文章对民居特别是福建民居做了仔细研究后从生态角度来分析传统民居的建造。