For sustainable building design, performance-based optimization incorporating parametric modelling and evolutionary optimization can allow architects to leverage building massing design to improve energy performance. However, two key challenges make such applications of performance-based optimization difficult in practice. First, due to the parametric modelling approaches, the topological variability in the building massing variants is often very limited. This, in turn, limits the scope for the optimization process to discover high-performing solutions. Second, for architects, the process of creating parametric models capable of generating the necessary topological variability is complex and time-consuming, thereby significantly disrupting the design processes. To address these two challenges, this paper presents a parametric massing algorithm based on the subtractive form generation principle. The algorithm can generate diverse building massings with significant topological variability by removing different parts from a predefined volume. Additionally, the algorithm can be applied to different building massing design scenarios without additional parametric modelling being required. Hence, using the algorithm can help architects achieve an explorative performance-based optimization for building massing design while streamlining the overall design process. Two case studies of daylighting performance optimizations are presented, which demonstrate that the algorithm can enhance the exploration of the potential in building massing design for energy performance improvements.

Significant research has been undertaken focusing on the application of evolutionary algorithms for design exploration at conceptual design stages. However, standard evolutionary algorithms are typically not well-suited to supporting such optimization-based design exploration due to the lack of design diversity in the optimization result and the poor search efficiency in discovering high-performing design solutions. In order to address the two weaknesses, this paper proposes a hybrid evolutionary algorithm, called steady-stage island evolutionary algorithm (SSIEA). The implementation of SSIEA integrates an island model approach and a steady-state replacement strategy with an evolutionary algorithm. The combination aims to produce optimization results with rich design diversity while achieving significant fitness progress in a reasonable amount of time. Moreover, the use of the island model approach allows for an implicit clustering of the design population during the optimization process, which helps architects explore different alternative design directions. The performance of SSIEA is compared against other optimization algorithms using two case studies. The result shows that, in contrast to the other algorithms, SSIEA is capable of achieving a good compromise between design diversity and search efficiency. The case studies also demonstrate how SSIEA can support conceptual design exploration. For architects, the optimization results with diverse and high-performing solutions stimulate richer reflection and ideation, rendering SSIEA a helpful tool for conceptual design exploration.

The incorporation of physical environmental performance as a primary consideration in building design can facilitate the harmonization of the built environment with the surrounding site and climate, enhance the building’s environmental adaptability and environmental friendliness, and contribute to the achievement of energy-saving and emission-reduction objectives through the integration of natural lighting and ventilation. General computational fluid dynamics (CFD) can help architects make accurate predictions and effectively control the building’s wind environment. However, CFD integration into the design workflow in the preliminary stages is frequently challenging due to program uncertainty, intricate parameter settings, and substantial computational expenses. This study offers a methodology and framework based on machine learning to overcome the complexity and computational cost barriers in simulating outdoor wind environments of buildings. In this framework, the machine learning model is trained using an automated CFD simulation system based on Butterfly and implemented within the Rhino and Grasshopper environment. This framework provides real-time simulation feedback within the design software and exhibits promising accuracy, with a Structural Similarity Index Measure (SSIM) ranging from 90–97% on a training dataset of 1200 unique urban geometries in Xinjiekou Area of Nanjing, China. Furthermore, we programmatically integrate various parts of the simulation and computation process to automate multiscenario CFD simulations and computations. This automation saves a significant amount of time in producing machine-learning training sets. Finally, we demonstrate the effectiveness and accuracy of the proposed working framework in the design process through a case study. Although our approach cannot replace CFD simulation computation in the later design stages, it can support architects in making design decisions in the preliminary stages with minimal effort and immediate performance feedback.

The good indoor light environment and comfort of the teaching space are very important for students’ physical and mental health. Meanwhile, China advocates energy conservation and emission reduction policies. However, in order to obtain lower building energy consumption, higher thermal comfort, and daylighting, architects use performance simulation software to repeatedly simulate and refine, which is time-consuming and difficult to obtain the best results from three performances. Given this problem, we constructed the design framework in the early stage of the architectural design of the teaching building. In the first stage of the framework, architects optimized the performance objectives of lighting, thermal comfort, and energy consumption, and performed a cluster analysis on the optimized non-dominated solution to provide a reference for the architect. In the second stage of the framework, architects used the data generated in the optimization process to train the BP neural network and use the trained BP neural network to predict the performance of the building. In this paper, we selected Nanjing Donglu Middle School as a case study. The optimization of the building performance was assessed by a genetic algorithm, generating 3000 sets of sample data during the optimization iteration. Then, we analyzed the non-dominated solution of the sample data through the method of cluster analysis and trained the BP neural network with the sample data as a data set. The prediction model with R-values of 0.998 in the training set and test set was obtained by repeatedly debugging the number of neurons in the BP neural network. Finally, five groups of design parameters were randomly selected and brought into the trained BP neural network, and the predictive value was close to the simulated value. The construction of the framework provides design ideas for architects in the early teaching of building design and helps designers to make better decisions.

Based on five periods of Landsat remote sensing data from 1980 to 2020, this study constructs a landscape ecological risk-ecosystem service value evaluation model and integrates it with a geodetector model to analyse the environmental benefits of the development of the “production–living–ecological space” pattern and its driving factors in the Xuzhou planning area. The results of the study are as follows: (1) Over the past 40 years, the expansion of living spaces has significantly encroached upon adjacent agricultural production areas and ecological spaces, such as forests and grasslands. Specifically, the areas of agricultural land, forests, and grassland have been diminished by 277.39 km², 23.8 km² and 12.93 km², respectively; in contrast, urban and rural living spaces have increased by 238.62 km² and 58.92 km², alongside a rise in industrial production areas, water bodies, and other ecological spaces. (2) Throughout the 40-year period, both the landscape ecological risk (ERI) and ecosystem service value (ESV) in the study area have shown a decreasing trend. The proportion of high- and medium-high-risk areas of the ERI have decreased by 5.19% and 7.50%, respectively, while low, lower, and medium ecological risk areas have increased by 6.40%, 3.22% and 3.07%, respectively. In addition, low-ESV areas have increased by 14.22%, while the proportion of high- and medium-high-ESV areas have decreased by 1.16%. (3) There is a significant positive spatial correlation between the ERI and ESV. Regions with dense ecological spaces comprising forests, water bodies, and grasslands, particularly in the northeastern part of the Jiawang District and the southeastern part of the Tongshan District, demonstrate superior regional ecosystem service quality. The ERI and ESV are dominated by “high–high” and “low–high” aggregation. Conversely, in the southwestern part of the study area, the expansion of living space has led to the transformation of some agricultural land, forest land, and grassland into less risky construction land, resulting in a decline in the quality of regional ecosystem services. The local spatial correlation between the ERI and ESV changed from “high–high”, “low–low”, “low–high” agglomeration to “low–low” agglomeration. (4) Key factors influencing the spatial differentiation of the “production–living–ecological space” include the GDP, population density, soil type, and the distance to towns and roads. Among these, the interaction between population density and soil type has the most significant effect on the changes in the pattern of the “production–living–ecological space”.