A preference-based multi-objective building performance optimization method for early design stage

A large number of cases show that the multi-objective optimization method can significantly improve building performance.The method for multi-objective building performance optimization(BPO)design has achieved rapid development in recent years.However,the BPO method still needs to be improved.Specifically,weak interaction between the optimization process and the decision-making process results in low optimization efficiency,which limits the widespread application of the optimization method in early design stage.In this paper,a new interactive BPO mode is explored to strengthen the interaction between the optimization process and decisionmaking process,and a preference-based multi-objective BPO method is proposed to account for designers'decision preferences during the optimization process,making the objective more controllable,improving the optimization efficiency and ensuring the diversity of solutions.Firstly,this paper illustrates the proposed method in detail,defines the concept of performance preference,expounds the flow of the preference-based multi-objective optimization algorithm,and proposes three indicators to evaluate the algorithm,which includes convergence speed,preference satisfaction rate,and diversity measurement.Secondly,through testing and comparison,it is found that the proposed preference-based algorithm has advantages over the non-preference optimization algorithm(represented by the NSGA-II algorithm).The proposed method leads to faster convergence and higher preference satisfaction,so it is more suitable for the BPO process in the early design stage.Specially,the proposed method can achieve 100%preference satisfaction rate with only 2400 simulations,while the non-preference method can only achieve 20%preference satisfaction rate after 5800 simulations.In this paper,a preference-based multi-objective BPO method is proposed to make the optimization process closely interact with the decision-making process and make the design preferences be accounted during the BPO process,thereby improving the optimization efficiency.In addition,this study first proposes two indicators to measure the quality of optimization results:preference satisfaction rate and diversity measurement.This study aims to guide the development of BPO methods towards providing high satisfaction rate and high quality optimization results.

Data Mining and Machine Learning Methods Applied to A Numerical Clinching Model

Numerical mechanical models used for design of structures and processes are very complex and high-dimensionally parametrised.The understanding of the model characteristics is of interest for engineering tasks and subsequently for an efficient design.Multiple analysis methods are known and available to gain insight into existing models.In this contribution,selected methods from various fields are applied to a real world mechanical engineering example of a currently developed clinching process.The selection of introduced methods comprises techniques of machine learning and data mining,in which the utilization is aiming at a decreased numerical effort.The methods of choice are basically discussed and references are given as well as challenges in the context of meta-modelling and sensitivities are shown.An incremental knowledge gain is provided by a step-bystep application of the numerical methods,whereas resulting consequences for further applications are highlighted.Furthermore,a visualisation method aiming at an easy design guideline is proposed.These visual decision maps incorporate the uncertainty coming from the reduction of dimensionality and can be applied in early stage of design.

Exploring the synergy of building massing and façade design through evolutionary optimization

In performance-based architectural design optimization, the design of building massings and façades is commonly separated, which weakens the effectiveness in performance improvement. In response, this study proposes a hybrid massing-façade integrated design generation and optimization workflow to integrate the two elements in an evolutionary design process. Compared with the existing approaches, the proposed workflow emphasizes the diversity of building design generation, with which various combinations of building massing forms and façade patterns can be systematically explored. Two case studies and a corresponding comparison study are presented to demonstrate the efficacy of the proposed workflow. Results show that the optimization can produce designs coupling the potential of building massings and façades in performance improvement. In addition, the optimization can provide information that supports early-stage architectural design exploration. Such information also enables the architect to understand the performance implications associated with the synergy of building massing and façade design.

Parametric wind design

Although gradual, the changes in the weather patterns are also noticeable and impactful to architectural design. If the local microclimate is taken into account early in the conceptual stage of design, the longevity of the ultimate structure can be greatly enhanced, despite challenging environmental factors. Parametric designing enables to discover the optimal architectural shape based on specific weather data. The paper intends to investigate how this design approach, coupled with Computational Fluid Dynamics simulations, can be used to create a wind-induced architecture. Both the benefits and the limitations of this approach are explored in detail. The interaction between an architectural shape and wind flow is tested in a study called ‘FlowBrane’. The process of （1） designing a parametrically changeable geometry, （2） testing its behavior in the wind, and （3） evaluating the results allows looping back to the initial geometric design, continuing to improve the design and ultimately the performance of the architecture in the specific wind conditions of the chosen site. However, the need to test multiple geometries separately and to adjust the wind simulation for each test （and for every wind direction） remains a disadvantage that should be addressed in further research.

A graphical multi-objective performance evaluation method with architect-friendly mode

In response to the inadaptation and difficulties for architects in the use of engineering simulation tools and optimization methods,a method is proposed for graphical performance evaluation achieved with a developed plugin for Grasshopper as an architect-friendly tool to support design exploration in early stage.The proposed method follows forward work-flow for interactive feedback of performance,focusing on thermal and visual comfort upon a variety of design options.A case study of shading design is demonstrated.The demonstration illustrated an intuitive and graphical process for qualitative performance evaluation,which is assisted by an overall ratio ranking the integrated performance of design options for a quantitative comparison.Compared with engineering optimization methods that focus on optimal performance-based solutions,the proposed method presented graphical feedbacks on design performance that are interactive with the designer for performance-informed decision making.In this way,the proposed method stimulates the effective and positive application of engineering tools and judgment at the early stage of iterative design.