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During the last decades, the increasing need to ensure building performance during architectural design has led to highly interactive relations between architecture and various other disciplines, in which concepts of sustainability and building performance are tightly integrated into the design process. However, the adoption of these concepts in architectural practices and the recent advancements in the field have remained rather slow. This research aims to understand performative design (PD) in the context of existing design practices and investigate integrated design strategies and potentials of computational design throughout the whole design process. In this research, a qualitative study was carried out, focusing on PD processes in architectural practices based on semi-structured interviews and case studies. The collected data from nine selected architectural practices is analyzed through a thematic coding approach. The findings reveal that the widespread use of computational tools and technologies and the increasing awareness of both architects and clients on building performance have changed the design priorities in architectural practices. The results also show that the competence of the design team, the tools used during design processes, the involvement of direct and indirect stakeholders, shifting client demands, and market values are effective in pursuing an integrated PD process. Consequently, this research contributes to the current understanding of performative architectural design practices by investigating the key performance concepts, supporting computational tools, and finally the current practices of PD. [ABSTRACT FROM AUTHOR]

Buildings - especially the residential buildings - are among the high priority areas in climate change due to their large share of CO2 emissions, their significant energy saving potentials and the increased comfort expectations of their occupants. A systematic and numerical investigation of how building performance will be affected by climate change in the future is crucial for both retrofitting the existing buildings and making long-term strategical decisions. This paper presents the simulation-based results of the climate change impact assessment for a typical multi-storey residential building in Istanbul. In this study, RCP4.5 and RCP8.5 scenarios - representing the low/medium and high CO2 emission scenarios - defined by the Intergovernmental Panel on Climate Change (IPCC) are used to generate two different climate data sets for 2060. Three cooling scenarios (natural ventilation, hybrid ventilation and air-conditioned) have been developed to represent different building usage profiles for the building simulations. Investigated performance metrics were selected as building heating/cooling energy consumption, CO2 emission and occupant thermal comfort. Analysis results analysis show that the predicted temperature rise will have a strong impact on the building cooling energy consumption and/or occupant thermal comfort. The results indicate the necessity of taking measures for adaptation of buildings to the changing climate and the importance of decarbonization of electricity generation to mitigate the effects of climate change. This study will also serve as a basis for future retrofit studies for the changing climate. [ABSTRACT FROM AUTHOR]

With the growing need for residential buildings as a result of population growth, the building sector is a high-priority area in climate change due to its large share of CO 2 emissions, the significant energy saving opportunities it represents, and the increasing expectations for occupant comfort. This paper presents the results of climate change impact assessment on a typical mid-rise residential building in four representative cities with different climatic characteristics in Turkey. Three different scenarios that characterize different solutions towards space cooling are developed, from naturally ventilated to fully air-conditioned. The energy requirements and corresponding CO 2 emissions due to space conditioning as well as occupant thermal comfort are the investigated building performance metrics. The preliminary results based on energy simulations indicate that pronounced overheating will be experienced in the future, which will have a strong effect on cooling energy use and/or occupant comfort. The projected energy demand also points to the future need to explore measures for climate change adaptation of buildings and the importance of the decarbonization of the electricity industry for climate change mitigation. The analysis results presented in this paper provide a basis for future studies on building retrofit for climate change. • Evaluation of climate change impact on a typical residential building in Turkey. • Cooling is to dominate thermal loads due to increased temperatures. • Warmer climates to be influenced most by negative effects. • Severe overheating for poorly ventilated, highly insulated buildings. • Adaptation measures needed for climate-proofing existing buildings. [ABSTRACT FROM AUTHOR]

Computational tools for early architectural design need to address issues related to building performance and integrally consider early design decisions regarding building form, spatial layout, orientation, and envelope articulation. This paper presents (1) a design optimization tool, Multiobjective Architectural Design Explorer (MADE) that supports performative-based building design, and (2) a design exploration strategy that effectively operationalizes MADE for Pareto-based comparative search. MADE implements genetic optimization in two steps. It first generates building layouts that satisfy formal, topological, and placement constraints with a single-objective genetic algorithm. Then, MADE determines the opening sizes of the generated layout(s) by optimizing the buildings' energy and daylighting performance with a multiobjective genetic algorithm.MADE is tested on the design of a library building and the proposed Pareto-based exploration strategy is demonstrated in the selection of an optimal design. The results point out the feasibility of the presented tool and the design strategy, and that multiple objectives can be satisfied by sequentially optimizing design objectives. [ABSTRACT FROM AUTHOR]

In this study, the effects of climate change on the energy consumption and thermal comfort of the education building were examined and the effectiveness of passive improvement scenarios based on window parameters was evaluated by machine learning and statistical analysis. The research was based on a four-stage approach based on building simulations: (i) creation and analysis of climate change scenario-modified climate datasets, (ii) climate change impact analysis on existing building, (iii) comparative analysis of improvement scenarios, and (iv) analysis of predictive models based on machine learning. An existing secondary school building in Ankara was chosen as a case study for the evaluation of the selected performance indicators. 2025 scenarios were parametrically modeled with varying window parameters. After analyzing the complete dataset generated from performance simulations with descriptive statistics, Random Forest (RF) prediction models are trained with a subset of the data for each performance indicator. For each performance indicator, the feature importance of fine-tuned RF models was calculated with 10-fold cross-validation method, and it was seen that the window SHGC value was the most critical parameter among the tested variables. Performance predictions with RF models deviate 2% on average from their actual values and imply high predictive capacity. Moreover, with the retrofit scenarios, total energy consumption showed a reduction of up to 50%, whereas a significant improvement in indoor thermal comfort was observed. The results emphasize that the right selection of window parameters in existing educational buildings has a great effect on building energy performance. The results show that machine learning can be used effectively in the adaptation processes of buildings to climate change. The method used can be extended to cover different building parameters and technologies. [ABSTRACT FROM AUTHOR]

This work introduces Evolutionary Architectural Space layout Explorer (EASE), a design tool that facilitates the optimization of 3D space layouts. EASE addresses architectural design exploration and the need to attend to many alternatives simultaneously in layout design. For this, we use evolutionary optimization to find a balance between divergent exploration and convergent exploitation. EASE comprises a novel sub-heuristic that constructs valid spatial layouts, a mathematical framework to quantify the satisfaction of constraints, and evolutionary operators to improve alternative layouts' fitness. We test EASE on the design of a library building. We evaluate EASE's performance for different building forms and different evolutionary algorithm parameters. The results suggest that EASE can generate valid layouts, quantify the constraints' degree of satisfaction and find a number of optimal layout solutions. The layouts that EASE generates are intended not as end results but design artifacts that provide insight into the solution space for further exploration. [ABSTRACT FROM AUTHOR]

3D-printed concrete (3DPC) is a layered anisotropic material whose properties are significantly impacted by the production parameters. To design and analyze large-scale printed structures, it is vital to understand the spatial variation of material properties throughout full-scale printed components. Presented study addresses this need by presenting a comprehensive experimental investigation that explores the variation of physical, mechanical, and thermophysical properties across a full-scale 3DPC wall with an approximate height of 2.36 m. The cores extracted from the wall were categorized in terms of the extraction location along the wall height (upper (U) and lower (L)) and the alignment of the specimen's longitudinal axis with respect to the printing space (printing (P), translation (T), and deposition (D) direction). Additionally, direct shear and four-point bending tests were carried out on printed beams. Control samples, mold-cast from the same concrete mix, were used for comparison. Obtained results indicated a pronounced anisotropy and property variation along the 3DPC wall height. These were linked not only to the frequency and orientation but also to the porosity of the interfaces which was significantly influenced by the self-weight compaction of the layers and the progressively increasing concrete viscosity caused by pump system heating over time. CT investigations, rate of water absorption, and water penetration tests revealed sparse and less connected interstrip and interlayer porosity in the L samples. Depending on the sample orientation and loading direction, compressive strength, elastic modulus and splitting tensile strength increased by up to 108 %, 53 %, and 100 %, respectively, in the L samples when compared to the U samples. The same trend was observed in the Poisson's ratio, albeit to a lesser extent. The triaxial thermal conductivities of the L samples calculated from the transient plane source measurements were up to 26 % higher than those for the U samples. Given the limited number of studies on real-scale applications, the presented research can serve as a benchmark, offering valuable insights to explore and validate the 3DPC behavior on a large scale. • Strong spatial variability of 3DPC properties across a full-scale printed wall. • Porosity depends on printing-space directions and wall height. • Pore size, content and connectivity are critical determinants of performance. • Stronger and stiffer behavior at lower levels due to better compaction. • Enhanced understanding of 3DPC behavior for full-scale construction scenarios. [ABSTRACT FROM AUTHOR]